The DataWeave Blog

Category: AI

  • Bridging the Gap: How Digital Shelf Analytics Empowers Marketing Mix Modelling for Smarter Brand Decisions

    Bridging the Gap: How Digital Shelf Analytics Empowers Marketing Mix Modelling for Smarter Brand Decisions

    Marketing Mix Modeling (MMM) has been a cornerstone of marketing analytics for decades: first as a service offered by large consultancies like Nielsen and IRI, and later as software solutions from NielsenIQ and Ekimetrics. By 2024, some 64% of senior marketing leaders had already adopted and used MMM solutions.

    However, despite this widespread adoption, MMM faces significant limitations in our fast-moving digital marketplace. According to Gartner, opaque pricing models and siloed data integration remain substantial barriers to actionable insights from these tools. Most critically, traditional MMM often misses vital variables influencing consumer behavior, such as:

    • Competitor price drops and promotions
    • Product availability issues and stockouts
    • Negative review trends and sentiment shifts
    • Search ranking fluctuations

    These blind spots must be addressed to unlock the full value of MMM investments and make truly informed marketing decisions.

    The Critical Data Gap In Traditional MMM

    Traditional MMM solutions expose brands to considerable risk, especially in the CPG and retail space. The fundamental challenge lies in MMM’s reliance on lagging indicators for essential metrics like historical sales and ad spend. Data inputs may be months or quarters old before they’re used for scenario analysis.

    That’s like making million-dollar marketing decisions while only looking in the rearview mirror when you need to watch the road ahead simultaneously.

    MMM tools also typically overlook external market factors that can dramatically impact performance. In today’s retail landscape, where market conditions change rapidly, being blind to real-time competitive dynamics creates significant vulnerability. Key external factors that traditional MMMs fail to capture include:

    • Competitor moves: Price changes, promotions, content updates
    • Consumer sentiment: Review trends, ratings, social engagement
    • Market dynamics: Stockouts, search ranking shifts, category growth

    How Digital Shelf Analytics Completes The Picture

    This is where Digital Shelf Analytics (DSA) plays a crucial complementary role. Brands and retailers leveraging DSA gain insights into real-time market dynamics that MMM alone cannot provide. However, brands using DSA in isolation often struggle to quantify how digital shelf improvements directly impact revenue. Answering questions like “Did better product content drive sales, or was it the influencer campaign?” remains challenging.

    Bridging these disconnected platforms requires intentional integration and a DSA platform that can feed intensively cleaned and organized data into existing MMM platforms. With the right data inputs, companies establish a powerful feedback loop for agile, data-driven decisions.

    A comprehensive DSA solution like DataWeave provides granular, actionable data on critical external variables such as:

    • Daily or weekly competitor pricing movements and promotional activity
    • Product content standardization and optimization across retailers
    • Review sentiment trends and potential reputation issues
    • Share of search/shelf performance relative to competitors

    When merged with established MMM capabilities, DSA creates a complete picture that fills the blind spots holding marketing teams back from maximizing ROI.

    The DSA + MMM Advantage in Retail Media

    The popularity of retail media networks has further amplified the need for integrated DSA and MMM approaches. These advertising platforms, operated by retailers, allow brands to display targeted ads to shoppers across digital properties based on first-party customer data and purchase insights.

    The retail media revolution has transformed e-commerce pages into sophisticated search engines for product discovery. This evolution has been so impactful that retail media ad revenue surged 16.3% in 2023, reaching $43.7B in the U.S., with continued growth projected.

    Major platforms like Walmart have expanded their retail media networks to capitalize on closed-loop attribution. Since retailers own the entire customer journey, they can track everything from ad impression to purchase on their e-commerce sites. This creates a significant advantage through accurate ROI measurement, unlike traditional advertising where attribution remains challenging.

    How DSA Enhances Retail Media Optimization

    With retail media emerging as a top-performing sales channel, brands need sophisticated optimization strategies. Every brand wants to maximize visibility and performance across individual eCommerce sites, just as they optimize for Google or emerging AI platforms.

    Integrating digital shelf analytics into marketing mix models enables brands to:

    • Allocate ad spend more intelligently using real-time competitive insights
    • Identify timely campaign activation opportunities in response to market changes
    • Monitor organic ranking trends to strategically time paid promotional activities
    • Measure true campaign impact on digital shelf performance metrics

    For example, when a competitor launches an aggressive price drop in your category, DSA provides visibility into this change. This intelligence can trigger recommended campaign adjustments, such as increased sponsored ad bidding in affected categories. Traditional MMM alone cannot deliver this level of responsive optimization.

    How to Integrate DSA into MMM: A 3-Step Framework

    Digital Shelf Analytics for Marketing Mix Modeling - 3 Step Framework

    Here’s how to integrate your Digital Shelf Analytics into your Marketing Mix Models to start making better data-driven decisions for your brand.

    Step 1: Map DSA Variables to MMM Inputs

    Begin by mapping specific DSA variables to your static MMM inputs. Ensure that competitors are properly configured for monitoring in your DSA platform and that metrics like price changes and search ranking positions are linked with your MMM’s models.

    This integration is crucial because traditional MMM models rely exclusively on historical data for forecasting. Adding real-time inputs delivers several benefits:

    • More accurate elasticity curves reflecting current market conditions
    • Better understanding of root causes behind demand shifts
    • Prevention of misattributing sales changes to your marketing activities when external factors may be responsible

    At DataWeave, our comprehensive coverage spans 500+ billion data points, 400,000 brands, and 1,500+ websites, ensuring brands never miss a competitor move and maintain complete visibility across the connected e-commerce landscape.

    Step 2: Feed High-Quality DSA Data into MMM Platforms

    Next, integrate critical digital shelf metrics into your MMM framework:

    • Review and sentiment scores and trends
    • Content quality measurements
    • Competitive positioning data
    • Price gap analytics
    • Search ranking performance

    DataWeave employs a rigorous data accuracy validation process to ensure teams work with the cleanest, most reliable data possible. Our sophisticated processing pipeline removes anomalies and standardizes information across retailers, providing the consistent, high-integrity data foundation that robust marketing mix modeling demands.

    Step 3: Validate and Iterate

    A powerful DSA solution helps measure whether your marketing efforts achieved their intended impact on the digital shelf. Use your DSA platform to assess campaigns’ actual effect on key performance indicators:

    • Do promo-driven sales lifts correlate with improved search rankings?
    • How do content improvements impact conversion rates?
    • What is the relationship between paid media and organic visibility?

    DataWeave enables users to correlate metrics across the entire consumer journey, from awareness through post-purchase. Rather than focusing solely on short-term spikes, brands can measure lasting impacts on digital shelf health. This end-to-end visibility empowers teams to make increasingly informed decisions with each campaign cycle.

    Executive Decision Support in Uncertain Times

    It is no surprise to anyone that we are living through volatile times. Executives may be uncomfortable if they cannot provide their teams with strategic direction based on data or the tools they need to accelerate their workdays.

    By integrating DSA with MMM, companies gain early warning signals about market shifts, enabling smarter resource allocation during budget constraints. This integration helps organizations move from tactical execution to strategic direction by:

    • Providing cross-channel impact analysis to understand the full marketing ecosystem
    • Equipping category managers with tactical optimization tools that support broader strategic objectives
    • Identifying competitive threats before they impact sales
    • Forecasting potential ROI impacts across various spending scenarios

    These capabilities help prevent wasted ad spend, missed opportunities, and lost sales.

    Future-Proofing with DSA-Driven MMM

    Several emerging trends highlight the growing importance of DSA-enhanced marketing mix modeling:

    • Trend 1: Navigating Economic Volatility – Brands can use DSA to track how competitors adjust pricing in response to cost shocks like tariffs and inflation. This real-time intelligence directly improves MMM’s inflation modeling accuracy.
    • Trend 2: AI-Powered Predictive Insights – Combining DSA trend detection (such as viral product reviews or sudden inventory fluctuations) with MMM helps forecast demand spikes from otherwise unforeseen events.
    • Trend 3: Automated Optimization – Smart campaign activations and adjustments based on real-time DSA triggers drive efficiency. DataWeave’s vision includes an automated retail media intelligence layer that optimizes spend across channels based on integrated insights.

    DataWeave’s Unique Advantage

    At DataWeave, we’ve seen our digital shelf analytics customers significantly improve their organic search rankings because of better-sponsored ad campaigns. What makes our approach to DSA-MMM integration uniquely powerful? Our platform is specifically designed to address the challenges of modern marketing mix modeling:

    • Superior data refresh rates ensure timely insights when they matter most
    • Unmatched marketplace coverage across more than 1,500 eCommerce sites globally
    • Advanced data normalization that standardizes metrics across disparate categories and retailers
    • API-first architecture enabling flexible data access and utilization

    Conclusion – From Hindsight to Foresight

    In the past, companies relied primarily on historical data for their marketing mix models. Today’s market leaders are incorporating digital shelf analytics to unlock superior insights, improve decision accuracy, and drive measurable ROI.

    DataWeave serves as the essential bridge between MMM systems and real-time, comprehensive market intelligence. When DSA and MMM work together, brands gain a complete picture: MMM shows precisely what happened, while DSA explains why it happened—and together, they reveal what’s coming next.

    Ready to transform your marketing mix modeling from hindsight to foresight? Contact us today to discover how our Digital Shelf Analytics can enhance your existing MMM investments and drive measurable business results.

  • Standard Reporting vs. Competitive Intelligence: What Retail Leaders Need to Know

    Standard Reporting vs. Competitive Intelligence: What Retail Leaders Need to Know

    Back in the day, pricing strategies were a lot easier. These days, not only do teams need to have robust standard price reporting workflows, but they also need to have the know-how and tools to gain and act on competitive intelligence. Retail leaders should prioritize automation and strategic thinking and ensure their teams have the tools, processes, and methodologies required to monitor the competition at scale and over the long term.

    Retail leaders who recognize the distinction between standard reporting and competitive intelligence are more likely to gain team buy-in, especially when developing pricing strategies that drive results. You can’t be everywhere at once, but you can optimize pricing strategies to stay ahead of the competition.

    This article has everything you need to know about the differences between standard reporting and competitive intelligence and how to use both to make your teams more effective than ever!

    Understanding the Distinction

    Standard price reporting is much like checking the weather to see if it’s stormy before grabbing a raincoat or sunhat. You need to do it to make essential, everyday choices, but it will not help you predict when the next storm is coming. Standard price reporting deals more with the short-term and immediate actions needed as opposed to long-term strategy.

    Don’t get us wrong, standard price reporting is still an essential responsibility of a pricing team’s function—but there’s more to it. It is also lower-tech than a competitive intelligence strategy and can rely on route heuristics.

    Think of it as data-in, data-out. It deals with pricing operations like:

    • Weekly price movements: Seeing which competitors, product categories, and individual items had pricing shifts in the short-term
    • Basic price indices: Outlining benchmarks to watch how your own, and your competitors’, products are trending in the market
    • Price competitiveness metrics: Setting thresholds that show whether your products are priced below, above, or equal to your competition for general trend reporting

    Standard price reporting is fundamental for operational teams that manage price adjustments in the short term. It can also help teams remain agile and reactive to market condition changes.

    It’s likely that your team already has standard reporting strategies or tools to help them with tactical execution. But are they harnessing competitive intelligence correctly with your help?

    Characteristics of Competitive Intelligence

    While standard price reporting is like checking the weather, competitive intelligence is like being a meteorologist who measures atmospheric changes, predicts storms, and scientifically analyzes weather patterns to keep everyone informed and in the know.

    Competitive intelligence goes well beyond simply tracking price movements and benchmarking them against a single set of standards. Competitive intelligence helps steer teams in a strategic direction based on insights from the market. It can drive long-term business success and is one of your best tools to ‘steer the ship’ as a retail leader.

    Here are some of the essential elements of competitive intelligence:

    • Strategic insights: Including but not limited to understanding your competitors’ pricing strategy, promotions, and product positioning
    • Market-wide patterns: Identifying trends based on geography, product category, or individual SKU across retailers to inform broader strategies
    • Long-term trends: Taking historical market and competitor data and combining it with real-time retail data to predict future price movements as shifts in consumer behavior to inform pricing strategies

    The pricing team serves as a critical strategic partner to senior leadership, delivering the cross-functional insights and market analysis needed to inform C-suite decision-making. By equipping executives with a holistic view of the competitive landscape, pricing gaps, and emerging trends, the team empowers leadership to align pricing strategies with broader business objectives.

    This partnership enables senior leaders to guide day-to-day pricing operations with confidence—ensuring tactical execution aligns with corporate goals, monitoring strategy effectiveness, and maintaining competitive agility. Through ongoing market intelligence and scenario modeling, the pricing function helps leadership proactively position the brand, capitalize on untapped opportunities, and future-proof revenue streams.

    Different Audiences, Different Needs

    As mentioned, there is a place for both standard price reporting and competitive intelligence. They have different roles to play, and different teams find them valuable. Since standard reporting mainly focuses on day-to-day shifts and being able to react to real-time changes, operational teams find it most useful.

    On the other hand, competitive intelligence is a tool that leadership can use to shape overarching pricing strategies. The insights from competitive intelligence drive operational activities over months and quarters, whereas standard reporting drives actions daily.

    To succeed in pricing, you need to rely on a combination of tactical standard reporting and competitive intelligence for long-term planning. With both, you can successfully navigate the ever-fluctuating retail market.

    Price Reporting for Operational Teams

    Your operational team is responsible for making pricing adjustments that directly impact sales volume. Automated data aggregation and AI-powered analytics can make this process faster and more accurate by eliminating the need for manual intervention.

    Instead of spending hours identifying changes, standard reporting tools surface the most critical areas that need attention and recommend adjustments. This helps operational teams react fast to shifting market conditions.

    Key functions of standard price reporting include:

    • Daily/weekly pricing decisions: Frequent price adjustments based on market trends will help your company remain competitive across entire product categories. With automated, real-time dashboards, your pricing team can monitor broad category-level pricing shifts and make necessary adjustments accordingly.
    • Individual SKU management: Not all pricing changes happen at the category level. Standard reporting also allows teams to view price and promotion changes on individual SKUs down to the zip code. It’s important to have targeted, granular insights when a change occurs even on a single SKU, especially because these individual changes are easy to miss. Advanced product matching algorithms can tie together exact products across retailers to monitor items conjointly. By incorporating similar product matching technologies beyond standard reporting, your teams can monitor individual price changes on comparable products.
    • Immediate action items: The best standard reporting tools alert pricing teams when there has been a change in competitor pricing and give them recommendations for what to change. If a competitor launches a flash sale or an aggressive discount program, your team should know as fast as possible which product to adjust. Without this functionality, teams can miss important changes or experience a delay in action that results in lost sales or customer perception.

    Competitive Intelligence for Leadership

    For Senior Retail Executives, Category Directors, and Pricing Strategy Leaders, pricing cannot only be about reacting to individual competitor price changes. Instead, you must proactively think about your market positioning and brand perception. Doing this without a complete competitive intelligence strategy can feel like throwing darts while blindfolded. Sometimes, you’ll hit the target, but mostly, you’ll miss or only come close. Competitive intelligence tools can help you hit that target every time. They leverage big data, artificial intelligence (AI), and predictive modeling to help you derive holistic insights to understand your current positioning relative to the current and future pricing landscape.

    Core strategic functions of competitive intelligence include:

    • Strategic planning: Competitive intelligence tools can help you forecast competitor behavior, economic shifts, and category-specific patterns you’d otherwise overlook (ex, price drops before new releases, subscription or bundling trends, or seasonable price cycles). Instead of reacting to a change, your team can already have made changes or at least know what playbook to implement.
    • Market positioning: Geographic pricing intelligence built into competitive intelligence tools can help you understand variations across locations and optimize multiple channels simultaneously. This can be the foundation of regional pricing strategies that factor in local economies and consumer perception.
    • Long-term decision-making: You can use competitive intelligence technology to align your pricing strategy with upcoming seasonal trends isolated using historical data, predicted economic shifts, and changes in customer purchasing behavior. This aggregate view of the pricing landscape will help you step out of the weeds and make better company decisions.

    From Data to Strategy – Transforming Basic Price Data

    Shifting your focus from isolated, reactive data to broader market trends is the key to going from basic price reporting to real competitive intelligence. Never forget the importance of real-time data, but know it’s your responsibility as a leader to bring a broader viewpoint to operations.

    Transforming from basic price data to competitive intelligence involves:

    1. Harnessing the data
      • Pattern recognition: Your solution should help you identify repeat pricing behaviors and competitor strategies
    2. Figuring out what to do with the data
      • Strategic implications: It should help you understand how your pricing changes will affect customer perception of your brand
    3. Doing something with the insights from your data
      • Action planning: The solution should help you create proactive strategies that position you as a market leader, leaving your competition to try to keep up with you instead of vice versa

    Leveraging Technology for Competitive Intelligence

    Technology is at the heart of leveling up your standard price reporting game. If you want industry-leading competitive intelligence, you can leverage DataWeave’s comprehensive pricing intelligence solution with built-in competitive intelligence capabilities and features for your operational teams.

    You can also uncover gaps and stay competitive in the dynamic world of eCommerce. It provides brands with the competitive intelligence they need to promptly adapt to market demand and competitors’ pricing. Stay ahead of market shifts by configuring your own alerts for price fluctuations on important SKUs, categories, or brands, all time-stamped and down to the zip.

    And since our platform relies on human-backed AI technology, you can have complete confidence in your data’s accuracy at any scale. If you want to bring a new strategic mindset to your pricing team, consider adding competitive intelligence to your tech stack. If you want to learn more, connect with our team at DataWeave today.

  • Preparing for Tariff Impact: A Retailer’s Guide to Price Intelligence

    Preparing for Tariff Impact: A Retailer’s Guide to Price Intelligence

    The power to impose tariffs on foreign countries is one of the most impactful measures a government has at their disposal. The government can use this power for various reasons: to punish rivals, equalize trade, give domestic products a comparative advantage, or collect more funds for the federal government.

    Whatever the reason, tariffs have real-world impacts on brands and retailers selling in a global economy. They effectively make products more expensive for some and comparatively cheaper for others. Since tariffs can be added or removed at the drop of a hat, retail executives, category managers, and pricing teams trying to keep up have their work cut out for them.

    You’ve come to the right place if you’re wondering how to prepare for and respond to potential tariffs. The answer lies in technology that will make you flexible when you need to react to policy changes. Establishing workflows and processes embedded with pricing intelligence can help you stay competitive even when global politics intercepts your business.

    Understanding Tariff Impact

    Before diving into tariffs’ implications on pricing strategies, we need to understand how tariffs work and the current economic environment. Tariffs are a government’s tax on products a foreign country sells to domestic buyers. You might remember President Trump’s expanded tariff policy in September 2018. It placed a 10% tax on $200 billion worth of Chinese imports for three months before raising to a rate of 25% in January 2019. At that time, an American buyer would pay the original price of the goods plus the tax to the American government. Many additional tariffs and counter-tariffs by other countries were enacted during Trump’s first term in office, including the European Union, Canada, Mexico, Brazil, and Argentina, resulting in a trade war.

    Announcements of when, where, and on what new tariffs will be imposed are unpredictable. The only predictable thing is that this type of market volatility is here to stay. Pricing teams should adjust their mindsets to assume that volatility may always be on the horizon. This is because tariffs have many cost implications. Besides the flat rate imposed by the government on a certain product, tariffs have historically raised the price of all goods.

    In economic terms, tariffs create a multiplier effect. Consider a tariff placed on gasoline imported from Canada. This measure may encourage American drilling but will have immediate ripple effects throughout the economy. Everything that relies on ground transportation will increase in price, at least in the short term.

    This means that a fashion brand that sources and manufactures its entire line domestically will incur more costs since transportation will be more expensive. If fashion companies act like most companies, they will pass that added tax burden on to the consumer through higher prices. The company will make this decision based on how sensitive its consumers are to price increases, i.e., the elasticity of demand. These interwoven relationships extend across industries and products, affecting most retailers somehow.

    Of course, category exposure varies by industry and sector. Tariffs are known to impact specific industries more than others. For example, steel, electronics, and agriculture products are at risk of price fluctuations based on their reliance on imported components. These have high category exposure. Some industries reliant on domestic production with stable input costs are less prone to category exposure. These include domestic power grids, natural gas, real estate, and handmade goods. No matter which industry you’re in, however, expect some spill over.

    Preparation Strategies

    Strategies to battle disruption in retail

    Forward-thinking leaders can help position their teams for success in the face of pricing volatility brought on by tariffs. The key is to enable teams to sense disruptions quickly and provide a way to take corrective action that doesn’t diminish sales. Here are three strategies you can implement ahead of time that will help keep you competitive during tariff disruption.

    Cost Monitoring

    Start by getting a firm handle on internal and external costs. Understand and analyze fluctuations in the cost of raw materials, production, and supply chain for your business to operate. Make sure that your products are priced with pre-defined logic so changes in price on one SKU don’t create confusion with another. For example, faux leather costs rise while genuine leather stays the same. In that case, a leather version of a product should be raised to reflect the price increase in the pleather variation, not to devalue the perception of luxury.

    Next, you will want to understand historical pricing trends as well as pricing indexes across your categories. These insights can help your teams anticipate cost fluctuations before they even arise and mitigate the risk that economic shifts create, even unexpected tariffs.

    Competition Tracking

    Tracking your competition is likely already a strategy you have in mind. But how well are your teams executing this important task? If they’re trying to watch for market shifts and adjust pricing in real time without the help of technology, things are likely slipping through the cracks.

    Competitive intelligence solutions help retailers discover all competitive SKUs across the e-commerce market, monitor for real-time pricing shifts, and take action to mitigate risk. You need an “always-on” competitive pricing strategy now so that the second a tariff is announced, you can see how it’s affecting your market. This way, you can maintain price competitiveness and avoid margin erosion when competitors’ pricing changes in response to a tariff or other market shift.

    Consumer Impact Assessment

    The multiplier effect is felt throughout the supply chain when tariffs are implemented. The effect can affect consumers in a number of ways and cause them to become spending averse in certain areas. Often, during times of economic hardship, grocery items remain relatively inelastic. This is because consumers continue to purchase essentials regardless of price changes. Conversely, the price of eating out or home delivery becomes more elastic since consumers cut back on dining expenses when costs rise across their shopping basket.

    You need to establish clear visibility into the results of your pricing changes. The goal should be to monitor progress and measure the ROI on specific and broad pricing changes across your assortment. Conducting market share impact analysis will also help you determine if you are losing out on potential customers or whether a decline in sales is being felt across your competition. Impact analysis tools can help your company check actual deployed price changes in real time.

    Response Framework

    Tariff response action plan for retailers

    Once you’ve prepared your team with strategies and technologies to set them up for success, it’s time to think about what to do once a tariff is announced or implemented. Here are three real-time decision-making strategies you should consider before your feet are to the fire. Having these in your back pocket will help you avoid financial disruption.

    Price Adjustment Strategies

    Think about how you strategically adjust prices. These could include percentage increases, flat rate increases, or absorbed via other strategies like bundling. You should also determine a cost increase threshold that you’re willing to absorb before raising prices. Think about the importance of remaining price attractive to consumers and weigh the risk of increasing prices past consumers’ ability or willingness to pay.

    Promotion Planning

    Folding increased costs into value-added offerings for consumers can be a good way to retain customer sentiment and sales volume without negatively affecting profit margins. You can leverage discounts, promotions, or bundling options to sell more of an item to a customer at a lower per-unit cost.

    What you don’t want to do is panic-adjust prices in response to tariffs of competitor moves. Instead, you can use a tool competitor intelligence solutions to watch if your competition is holding prices steady or adjusting. With full information about pricing at your disposal, you can make better decisions on your promotional strategy and not undercut yourself or lose customer loyalty.

    Alternative Sourcing

    Let’s face it: putting all your eggs in one basket is bad for business. Instead of relying solely on a single supplier for production, you should have a diverse set of suppliers ready and able to shift production when tariffs are announced. If a tariff impacts Chinese exports, having a backup supplier in Vietnam can prevent added costs entirely. You can also consider strategies like bulk pricing, set pricing, or shifting entirely to domestic suppliers.

    Forward Buying

    Proactively stockpile inventory by purchasing large quantities of at-risk products before tariffs take effect. This strategy locks in lower costs and ensures supply continuity during disruptions. However, balance this with careful demand forecasting to avoid overstocking, which ties up cash flow and incurs storage costs. Use historical sales data and tariff implementation timelines to optimize order volumes—this is especially effective for products with stable demand or long shelf lives.

    Market Intelligence Requirements

    Preparing your pricing teams and giving them a framework upon which to act when tariffs are announced doesn’t have to be complicated. You can get access to the right data on costs, competitors, and consumer behavior with DataWeave’s pricing intelligence capability.

    We provide retailers with insights on pricing trends, category exposure, and competitor adjustments. Our AI-powered competitor intelligence solutions allow you to get timely alerts whenever a significant change happens. This can include changes to competitor pricing and category-level shifts that you’d otherwise react to when it’s too late.

    These automated insights can also help you track historical pricing trends, elasticity, and margin impact to construct a clear response framework in an emergency. Additionally, our analytics capabilities can help you identify patterns to power pre-emptive pricing and promotional strategies.

    Getting the right pricing intelligence strategy in place now can prevent disaster later. Think through your preparedness strategy and how you want your teams to respond in the event of a new tariff, and consider how much easier reacting accurately would be with all the data needed at your fingertips. Reach out to us to know more.

  • Beyond MAP Pricing: Strategic Approaches for Brands and Retailers

    Beyond MAP Pricing: Strategic Approaches for Brands and Retailers

    Many retailers view minimum advertised pricing (MAP) policies as a necessary evil since they present several challenges for competitive positioning. In an idealistic free market, there wouldn’t be a need for MAP policies, and healthy competition would do the work of setting the final advertised price.

    However, MAP policies aren’t beneficial only for brands; they also greatly benefit retailers. This article will examine why MAP pricing can be a strategic advantage for both brands and retailers. We’ll also look at ways brand managers and retail pricing teams can navigate MAP requirements to maintain profitability and safeguard customer trust.

    Understanding MAP Fundamentals

    Minimum Advertised Price (MAP) is a policy set by brands that requires their sales channels to price the brand’s products at a minimum dollar value. Retailers are free to price the items higher, but the advertised price is never to exceed the minimum threshold.

    This agreement is established at the outset of a relationship or new product launch and can change at the brand’s discretion. Consumers typically see only the minimum advertised price when they search for a product across competing retailers. This means retailers need to find other ways to differentiate themselves beyond offering the lowest price.

    But a retailer can still effectively price the product at a lower cost to win sales away from the competition. This comes in the form of discounts applied at checkout, bundled deals, or other promotions that affect the final cart but not the advertised price. Only the advertised price must remain within MAP guidelines. This gives retailers a way to set themselves apart from the competition while still protecting the brand.

    A minimum advertised price has three central values: one for the brand, one for the retailer, and one for both.

    1. Brand or manufacturer: A MAP policy protects the brand’s value and prevents price erosion. If a retailer consistently undercuts a product’s price to make it more competitive, customers may begin to perceive the brand as lower in value over time. It can cause the brand to appear less premium than if prices hold steady. If a customer pays full price one day and then sees the same item advertised at a lower base price the next, it can weaken brand loyalty and cause dissatisfaction.
    2. Retailer: Minimum advertised pricing policies prevent retailers from engaging in a pricing war with one another, driving the price of an item down and hurting margins. This race to the bottom is bad for business. Apart from reducing profits, it discourages sellers from investing in marketing and other activities that drive sales. It also means that smaller retailers can compete with larger retailers, effectively leveling the playing field across the market.
    3. All parties: The issue of counterfeit and unauthorized sellers on the grey market plagues retailers and brands. One of the most straightforward ways to identify these sellers that undercut prices and damage brand perception is to track who is pricing products outside of agreements. Unauthorized or counterfeit sellers can be identified by establishing a MAP policy and monitoring who sells at the wrong price. Then, official legal action can be taken to prevent those merchants from selling the product.

    Brand Perspective

    Developing a clear and precise MAP policy is an important option for brands looking to stay competitive. Make sure you outline the minimum advertised price for each product for each sales channel and do so by geography. Write clear instructions on how discounts, promotions, and sales can be applied to the advertised price to avoid misunderstandings later. Ensure you work with your legal team to fill in any gaps before presenting them to retailers.

    If you find sellers acting outside the MAP policy, you must act swiftly to enforce your MAP policy. Cease and desist orders are the most common enforcement strategy a brand can use on unauthorized sellers and counterfeiters. But there are legal considerations for authorized sellers, too. You may need to fine the retailer for damages, restrict inventory replenishment until prices have been adjusted, remove seller authorization by terminating the relationship entirely, or escalate to your legal team.

    Open communication between the brand and retailer is in everyone’s best interest to ensure minimum pricing is being used. Have explanatory documents available for your retailers’ non-legal teams to reference while they set prices. These can take the form of checklists, video explainers, or even well-informed brand representatives working closely with retail pricing teams. It’s likely that some MAP violations will occur from time to time. The importance your retail partners place on fixing those errors will help you determine how much goodwill you will give them in the future.

    Brands can consider rewarding retailers that consistently adhere to minimum advertised price policies. Rewards often take the form of more lenient promotion policies, especially during major holidays like Christmas, Prime Day, or Black Friday. However, it’s never advisable to relax the actual MAP policy to allow one retailer to advertise a lower price year-round.

    Retailer Strategies

    A retailer can take several approaches to complying with a brand’s MAP policy while still maximizing sales. First, you need a dedicated compliance process spearheaded by compliance specialists or, better yet, enabled by technology. Embedding a process that checks for MAP violations into daily or weekly operations will prevent problems before brands become aware.

    Automated price tracking tools can help discover discrepancies so that you don’t accidentally violate a MAP agreement. Make sure MAP training extends beyond your pricing team and includes marketing. Anyone who participates in promotions or events should be made aware of the agreements made with specific brands. Determine if there are alternative promotion methods available to attract customers. You could offer free shipping on certain items, bundle giveaways, or apply cart-wide discounts at checkout.

    Monitoring your competition in real time will also help you stay ahead. If you discover a competitor undercutting your prices, bring this to the attention of your brand representative. This can build loyalty with the brand and help prevent lost sales due to market share loss.

    Digital Implementation for MAP Compliance

    Pricing teams at brands and retailers manually attempting to manage MAP pricing will lag behind the competition without help. They must discover, monitor, and enforce MAP compliance simply and effectively.

    Over the past several years, there has been a seemingly exponential proliferation of online sellers, complicating the industry and making it nearly impossible to find and discover all instances of every product you sell. It’s further complicated by marketplaces like Amazon, Walmart, and eBay, which are full of individual unauthorized sellers and resellers.

    Implementing a digital tool is the first step to effectively discovering and monitoring MAP compliance, even across these marketplaces. This tool should monitor all competition for you and discover imbalances in pricing parity.

    DataWeave’s MAP Violations Merchant Analytics solution has AI-backed software that scours the web for your products. It uses identifiers like UPCs and product titles and compares imagery to find where the product is sold. Our AI-powered image recognition capabilities are especially helpful in identifying inauthentic listings that may be counterfeit products or unauthorized sellers. It also has built-in geographic and channel-specific MAP monitoring capabilities to help with localized enforcement.

    The tool can aggregate all this data and present dashboard views of your own and competitors’ pricing that are easy to digest and act on. After all, retailers need to monitor their own MAP compliance as well as the competition’s. Brands can also track competitor sellers’ networks to explore potential new retail partnerships and grow their network reach.

    The MAP Violations Merchant Analytics solution has automated violation alerts and advanced reporting built into it. This means you can get real-time alerts instead of pouring through dashboards searching for exceptions each week. For deeper insights, the dashboards provide time-stamped proof of which sellers are undercutting MAP minimums, so you have all the information you need to make a case against them. Discovering repeat offenders is easy with historical trends dashboards that show which sellers have a history of violations.

    With all this information on who is violating what—and when—enforcement becomes much more manageable. Send cease and desist orders to unauthorized sellers and start having conversations with authorized sellers acting outside of your agreement. Acting quickly will help prevent hits to your brand’s reputation, price erosion, and lost sales.

    DataWeave’s MAP solution gives you the competitive edge to effectively discover MAP violations, monitor market activity, and act quickly when an issue is discovered.

    Make MAP Compliance a Strategic Advantage

    Basic MAP compliance and enforcement isn’t simply about setting pricing policies anymore. These policies are foundational to brand strategies, maintaining good relationships with retailers, and establishing long-term profitability for your business.

    When you let MAP violations go unchecked, it can erode your margins, damage how your customers perceive your brand, and create confusion across channels. Discovering, monitoring, and acting on MAP violations is much easier with the help of tools like DataWeave’s AI-enabled MAP Violations Merchant Analytics.

    Ready to take control of MAP pricing at your company? Request a MAP policy assessment from DataWeave today!

  • Portfolio Enhancement Through Price Relationship Management: Building Coherent Pricing Across Product Lines

    Portfolio Enhancement Through Price Relationship Management: Building Coherent Pricing Across Product Lines

    Do you remember when the movie Super Size Me came out? If you missed it, it was about the harmful effects of eating fast food too often. One aspect of the film that stands out is McDonald’s clever use of pricing to encourage consumers to buy bigger—and therefore more expensive—meals.

    Hungry patrons could upgrade their meal to a Super Size version for only a few cents more. In doing so, McDonald’s was able to capitalize on perceived value, i.e., getting more product for an apparent lower total price for the volume. It encouraged restaurant-goers to spend a little more while feeling like they got a great deal. It was a smart use of strategic pricing.

    There are hundreds of pricing relationship types like this one that pricing leaders need to be aware of and can use to their advantage when creating their team’s pricing strategy and workflows. You need to maintain profitable and logical price relationships across your entire product portfolio while keeping up with the competition. After all, the gimmick to Super Size would never have worked if the upgrade had been of less value than just buying another burger, for example.

    In this article, we’ll examine more real-world examples of pricing challenges so you can consider the best ways to manage complex price relationships. We’ll examine things like package sizes, brands, and product lines and how they’re intertwined in systematic price relationship management. Read on to learn how to prevent margin erosion, improve customer perception of your brand, and keep your pricing consistent and competitive.

    The Price Relationship Challenge

    Pricing is one of the most challenging aspects of managing a retail brand. This is especially true if you are dealing with a large assortment of products, including private label items, the same products of differing sizes, and hundreds, or even thousands, of competing products to link. Inconsistencies in your price relationship management can confuse customers, erode trust, and harm your bottom line.

    Let’s take a look at a few common pitfalls in portfolio pricing that you might run into in real life to better understand the impact on customer perception, trust, and sales.

    Pricing Relationship Challenges Retailers Need to Account For

    Private Label vs. Premium Product Pricing

    Let’s consider a nuanced scenario where price relationships between a retailer’s private label and premium branded products create an unexpected customer perception. Imagine you’re in a supermarket, comparing prices on peanut butter. You’ve always opted for the store’s private-label brand, “Best Choice,” because it’s typically the more affordable option. Here’s what you find:

    • Best Choice (Private Label) 16 oz – $3.50
    • Jif (National Brand) 16 oz – $3.25

    At first glance, this pricing feels off—shouldn’t the private label be the cheaper option? If a customer has been conditioned to expect savings with private-label products, seeing a national brand undercut that price could make them pause.
    This kind of pricing misalignment can erode trust in private-label value and even push customers toward the national brand. When price relationships don’t follow consumer expectations, they create friction in the shopping experience and may lead to lost sales for the retailer’s own brand.

    Value Size Relationships

    A strong value-size relationship ensures that customers receive logical pricing as they move between different sizes of the same product. When this relationship is misaligned, customers may feel confused or misled, which can lead to lost sales and eroded trust.

    Let’s look at a real-world example using a well-known branded product—salad dressing. Imagine you’re shopping for Hidden Valley Ranch (HVR) dressing and see the following pricing on the shelf:

    • HVR 16 oz – $3.99
    • HVR 24 oz – $6.49
    • HVR 36 oz – $8.99

    At first glance, you might assume that buying a larger size offers better value. However, a quick calculation shows that the price per ounce actually increases with size:

    • 16 oz = $0.25 per ounce
    • 24 oz = $0.27 per ounce
    • 36 oz = $0.25 per ounce

    Customers expecting a discount for buying in bulk may feel misled or frustrated when they realize the mid-size option (24 oz) is actually the most expensive per ounce. This mispricing could drive shoppers to purchase the smallest size instead of the intended larger, more profitable unit—or worse, lead them to a competitor with clearer pricing structures.

    Retailers must maintain logical price progression by ensuring that price per unit decreases as the product size increases. This not only improves customer trust but also encourages higher-volume purchases, driving profitability while maintaining a fair value perception.

    Price Link Relationships

    A well-structured price link relationship ensures customers can easily compare similar offerings of the same product and size. When the pricing across different versions or variations of the same item isn’t clear or consistent, it can confuse customers and damage trust, ultimately leading to missed sales and a negative brand perception.

    Let’s break this down with an example of a popular product—coffee. Imagine you’re shopping for a bag of Starbucks coffee and you see the following pricing on the shelf:

    • Starbucks Classic Coffee, 12 oz – $7.99
    • Starbucks Coffee, Mocha, 12 oz – $9.99
    • Starbucks Ground Coffee, Pumpkin Spice, 12 oz – $12.99

    At first glance, the product is the same size (12 oz) across all options, but the prices vary significantly. One might assume that the price difference is due to differences in quality or features, but what if there’s no clear indication of why the different flavors are priced higher than the standard?

    After investigating, you may realize that the only differences are related to different variants—like “Mocha” or “Pumpkin Spice” rather than any significant changes in the product’s core attributes. When customers realize they’re paying a premium for just different flavors, without any tangible difference in product quality, it can lead to feelings of confusion and frustration.

    Retailers must ensure that price links between similar offerings are justifiable by clearly communicating what differentiates each product. This avoids the perception that customers are being charged extra for little added value, building trust and encouraging repeat purchases. By maintaining transparent price link relationships, businesses can foster customer loyalty, increase sales, and drive better overall satisfaction.

    What is the Foundational Process to Tackle the Price Relationship Challenge?

    Now that we’ve reviewed several challenges brands face when pricing their products, what can be done about them?

    If you’re a pricing leader, you must create a robust pricing strategy that considers customer expectations, competitive data, sizing, and the overall value progressions of your product assortment. These are the three foundational steps to solve your price relationship challenges.

    1. First, you need to group products together accurately.
    2. Second, you need to establish price management rules around the group of related items.
    3. Third, you should set in place a process to review your assortment each week to see if anything is out of tolerance.

    This process is difficult, if not impossible, to manage manually. To effectively set up and execute these steps, you’ll need the help of an advanced pricing intelligence system.

    Implementation Strategy

    Want to know how to roll out a price relationship management strategy? Follow this implementation strategy for a practical way to get started.

    1. Set up price relationship rules: Determine which of your products go together, such as same products with different sizes or color options. Assign different product assortment groups and determine tolerances for scaling prices based on volume or unit counts.
    2. Monitoring and maintenance: Establish rules to alert the appropriate party when something is out of tolerance or a price change has been discovered with a competitive product.
    3. Exception management: Only spend time actioning the exceptions instead of pouring through clean data each week, looking for discrepancies. This will save your team time and help address the most significant opportunities first.
    4. Change management considerations: Think about the current processes you have in place. How will this affect the individuals on your team who have managed pricing operations? Establish a methodology for rolling this new strategy and technology out over select product assortments or brands one at a time to build trust with internal players.

    DataWeave offers features specifically built to help pricing teams manage pricing strategies. These applications can help you optimize profit margins and improve your overall market positioning for long-term success. A concerted effort to create brand hierarchies within your own product assortment from the get-go, followed by routine monitoring and real-time updates, can make all the difference in your pricing efforts.

    Within DataWeave, you can create price links between your products (value sizing) and those of the competition. These will alert you to exceptions when discrepancies are discovered outside your established tolerance levels. If a linked set of your products in different sizes shows inconsistent pricing based on scaled volumes, your team can quickly know how to make changes. If a competitor’s price drops significantly, you can react to that change before you lose sales.

    DataWeave even offers AI-driven similar product matching capabilities, which can help you manage pricing for private label products by finding and analyzing similar products across the market.

    If you want to learn more about price relationship management, connect with our team at DataWeave today.

  • Maximizing Competitive Match Rates: The Foundation of Effective Price Intelligence

    Maximizing Competitive Match Rates: The Foundation of Effective Price Intelligence

    Merchants make countless pricing decisions every day. Whether you’re a brand selling online, a traditional brick-and-mortar retailer, or another seller attempting to navigate the vast world of commerce, figuring out the most effective price intelligence strategy is essential. Having your plan in place will help you price your products in the sweet spot that enhances your price image and maximizes profits.

    For the best chance of success, your overall pricing strategy must include competitive intelligence.

    Many retailers focus their efforts on just collecting the data. But that’s only a portion of the puzzle. The real value lies in match accuracy and knowing exactly which competitor products to compare against. In this article, we will dive deeper into cutting-edge approaches that combine the traditional matching techniques you already leverage with AI to improve your match rates dramatically.

    If you’re a pricing director, category manager, commercial leader, or anyone else who deals with pricing intelligence, this article will help you understand why competitive match rates matter and how you can improve yours.

    Change your mindset from tactical to strategic and see the benefits in your bottom line.

    The Match Rate Challenge

    To the layman, tracking and comparing prices against the competition seems easy. Just match up two products and see which ones are the same! In reality, it’s much more challenging. There are thousands of products to discover, analyze, compare, and derive subjective comparisons from. Not only that, product catalogs across the market are constantly evolving and growing, so keeping up becomes a race of attrition with your competitors.

    Let’s put it into focus. Imagine you’re trying to price a 12-pack of Coca-Cola. This is a well-known product that, hypothetically, should be easy to identify across the web. However, every retailer uses their own description in their listing. Some examples include:

    How product names differ on websites - Amazon Example
    Why matching products is a challenge - Naming conventions on Target
    Match Rate Challenge - how product names differ on retailers - Wamlart
    • Retailer A lists it as “Coca-Cola 12 Fl. Oz 12 Pack”
    • Retailer B shows “Coca Cola Classic Soda Pop Fridge Pack, 12 Fl. Oz Cans, 12-Pack”
    • Retailer C has “Coca-Cola Soda – 12pk/12 fl oz Cans”

    While a human can easily deduce that these are the same product, the automated system you probably have in place right now is most likely struggling. It cannot tell the difference between the retailers’ unique naming conventions, including brand name, description, bundle, unit count, special characters, or sizing.

    This has real-world business impacts if your tools cannot accurately compare the price of a Coca-Cola 12-pack across the market.

    Why Match Rates Matter

    If your competitive match rates are poor, you aren’t seeing the whole picture and are either overcharging, undercharging, or reacting to market shifts too slowly.

    Overcharging can result in lost sales, while undercharging may result in out-of-stock due to spikes in demand you haven’t accounted for. Both are recipes to lose out on potential revenue, disappoint customers, and drive business to your competitors.

    What you need is a sophisticated matching capability that can handle the tracking of millions of competitive prices each week. It needs to be able to compare using hundreds of possible permutations, something that is impossible for pricing teams to do manually, especially at scale. With technology to make this connection, you aren’t missing out on essential competitive intelligence.

    The Business Impact

    Besides the bottom-line savings, accurately matching competitor products for pricing intelligence has other business impacts that can help your business. Adding technology to your workflow to improve match rates can help identify blind spots, improve decision quality, and improve operational efficiency.

    • Pricing Blind Spots
      • Missing competitor prices on key products
      • Inability to detect competitive threats
      • Delayed response to market changes
    • Decision Quality
      • Incomplete competitive coverage leads to suboptimal pricing
      • Risk of pricing decisions based on wrong product comparisons
    • Operational Efficiency
      • Manual verification costs
      • Time spent reconciling mismatched products
      • Resources needed to maintain price position

    Current Industry Challenges

    As mentioned, the #1 reason businesses like yours probably aren’t already finding the most accurate matches is that not all sites carry comparable product codes. If every listing had a consistent product code, it would be very easy to match that code to your code base. In fact, most retailers currently only achieve 60-70% match rates using their traditional methods.

    Different product naming conventions, constantly changing product catalogs, and regional product variations contribute to the industry challenges, not to mention the difficulty of finding brand equivalencies and private label comparisons across the competition. So, if you’re struggling, just know everyone else is as well. However, there is a significant opportunity to get ahead of your competition if you can improve your match rates with technology.

    The Matching Hierarchy

    • Direct Code Matching: There are a number of ways to start finding matches across the market. The base tier of the hierarchy of most accurate approaches is Direct Code matching. Most likely, your team already has a process in place that can compare UPC to UPC, for example. When no standard codes are listed, your team is left with a blind spot. This poses limitations in modern retail but is an essential first step to identifying the “low-hanging fruit” to start getting matches.
    • Non-Code-Based Matching: The next level of the hierarchy is implementing non-code-based matching strategies. This is when there are no UPCs, DPCIs, ASINs, or other known codes that make it easy to do one-to-one comparisons. These tools can analyze complex metrics like direct size comparisons, unique product descriptions, and features to find more accurate matches. They can look deep into the listing to extract data points beyond a code, even going as far as analyzing images and video content to help find matches. Advanced technologies for competitive matching can help pricing teams by adding different comparison metrics to their arsenal beyond code-based. 
    • Private Label Conversions: Up until this level of the hierarchy, comparisons relied on direct comparisons. Finding identical codes and features and naming similarities is excellent for figuring out one-to-one comparisons, but when there is no similar product to compare with for pricing intelligence, things get more complicated. This is the third tier of the matching hierarchy. It’s the ability to find similar product matches for ‘like’ products. This can be used for private label conversions and to create meaningful comparisons without direct matches.
    • Similar Size Mappings: This final rung on the matching hierarchy adds another layer of advanced calculations to the comparison capability. Often, retailers and merchants list a product with different sizing values. One may choose to bundle products, break apart packs to sell as single items or offer a special-sized product manufactured just for them. 
    Similar Size Mappings - product matching hierarchy - Walmart
    Similar Size Mappings - product matching hierarchy - Costco

    While at the end of the day, the actual product is the same, when there are unusual size permutations, it can be hard to identify the similarities. Technology can help with value size relationships, package variation handling, size equalization, and unit normalization.

    The AI Advantage

    AI is the natural solution for efficiently executing competitive product matching at scale. DataWeave offers solutions for pricing teams to help them reach over 95% product match accuracy. The tools leverage the most modern Natural Language Processing models for ingesting and analyzing product descriptions. Image recognition capabilities apply methods such as object detection, background removal, and image quality enhancement to focus on an individual product’s key features to improve match accuracy.

    Deep learning models have been trained on years of data to perform pattern recognition in product attributes and to learn from historical matches. All of these capabilities, and others, automate the attribute matching process, from code to image to feature description, to help pricing teams build the most accurate profile of products across the market for highly accurate pricing intelligence.

    Implementation Strategy

    We understand that moving away from manual product comparison methods can be challenging. Every organization is different, but some fundamental steps can be followed for success when leveling up your pricing teams’ workflow.

    1. First, conduct a baseline assessment. Figure out where you are on the Matching hierarchy. Are you still only doing direct code-based comparisons? Has your team branched out to compare other non-code-based identifiers?
    2. Next, establish clear match rate targets for yourself. If your current match rate is aligned with industry norms, strive to significantly improve it, aiming for a high alignment that supports maximizing the match rate. Break this down into achievable milestones across different stages of the implementation process.
    3. Work with your vendor on quality control processes. It may be worth running your current process in tandem to be able to calculate the improvements in real time. With a veteran technology provider like DataWeave, you can rely on the most cutting-edge technology combined with human-in-the-loop checks and balances and a team of knowledgeable support personnel. Additionally, for teams wanting direct control, DataWeave’s Approve/Disapprove Module lets your team review and validate match recommendations before they go live, maintaining full oversight of the matching process.
    4. The more data about your products it has, the better your match rates. DataWeave’s competitive intelligence tools also come with a built-in continuous improvement framework. Part of this is the human element that continually ensures high-quality matches, but another is the AI’s ‘learning’ capabilities. Every time the AI is exposed to a new scenario, it learns for the next time.
    5. The final step, ensure cross-functional alignment is achieved. Every one from the C-Suite down should be able to access the synthesized information useful for their role without complex data to sift through. Customized dashboards and reports can help with this process.

    Future-Proofing Match Rates

    The world of retail is constantly evolving. If you don’t keep up, you’re going to be left behind. There are emerging retail channels, like the TikTok shop, and new product identification methods to leverage, like image comparisons. As more products enter the market along with new retailers, figuring out how to scale needs to be taken into consideration. It’s impossible to keep up with manual processes. Instead, think about maximizing your match rates every week and not letting them degrade over time. A combination of scale, timely action, and highly accurate match rates will help you price your products the most competitively.

    Key Takeaways

    Match rates are the foundation of pricing intelligence. You can evaluate how advanced your match rate strategy is based on the matching hierarchy. If you’re still early in your journey, you’re likely still relying on code-to-code matches. However, using a mix of AI and traditional methods, you can achieve a 95% accuracy rate on product matching, leading to overall higher competitive match rates. As a result, with continuous improvement, you will stay ahead of the competition even as the goalposts change and new variables are introduced to the competitive landscape.

    Starting this process to add AI to your pricing strategy can be overwhelming. At DataWeave, we work with you to make the change easy. Talk to us today to know more.

  • Beyond Basic Price Monitoring: Advanced Applications of Competitive Intelligence

    Beyond Basic Price Monitoring: Advanced Applications of Competitive Intelligence

    It’s up to senior leadership, whether you’re a Chief Strategy Officer, Pricing Executive, or Commercial Director, to think big picture about your company’s competitive intelligence strategy. For more junior team members, it’s easy to get caught in the “this is how we’ve always done it” mindset and continue to go through the motions of price monitoring.

    You don’t have that luxury—it’s up to you to find and implement new ways to move beyond basic price monitoring and usher your company into an era of achieving actionable insights through competitive intelligence. There is much more to gain from competitive data than simple price monitoring.

    How can retailers leverage clean, competitive data to uncover strategic insights beyond basic price comparisons? This article will help you shift your mindset from tactical monitoring to strategic insight generation. We’ll see how sophisticated analysis of clean and refined competitive data can reveal competitor strategies, regional and geographic opportunities, and overall market trends.

    It’s time to shift away from standard reporting, which should be left for your pricing owners and end users, and towards gaining competitive intelligence to shape your holistic company pricing strategy. With the right tools, you can make this shift a reality.

    Regional Price Intelligence

    One significant opportunity you should take advantage of is a greater understanding of regional price intelligence. Understanding the nuances that shape how products, categories, and other retailers’ prices according to geographical differences can set your company up to win customer trust and dollars at checkout.

    Understanding geographic and regional pricing strategies

    Geographic price intelligence helps leaders leverage market opportunities based on where sales are happening. Variations in how products and categories are priced across regions often reflect competitor tactics, local demand, and cost structures.

    Let’s consider an example that impacts a broad geography, such as the entire continental United States – egg prices. Eggs are a staple grocery item and are frequently a loss leader in stores. This means they are products priced below their cost specifically to draw customers into stores.

    However, Avian Flu outbreaks affecting millions of birds have become more common recently. These outbreaks drive the cost of eggs higher as flocks must be culled to prevent the spread of the disease. This means that retailers must act to maintain acceptable margins or losses without frightening away customers or losing their trust.

    Avian Flu has been especially bad in Iowa and California. Retailers in these regions face tough decisions during outbreaks. They need to figure out how to balance the high prices required to cover the supply shortages with maintaining consumer trust that this staple product will not be perceived as ‘overpriced.’ Customers expect retailers to be fair even when supply chain issues make it challenging to keep prices stable.

    Another example impacting the broader USA is credit card defaults. Credit card defaults are reaching levels unseen since the financial crisis of 2008. $46 billion worth of credit card balances were written off in the first nine months of 2024 alone. This unprecedented figure highlights the fact that many Americans are struggling financially. Higher-income earners continue to do ok, but lower-income families are feeling the pressure more than ever.

    Understanding the differences between the geographies you sell in can help you construct your pricing strategies better. This is especially true as consumers brace themselves for more anticipated economic hardship.

    Retailers must set realistic financial targets without overpricing their catalogs. Otherwise, they risk losing customers who would otherwise have bought their products. Competitive intelligence can help retailers understand how economic disparities impact core consumer bases.

    Pricing leaders can leverage insights around geographic variations in supply, demand, and competitor pricing to help in situations like these. With how important eggs are, changes to their price can spill over into other categories. And with credit card defaults affecting hundreds of thousands of Americans, having a way to dive into these topics can help shape overarching strategies.

    Customer perception is a recurring theme in competitive intelligence. It’s not only about the actual value your brand offers but the perceived value based on historical context, current events, and competition.

    Leveraging Regional Price Differences for Strategic Advantage

    On the topic of customer perception, there are strategic ways to use customer perception to your advantage. One of these is detecting cross-market arbitrage opportunities using competitive intelligence and actioning them.

    But what is cross-market arbitrage? It’s the practice of exploiting the differences in price across different markets or regions. As a retailer, you can use cross-market arbitrage to your advantage by finding disparities in market conditions and strategically pricing your products to entice customers or offer more value. These opportunities can be in demand, supply, or competitive pricing. Acting quickly in markets where frequent disruptions happen can drive your business forward.

    DataWeave’s advanced competitive intelligence tools can analyze regional market trends to help you respond to real-time local demand fluctuations or cost pressures.

    Local Market Dynamics

    Pricing isn’t a one-size-fits-all operation. Where and what you’re pricing truly matters. Pricing teams should take price zones into account when constructing pricing strategies. This is because pricing isn’t equivalent across locations; it’s localized. Understanding this fact is critical for category-specific considerations at the macro and micro levels.

    This map shows a retailer’s regional price differentials on a breakfast basket. With the ability to access and refine your data, you can better construct maps like this one to track local market dynamics. Determine where you need to differentiate prices based on locality, understand the strategic stance of your competitors, and plan if you start to see changes in competitive price zones.

    Map shows a retailer's regional price differentials on a breakfast basket

    Competitor Strategy Detection

    As a retailer, you should continuously monitor your competitors, whether they’re succeeding or stagnating. One example of a major retailer that is seeing growth even during this challenging economic time is Costco. Costco is an interesting case because they do not have stores in every major city or even in every state.

    Costco has its brand strategy down, and it is tied to the pricing strategy. Costco has committed to its customers to provide quality items at competitive prices, and they’ve delivered even in a volatile economy. Costco has managed to maintain competitive prices on core merchandise and make strategic pricing adjustments on items that matter most to members. Their private label brand, Kirkland Signature, highlights their value-first approach. They continue to lead with price reductions like:

    • Organic Peanut Butter: $11.49 → $9.99
    • Chicken Stock: $9.99 → $8.99
    • Sauvignon Blanc: $7.49 → $6.99

    Costco has figured out how to balance premium offerings for cost-conscious consumers with standardly priced items filling the shopper’s basket. This demonstrates that they have a pricing strategy that relies on competitive intelligence and market trends.

    With the correct data and tools, any retailer can conduct research to discover more about their competitors and gain usable insights into their implemented pricing strategies. Once established, this can act as an early warning signal so you can take action.

    For example, understanding whether a retailer operates with a stable Everyday Low Price (EDLP) strategy or a more dynamic High/Low pricing approach is crucial when building and maintaining the integrity of your pricing strategy.

    Data should be able to show you things like:

    • When holiday price decreases start to accelerate
    • How quickly a retailer responds to cost increases (especially at the category or item level)
    • The cadence of seasonal campaigns and their impact on pricing behavior

    When we move beyond the numbers, these patterns tell a story about how to win in today’s competitive retail landscape. After all, pricing isn’t just a standard reporting tactic. In its truest form, it’s a strategy rooted in understanding the bigger picture of your consumers, competition, and the economy.

    Actionable Intelligence Framework

    With a practical system to turn insights into action, your company’s pricing strategy is much more likely to drive actual results. Merely collecting data and churning out out-of-date reports won’t cut it. Instead, begin to identify patterns and insights for accurate competitive intelligence. Use this simple framework to start setting up a comprehensive competitive intelligence process.

    • Setting up monitoring systems: Leverage technology to monitor and aggregate data on your competition, market trends, and consumer behavior. Ensure the system chosen can clean and refine the data along the way so it’s ready to be analyzed.
    • Creating action triggers: Define clear thresholds and triggers based on key insights. These can be things like price changes of a certain amount, competitor moves, assortment changes, or regional and geographic trends. These triggers should prompt specific, pre-planned actions for your team to capitalize on opportunities.
    • Response protocol development: Change management is easier with a plan. Work on building out and training your teams on protocols for specific triggers. When something arises, they know the protocol to take advantage of the opportunity or mitigate the challenge effectively.
    • Performance measurement: Measure the impact of your team’s protocol-based actions with the help of pre-determined KPIs and then hone your approach to competitive intelligence based on the results.

    Competitive Intelligence at Your Fingertips

    Shifting from a latent standard reporting and price monitoring mindset to a growth mindset centered around competitive intelligence doesn’t need to be a struggle. The key is to lean on the tools that will accelerate your company’s journey to finding the right insights and putting action behind them quickly.

    Start by conducting a competitive intelligence maturity assessment to evaluate your organization’s current state and identify areas for improvement. Then, create a capability development roadmap for your company to track efficacy and progress toward your goal.

    Want to talk to the experts in competitive pricing intelligence? Click here to speak with the DataWeave team!

  • From Raw Data to Retail Pricing Intelligence: Transforming Competitive Data into Strategic Assets

    From Raw Data to Retail Pricing Intelligence: Transforming Competitive Data into Strategic Assets

    Poor retail data is the bane of Chief Commercial Officers and VPs of Pricing. If you don’t have the correct inputs or enough of them in real time, you can’t make data-driven business decisions regarding pricing.

    Retail data isn’t limited to your product assortment. Price data from your competition is as important as understanding your brand hierarchies and value size progressions. However, the vast and expanding nature of e-commerce means new competitors are around every corner, creating more raw data for your teams.

    Think of competitive price data like crude oil. Crude or unrefined oil is an extremely valuable and sought-after commodity. But in its raw form, crude oil is relatively useless. Simply having it doesn’t benefit the owner. It must be transformed into refined oil before it can be used as fuel. This is the same for competitive data that hasn’t been transformed. Your competitive data needs to be refined into an accurate, consistent, and actionable form to power strategic insights.

    So, how can retailers transform vast amounts of competitive pricing data into actionable business intelligence? Read this article to find out.

    Poor Data Refinement vs. Good Refinement

    Let’s consider a new product launch as an example of poor price data refinement vs. good data refinement, which affects most sellers across industries.

    Retailer A

    Imagine you’re launching a limited-edition sneaker. Sneakerheads online have highly anticipated the launch, and you know your competitors are watching you closely as go-live looms.

    Now, imagine that your pricing data is outdated and unrefined when you go to price your new sneakers. You base your pricing assumptions on last year’s historical data and don’t have a way to account for real-time competitor movements. You price your new product the same as last year’s limited-edition sneaker.

    Your competitor, having learned from last year, anticipates your new product’s price and has a sale lined up to go live mid-launch that undercuts you. Your team discovers this a week later and reacts with a markdown on the new product, fearing demand will lessen without action.

    Customers who have already bought the much-anticipated sneakers feel like they’ve been overcharged now, and backlash on social media is swift. New buyers see the price reduction as proof that your sneakers aren’t popular, and demand decreases. This hurts your brand’s reputation, and the product launch is not deemed a success.

    Retailer B

    Imagine your company had refined competitive data to work with before launch. Your team can see trends in competitors’ promotional activity and can see that a line of sneakers at a major competitor is overdue for sale based on trends. Your team can anticipate that the competitor is planning to lower prices during your launch week in the hope of undercutting you.

    Instead of needing to react retroactively with a markdown, your team comes up with clever ways to bundle accessories with a ‘deal’ during launch week to create value beyond just the price. During launch week, your competitor’s sneakers look like the lesser option while your new sneakers look like the premium choice while still being a good value. Customer loyalty improves, and buzz on social media is positive.

    Here, we can see that refined data drives better decision-making and competitive advantage. It is the missing link in retail price intelligence and can set you ahead of the competition. However, turning raw competitive data into strategic insights is easier said than done. To achieve intelligence from truly refined competitive pricing data, pricing teams need to rely on technology.

    The Hidden Cost of Unrefined Data

    Technology is advancing rapidly, and more sellers are leveraging competitive pricing intelligence tools to make strategic pricing decisions. Retailers that continue to rely on old, manual pricing methods will soon be left behind.

    You might consider your competitive data process to be quite extensive. Perhaps you are successfully gathering vast data about your competitors. But simply having the raw data is just as ineffective as having access to crude oil and making no plan to refine it. Collection alone isn’t enough—you need to transform it into a usable state.

    Attempting to harmonize data using spreadsheets will waste time and give you only limited insights, which are often out of date by the time they’re discovered. Trying to crunch inflexible data will set your team up for failure and impact business decision quality.

    The Two Pillars of Data Refinement

    There are two foundational pillars in data refinement. Neither can truly be achieved manually, even with great effort.

    Competitive Matches

    There are always new sellers and new products being launched in the market. Competitive matching is the process of finding all these equivalent products across the web and tying them together with your products. It goes beyond matching UPCs to link identical products together. Instead, it involves matching products with similar features and characteristics, just as a shopper might decide to compare two similar products on the shelf. For instance private label brands are compared to legacy brands when consumers shop to discern value.

    A retailer using refined competitive matches can quickly and confidently adjust its prices during a promotional event, know where to increase prices in response to demand and availability and stay attractive to sensitive shoppers without undercutting margins.

    Internal Portfolio Matches

    Product matching is a combination of algorithmic and manual techniques that work to recognize and link identical products. This can even be done internally across your product portfolio. Retailers selling thousands or even hundreds of thousands of products know the challenge of consistently pricing items with varying levels of similarity or uniformity. If you must sell a 12oz bottle of shampoo for $3.00 based on its costs, then a 16oz bottle of the same product should not sell for $2.75, even if that aligns with the competition.

    Establishing a process for internal portfolio matching helps to eliminate inefficiencies caused by duplicated or misaligned product data. Instead of discovering discrepancies and having to fire-fight them one by one, an internal portfolio matching feature can help teams preempt this issue.

    Leveraging AI for Enhanced Match Rates

    As product SKUs proliferate and new sellers seem to enter the market at lightning speed, scaling is essential without hiring dozens more pricing experts. That’s where AI comes in. Not only can AI do the job of dozens of experts, but it also does it in a fraction of the time and at an improved match accuracy rate.

    DataWeave’s AI-powered pricing intelligence and price monitoring offerings help retailers uncover gaps and opportunities to stay competitive in the dynamic world of e-commerce. It can gather competitive data from across the market and accurately match competitor products with internal catalogs. It can also internally match your product portfolio, identifying product family trees and setting tolerances to avoid pricing mismatches. The AI synthesizes all this data and links products into a usable format. Teams can easily access reports and dashboards to get their questions answered without manually attempting to refine the data first.

    How AI helps convert raw data to pricing and assortment intelligence

    From Refinement to Business Value

    Refined competitive price data is your team’s foundation to execute these essential pricing functions: price management, price reporting, and competitive intelligence.

    Price Management

    Refined data is the core of accurate price management and product portfolio optimization. Imagine you’re an electronics seller offering a range of laptops and personal computing devices marketed toward college students. Without refined competitive data, you might fail to account for pricing differences based on regionality for similar products. Demand might be greater in one city than in another. By monitoring your competition, you can match your forecasted demand assumptions with competitor pricing trends to better manage your prices and even offer a greater assortment where there is more demand.

    Price Reporting

    Leadership is always looking for new and better market positioning opportunities. This often revolves around how products are priced, whether you’re making a profit, and where. To effectively communicate across departments and with leadership, pricing teams need a convenient way to report on pricing and make changes or updates as new ad hoc requests come through. Spending hours constructing a report on static data will feel like a waste when the C-Suite asks for it again next week but with current metrics. Refined, constantly updated price data nips this problem in the bud.

    Competitive Intelligence

    Unrefined data can’t be used to discover competitive intelligence accurately. You might miss a new player, fail to account for a new competitive product line, or be unable to extract insights quickly enough to be helpful. This can lead to missed opportunities and misinformed strategies. As a seller, your competitive intelligence should be able to fuel predictive scenario modeling. For example, you should be able to anticipate competitor price changes based on seasonal trends. Your outputs will be wrong without the correct inputs.

    Implementation Framework

    As a pricing leader, you can take these steps to begin evaluating your current process and improve your strategy.

    • Assess your current data quality: Determine whether your team is aggregating data across the entire competitive landscape. Ask yourself if all attributes, features, regionality, and other metrics are captured in a single usable format for your analysts to leverage.
    • Setting refinement objectives: If your competitive data isn’t refined, what are your objectives? Do you want to be able to match similar products or product families within your product portfolio?
    • Measuring success through KPIs: Establish a set of KPIs to keep you on track. Measure things like match rate accuracy, how quickly you can react to price changes, assortment overlaps, and price parity.
    • Building cross-functional alignment: Create dashboards and establish methods to build ad hoc reports for external departments. Start the conversation with data to build trust across teams and improve the business.

    What’s Next?

    The time is now to start evaluating your current data refinement process to improve your ability to capture and leverage competitive intelligence. Work with a specialized partner like DataWeave to refine your competitive pricing data using AI and dedicated human-in-the-loop support.

    Want help getting started refining your data fast? Talk to us to get a demo today!

  • How AI Can Drive Superior Data Quality and Coverage in Competitive Insights for Retailers and Brands

    How AI Can Drive Superior Data Quality and Coverage in Competitive Insights for Retailers and Brands

    Managing the endlessly growing competitive data from across your eCommerce landscape can feel like pushing a boulder uphill. The sheer volume can be overwhelming, and ensuring that data meets standards of high accuracy and quality, and the insights are actionable is a constant challenge.

    This article explores the challenges eCommerce companies face in having sustained access to high-quality competitive data and how AI-driven solutions like DataWeave empower brands and retailers with reliable, comprehensive, and timely market intelligence.

    The Data Quality Challenge for Retailers and Brands

    Brands and retailers make innumerable daily business decisions relying on accurate competitive and market data. Pricing changes, catalog expansion, development of new products, and where to go to market are just a few. However, these decisions are only as good as the insights derived from the data. If the data is made up of inaccurate or low-quality inputs, the outputs will also be low-quality.

    Managing eCommerce data at scale gets more complex every year. There are more market entrants, retailers, and copy-cats trying to sell similar or knock-off products. There are millions of SKUs from thousands of retailers in multiple markets. Not only that, the data is constantly changing. Amazon may add a new subcategory definition in an existing space, or Staples might decide to branch out into a new industry like “snack foods for the office”, an established brand might introduce new sizing options in their apparel, or shrinkflation might decrease the size of a product.

    Given this, it is imperative that conventional data collection and validation methods need to be revised. Teams that rely on spreadsheets and manual auditing processes can’t keep up with the scale and speed of change. An algorithm that once could match products easily needs to be updated when trends, categories, or terminology change.

    With SKU proliferation, visually matching product images against the competition becomes impossible. Knowing where to look for comprehensive data becomes impossible with so many new sellers in the market. Luckily, technology has advanced to a place where manual intervention isn’t the main course of action.

    Advanced AI capabilities, like DataWeave’s, tackle these challenges to help gather, categorize, and extract insights that drive impactful business decisions. It performs the millions of actions that your team can’t accomplish with greater accuracy and in near real-time.

    Improving the Accuracy of Product Matching

    Image Matching for Data Quality

    DataWeave’s product matching capabilities rely on an ensemble of text and image-based models with built-in loss functions to determine confidence levels in all insights. These loss functions measure precision and recall. They help in determining how accurate – both in terms of correctness and completeness – the results are so the system can learn and improve over time. The solution’s built-in scoring function provides a confidence metric that brands and retailers can rely on.

    The product matching engine is configurable based on the type of products that we are matching. It uses a “pipelined mode” that first focuses on recall or coverage by maximizing the search space for viable candidates, followed by mechanisms to improve the precision.

    How ‘Embeddings’ Enhance Scoring

    Embeddings are like digital fingerprints. They are dense vector representations that capture the essence of a product in a way that makes it easy to identify similar products. With embeddings, we can codify a more nuanced understanding of the varied relationships between different products. Techniques used to create good embeddings are generic and flexible and work well across product categories. This makes it easier to find similarities across products even with complex terminology, attributes, and semantics.

    These along with advanced scoring mechanisms used across DataWeave’s eCommerce offerings provide the foundation for:

    • Semantic Analysis: Embeddings identify subtle patterns and meanings in text and image data to better align with business contexts.
    • Multimodal Integration: A comprehensive representation of each SKU is created by incorporating embeddings from both text (product descriptions) and images or videos (product visuals)
    • Anomaly Detection: AI models leverage embeddings to identify outliers and inconsistencies to improve the overall score accuracy.
    DataWeave's AI Tech Stack

    Vector Databases for Enhanced Accuracy

    Vector databases play a central role in DataWeave’s AI ecosystem. These databases help with better storage, retrieval, and scoring of embeddings and serve to power real-time applications such as Verification. This process helps pinpoint the closest matches for products, attributes, or categories with the help of similarity algorithms. It can even operate when there is incomplete or noisy data. After identification, the system prioritizes data that exhibits high semantic alignment so that all recommendations are high-quality and relevant.

    Evolution of Embeddings and Scoring: A Multimodal Perspective

    Product listings undergo daily visual and text changes. DataWeave takes a multimodal approach in its AI to ensure that any content shown on a listing is accounted for, including visuals, videos, contextual signals, and text. DataWeave is continually evolving its embedding and scoring models to align with industry advancements and always works within an up-to-date context.

    DataWeave’s AI framework can:

    • Handle Diverse Data Types: The framework captures a holistic view of the digital shelf by integrating insights from multiple sources.
    • Improve Matching Precision: Sophisticated scoring methods refine the accuracy of matches so that brands and retailers can trust the competitive intelligence.
    • Scale Across Markets: Additional, expansive datasets are easy for DataWeave’s capabilities, meaning brands and retailers can scale across markets without pausing.

    Quantified Improvements: Model Accuracy and Stats

    • Since we deployed LLMs and CLIP Embeddings, Product Matching accuracy improved by > 15% from the previous baseline numbers in categories such as Home Improvement, Fashion, and CPG.
    • High precision in certain categories such as Electronics and Fashion. Upwards of 85%.
    • Close to 90% of matches are auto-processed (auto-verified or auto-rejected).
    • Attribute tagging accuracy > 75% and significant improvement for the top 5 categories.

    Business Use Case: Multimodal Matching for Price Leadership

    For example, if you’re a retailer selling consumer electronics, you probably want to maintain your price leadership across your key markets during peak times like Black Friday Cyber Monday. Doing so is a challenge, as all your competitors are changing prices several times a day to steal your sales. To get ahead of them, this retailer could use DataWeave’s multimodal embedding-based scoring framework to:

    • Detect Discrepancies: Isolate SKUs with price mismatches with your competition and take action before revenue is lost.
    • Optimize Coverage: Establish a process to capture complete data across the competition so you can avoid knowledge gaps.
    • Enable Timely Decisions: Address the ‘low-hanging fruit’ by prioritizing products that need pricing adjustments based on confidence scores on high-impact products. Leverage confidence metrics to prioritize pricing adjustments for high-impact products.

    This approach helps retailers stay competitive even as eCommerce evolves around us. By acting fast on complete and reliable data, they can earn and sustain their competitive advantage.

    DataWeave’s AI-Driven Data Quality Framework

    Let’s look at how our AI can gather the most comprehensive data and output the highest-quality insights. Our framework evaluates three critical dimensions:

    • Accuracy: “Is my data correct?” – Ensuring reliable product matches and attribute tracking
    • Coverage: “Do I have the complete picture?” – Maintaining comprehensive market visibility
    • Freshness: “Is my data recent?” – Guaranteeing timely and current market insights
    The 3 pillars to gauge data quality at DataWeave

    Scoring Data Quality

    To maintain the highest levels of data quality, we rely on a robust scoring mechanism across our solutions. Every dataset that is evaluated is done so based on several key parameters. These can include things like accuracy, consistency, timeliness, and completeness of data. Scores are dynamically updated as new data flows in so that insights can be acted upon.

    • Accuracy: Compare gathered data with multiple trusted sources to reduce discrepancies.
    • Consistency: Detect and rectify variations or contradictions across the data with regular audits.
    • Timeliness: Scoring emphasizes data recency, especially for fast-changing markets like eCommerce.
    • Completeness: Ensure all essential data points are included and gaps in coverage are highlighted by analysis.

    Apart from this, we also leverage an evolved quality check framework:

    DataWeave's Data Quality Check framework

    Statistical Process Control

    DataWeave implements a sophisticated system of statistical process control that includes:

    • Anomaly Detection: Using advanced statistical techniques to identify and flag outlier data, particularly for price and stock variations
    • Intelligent Alerting: Automated system for notifying stakeholders of significant deviations
    • Continuous Monitoring: Real-time tracking of data patterns and trends
    • Error Correction: Systematic approach to addressing and rectifying data discrepancies

    Transparent Quality Assurance

    The platform provides complete visibility into data quality through:

    • Comprehensive Data Transparency & Statistics Dashboard: Offering detailed insights into match performance and data freshness
    • Match Distribution Analysis: Tracking both exact and similar matches across retailers and locations as required
    • Product Tracking Metrics: Visibility into the number of products being monitored
    • Autonomous Audit Mechanisms: Giving customers access to cached product pages for transparent, on-demand verification

    Human-in-the-Loop Validation (Véracité)

    DataWeave’s Véracité system combines AI capabilities with human expertise to ensure unmatched accuracy:

    • Expert Validation: Product category specialists who understand industry-specific similarity criteria
    • Continuous Learning: AI models that evolve through ongoing expert feedback
    • Adaptive Matching: Recognition that similarity criteria can vary by category and change over time
    • Detailed Documentation: Comprehensive reasoning for product match decisions

    Together, these elements create a robust framework that delivers accurate, complete, and relevant product data for competitive intelligence. The system’s combination of automated monitoring, statistical validation, and human expertise ensures businesses can make decisions based on reliable, high-quality data.

    In Conclusion

    DataWeave’s AI-driven approach to data quality and coverage empowers retailers and brands to navigate the complexities of eCommerce with confidence. By leveraging advanced techniques such as multimodal embeddings, vector databases, and advanced scoring functions, businesses can ensure accurate, comprehensive, and timely competitive intelligence. These capabilities enable them to optimize pricing, improve product visibility, and stay ahead in an ever-evolving market. As AI continues to refine product matching and data validation processes, brands can rely on DataWeave’s technology to eliminate inefficiencies and drive smarter, more profitable decisions.

    The evolution of AI in competitive intelligence is not just about automation—it’s about precision, scalability, and adaptability. DataWeave’s commitment to high data quality standards, supported by statistical process controls, transparent validation mechanisms, and human-in-the-loop expertise, ensures that insights remain actionable and trustworthy. In a digital landscape where data accuracy directly impacts profitability, investing in AI-powered solutions like DataWeave’s is not just an advantage—it’s a necessity for sustained eCommerce success.

    To learn more, reach out to us today or email us at contact@dataweave.com.

  • Enterprise Data Security at DataWeave: Empowering Smarter Decisions with Seamless, Secure Data Management and Integration

    Enterprise Data Security at DataWeave: Empowering Smarter Decisions with Seamless, Secure Data Management and Integration

    At DataWeave, data security isn’t just about compliance—it’s about enabling peace of mind and better decision-making for our customers. Our customers rely on us not just for competitive and market intelligence but also for the seamless integration of critical data sources into their decision-making frameworks. To achieve this, we have built a security-first infrastructure that ensures organizations can confidently leverage both external and internal data without compromising privacy or protection.

    Secure Data Integration: The Foundation of Smarter Decisions

    Effective decision-making in today’s digital commerce landscape depends on combining multiple data sources—including first-party customer data, pricing intelligence, and business rules—into a unified framework. However, without the right security measures in place, businesses often struggle to operationalize this data effectively.

    At DataWeave, we eliminate this challenge by offering:

    • Integration with Leading Data Storage Solutions: Our platform seamlessly connects with data lakes and warehouses like AWS S3 and Snowflake, ensuring that businesses can easily ingest and analyze our data in real time.
    DataWeave's Data Security Framework
    • Support for Sandboxed Environments & Data Clean Rooms: Organizations can securely merge internal and external datasets without compromising confidentiality, unlocking deeper insights for pricing and business strategies.
    • Automated Data Ingestion & Management: We simplify the process of integrating first-party data alongside competitive insights, allowing customers to focus on execution rather than infrastructure management.

    Our Purpose-Built Security Framework

    Handling millions of data points daily demands a security framework that is not only robust but also scalable and adaptable to evolving threats. DataWeave’s multi-tenant architecture ensures seamless data security without compromising operational efficiency.

    • Multi-Tenant Architecture: Our system allows multiple customers to share the same application infrastructure while maintaining complete data isolation and security.
      • Tenants share infrastructure and computing resources but remain logically isolated.
      • Application-level controls ensure privacy while maximizing cost efficiency.
      • Centralized updates, maintenance, and easy scalability for new tenants.
    • End-to-End Encryption & Access Controls: Every piece of data is encrypted both in transit and at rest. Role-based access controls (RBAC) restrict visibility to only authorized personnel, ensuring minimal risk of unauthorized data access.

    Active Monitoring & Automated Compliance Management: We leverage automated access controls that adjust permissions dynamically as organizational roles evolve, ensuring that compliance is continuously maintained.

    Certifications That Inspire Confidence

    Data security is at the core of everything we do. Our compliance with the highest industry standards ensures that businesses can trust us with their sensitive data.

    SOC 2 Type II Certification: DataWeave’s SOC 2 compliance is a testament to our commitment to stringent security protocols. This certification guarantees that we adhere to strict standards in data protection, availability, and confidentiality.

    We implement a phased approach to security improvement:

    • Prioritizing Critical Systems: To maximize impact, we prioritized systems that had the highest data security relevance and expanded the coverage thereafter. By addressing these priority areas, we were able to make meaningful security improvements early in the process.
    • Automating Monitoring and Compliance: Partnering with Sprinto streamlined the compliance journey by automating key processes. This included real-time monitoring of our cloud environments, automated generation of audit-ready evidence, and integration with critical systems like AWS, Bitbucket, and Jira. These enhancements ensured efficient management of compliance requirements while reducing the burden on our teams.
    SOC 2 Compliance at DataWeave
    • Fostering a Culture of Shared Responsibility: We conducted organization-wide training sessions to embed compliance as a shared responsibility across all teams. By educating employees on the importance of security practices and providing them with the tools to manage compliance autonomously, we established a security-first mindset throughout the company.

    This systematic method allowed us to deliver immediate improvements while aligning long-term practices with industry’s best standards.

    What This Means for Our Customers

    By combining robust security with seamless data integration, DataWeave empowers businesses to:

    • Optimize Price Management & Modelling: With secure access to real-time data, organizations can make informed pricing decisions that enhance profitability and market competitiveness.
    • Run Advanced Simulations & Testing: Reliable, secure data enables businesses to model various pricing and assortment strategies before implementation, reducing risks and maximizing returns.
    • Uncompromised Data Security: SOC 2 Type II compliance ensures stringent protocols to protect your data at every stage.
    • Simplified Vendor Processes: Verified security certifications reduce friction during due diligence and onboarding, making it easier to partner with us.
    • Aligned Standards: Our adherence to industry benchmarks reflects our commitment to meeting your expectations as a trusted technology partner.
    • Scalable Operations: Expand across regions while maintaining full confidence in data privacy and security.
    • Secure Collaboration: Share insights across teams with tools designed to protect sensitive information.

    Our customers are increasingly looking to integrate their internal datasets with the external competitive intelligence provided by DataWeave. This can be a complex and risky process without the right security measures in place. We remove these roadblocks by providing a secure, scalable infrastructure designed to help businesses unify data without security concerns.

    By ensuring seamless compatibility with key data storage platforms, such as Snowflake and AWS S3, we enable organizations to consolidate valuable first-party data with timely market insights. This integration empowers businesses to refine their pricing, assortment, and digital shelf strategies, thereby driving superior customer experiences—without the headaches of data security risks.

    Security remains a top priority in everything we do. Our SOC 2 Type II-certified framework enforces rigorous encryption, access controls, and real-time compliance monitoring. We take on the burden of data security so our customers can focus on innovation and growth.

    With DataWeave, businesses can confidently leverage secure data-driven decision-making to unlock new opportunities, optimize operations, and scale without compromise.

    To learn more, write to us at contact@dataweave.com or request a consultation here.

  • Black Friday 2024 in Canada: Insights on Consumer Electronics and Home & Furniture

    Black Friday 2024 in Canada: Insights on Consumer Electronics and Home & Furniture

    Black Friday and Cyber Monday are major retail events in Canada, with 43% and 29% of the population making purchases during these sales respectively, according to a YouGov report. Consumer electronics continue to lead the Canadian retail market during these events, with 55% of surveyed shoppers choosing to buy tech products on Black Friday. Household appliances come in second, with 25% of shoppers opting for these items, while 18% prefer to shop for furniture deals.

    These statistics highlight the importance of delivering value during the Thanksgiving sales week. Retailers must cater to shoppers’ expectations with competitive pricing, attractive deals, and a seamless shopping experience. So, what unique offerings did Canadian retailers present to shoppers this season?

    To understand the pricing and discount dynamics during BFCM 2024 in Canada, DataWeave analyzed discounts across leading consumer electronics and home & furniture retailers. Using our AI-powered pricing intelligence platform, we analyzed 37,108 SKUs across these categories for major retailers including Amazon, Walmart, Best Buy, Home Depot, and Canadian Tire from the 10th to 29th November. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “sofa” and “wearables”.

    In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Also check out our detailed analysis of discounts and pricing for the consumer electronics, apparel, health & beauty, grocery, and home & furniture categories across major US retailers this Black Friday.

    Consumer Electronics

    Retailers in Focus

    Consumer electronics saw robust participation from major retailers, with Amazon, Best Buy, and Walmart leading the charge. Here’s how they stacked up in terms of discounts:

    Pricing Trends Across Leading Consumer Electronics Retailers in Canada - Black Friday Cyber Monday 2024
    • Best Buy emerged as the frontrunner in absolute discounts at 31.2%, while Amazon impressed with a notable 19.7% additional discount, indicating a strong Black Friday-specific markdown strategy.
    • Walmart offered steady competition, particularly in audio and video products, which reached an average absolute discount of 37.2%. However, it’s average additional discount was only 3.1%, indicating muted BFCM-specific price reductions in this category.

    Subcategory Insights

    Diving deeper into consumer electronics subcategories, we observed varied discounting strategies.

    Pricing Trends Across Leading Canadian Consumer Electronics Retailer Subcategories - Black Friday Cyber Monday 2024
    • Audio & Video stood out as the most discounted subcategory, with Walmart leading at 37.2%.
    • In Wearables, Walmart again took the top spot with 36.4%, while Amazon offered higher additional discounts (22.4%).
    • Discounting for computers and gaming was less aggressive, highlighting strategic pricing to maintain profitability in these high-demand segments.

    Brand Performance

    Brand-level data highlighted how key players used Black Friday to drive visibility and sales.

    Pricing Trends Across Leading Canadian Consumer Electronics Brands - Black Friday Cyber Monday 2024
    • Dell led in average absolute discounts (36.7%) followed by Samsung at 36.68%
    • Audio brand JBL offered significant absolute discounts at 35.9%.
    • Apple and Lenovo offered comparatively fewer discounts but maintained strong visibility, as seen in their increase in the Share of Search during the sale period.
    Visibility Trends Across Leading Canadian Consumer Electronics Brands - Share of Search - Black Friday Cyber Monday 2024
    • MSI (laptop brand) and Bose (audio and earphone brand) experienced significant increases in visibility, with Share of Search increases of 5% and 3.6%, respectively.
    • Notably, HP faced a decline (-3.2%) in the Share of Search, suggesting missed opportunities to align promotions with consumer interest.

    Home & Furniture

    Retailers in Focus

    The home and furniture category saw competitive discounting, with Walmart, Canadian Tire, and Home Depot vying for consumer attention.

    Black Friday - Cyber Monday Trends Across Leading Canadian Home & Furniture Retailers
    • Walmart took the lead with the highest absolute discounts at 36.8%. The retailer’s additional discounts were more conservative at 3.6%. This is similar to their discount levels in Consumer Electronics.
    • Canadian Tire offered stiff competition, providing 31.6% absolute discounts and 25% additional discounts.
    • Home Depot matched its absolute and additional discounts, maintaining consistency at 24.1%.

    Subcategory Insights

    Home and furniture subcategories revealed targeted discount strategies.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Subcategories - Canada
    • Bedding emerged as the most discounted subcategory at Walmart (50.6%) and Canadian Tire (35.3%).
    • Kitchenware saw competitive pricing, with Walmart leading at 42.9%, followed by Canadian Tire at 33.9%.
    • Canadian Tire focused on lighting, offering the highest absolute discounts in this subcategory (38.2%)

    Brand Performance

    Brand-level analysis revealed stark contrasts in discounting approaches.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Brands - Canada
    • Furniture brands Homcom led in absolute discounts (36.4%), while South Shore stood out with the highest additional discounts (30.2%).
    • Value-oriented brands like furnishings brand Mainstays and mattress and bedding brand Zinus offered more modest discounts, focusing on consistent affordability.
    Black Friday - Cyber Monday Trends Across Leading Canadian Home & Furniture Brands - Share of Search and Visibility
    • Zinus (mattresses and sofa brand) experienced a significant 7.9% increase in the Share of Search, driven by aggressive promotions.
    • Home furnishings brands like Costway and Safavieh faced declines, reflecting the importance of aligning promotional strategies with consumer expectations.

    Insights for Retailers and Brands

    This Black Friday, Canadian retailers effectively balanced deep discounts with category-specific strategies to maximize sales. However, the fluctuating Share of Search highlights the critical need for brands to align promotions with consumer interest.

    For brands and retailers looking to stay ahead of the competition, DataWeave’s pricing intelligence platform offers unparalleled insights to refine discounting strategies and boost visibility. Contact us to learn how we can help you stay competitive in this dynamic retail landscape.

  • A Deep Dive into Consumer Electronics Pricing During Black Friday 2024

    A Deep Dive into Consumer Electronics Pricing During Black Friday 2024

    Americans spent a whopping total of $10.8 billion online this Black Friday. As Thanksgiving Week 2024 wraps up, one thing is clear: the consumer electronics category continues to dominate seasonal shopping trends. Fueled by a blend of enticing deals and high consumer demand, the sector delivered competitive discounts across subcategories like wearables, gaming, and mobile devices.

    At DataWeave, we analyzed discounting trends in the U.S. consumer electronics market during this year’s sales events. Using our AI-powered pricing intelligence platform, we tracked pricing and promotions for 22383 SKUs across Amazon, Walmart, Target, and Best Buy from November 10 to 29. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “gaming” and “apple.” Here’s what we uncovered.

    Also check out our insights on discounts and pricing for health & beauty, grocery, apparel, and home & furniture categories this Black Friday.

    Retailers Battle It Out with Competitive Discounts

    Discount trends reveal clear leaders in terms of markdowns:

    • Walmart offered the deepest average absolute discounts at 36.9%.
    • Amazon and Target followed closely, highlighting a diverse range of deals designed to appeal to budget-conscious shoppers
    • Best Buy, the specialist consumer electronics retailer, offers the lowest discounts this Black Friday at 26.2%.
    Pricing Trends Across Leading Consumer Electronics Retailers - Black Friday Cyber Monday 2024

    Note: The Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Subcategory Spotlight: Where the Best Deals Happened

    From audio & video to wearables, each retailer carved out competitive advantages across subcategories.

    Pricing Trends Across Leading Consumer Electronics Retailer Subcategories - Black Friday Cyber Monday 2024
    • Both Amazon and Walmart offered high discounts in audio & video and wearables, but Walmart led, with discounts up to 46.3%.
    • Best Buy, meanwhile, offered high absolute discounts on Mobile Devices(34%) and Storage (31%), followed by high discounts on wearables and Audio & Video.
    • Amazon maintained a balanced approach, excelling in audio & video and mobile devices.

    Brand-Level Insights: HP and Samsung Dominate

    The biggest winners this year were brands that strategically leveraged Black Friday discounts to boost visibility and sales:

    • HP took the top spot with average discounts of 36.9%, followed by Samsung at 31.4%.
    • Despite its premium reputation, Apple offered an average discount of 29.3%, signaling a shift in strategy to attract deal hunters.
    Pricing Trends Across Leading Consumer Electronics Brands - Black Friday Cyber Monday 2024

    Share of Search: Shifting Consumer Attention

    Search trends reveal how discounts shaped brand visibility:

    • Microsoft saw the largest spike in share of search (+8.6%), thanks to aggressive pricing on gaming consoles and accessories.
    • Marshall and Amazon also saw significant gains in visibility.
    • Surprisingly, HP experienced a sharp decline (-9.8%), indicating missed opportunities despite steep discounts.
    Visibility Trends Across Leading Consumer Electronics Brands - Share of Search - Black Friday Cyber Monday 2024

    Consumer Electronics: Lowest-Priced Retailer Analysis

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 340 matched products across retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Category-Level Highlights

    Retailers Offering Most Value - Lowest Priced - Consumer Electronics - Black Friday 2024
    • Amazon leads with the highest average discount (41.35%), offering the most value to consumers. It is followed by Target (39.37%) and Walmart (36.15%).
    • Best Buy, the specialist consumer electronics retailer, ranks last with an average discount of 31.53%, emphasizing a less aggressive pricing strategy compared to competitors.

    Subcategory Highlights

    Lowest Priced Retailer Across Major Subcategories- Consumer Electronics - Black Friday 2024
    • Wearables: Amazon offers the steepest discounts (55.40%), followed by Best Buy (50.60%) and Walmart (45.75%).
    • Mobile Devices: Amazon also leads (37.94%), with Walmart (29.30%) in second place and Target trailing at 19.48%.
    • Gaming: Target takes the lead (37.47%), with Amazon and Best Buy offering similar discounts around 30%.
    • Computers: Target again emerges as the leader (39.18%), narrowly surpassing Walmart (36.13%).

    Brand Highlights

    Lowest Priced Retailer Across Leading Brands- Consumer Electronics - Black Friday 2024
    • Apple: Amazon dominates with 53.06%, closely followed by Walmart (50.55%), while Target and Best Buy hover around 43%.
    • Nintendo: Target edges out Amazon (37.62% vs. 36.54%), with Best Buy (33.21%) and Walmart (25.92%) trailing.
    • Beats by Dr. Dre: Amazon leads (46.07%), with Target (37.14%) as the runner-up. Best Buy and Walmart offer comparatively modest discounts around 25%.
    • Bose: Walmart emerges as the value leader (23.90%), surpassing Target (16.09%) and Best Buy (15.29%).
    • Cricut: Amazon sets a high benchmark (54.13%), with Target far behind (36.43%) for this viral portable printer brand. Best Buy (12.32%) and Walmart (10.79%) offer significantly lower discounts.

    What This Means for Retailers and Brands

    Retailers looking to stay competitive should focus on strategic discounting and enhanced brand visibility. Brands must align with consumer expectations by:

    • Leveraging platforms like DataWeave to analyze discount trends.
    • Optimizing pricing and assortment strategies for seasonal demand.

    For more insights into consumer electronics pricing, contact DataWeave to discover how our AI-powered solutions can drive success in today’s fast-paced market. Stay tuned for more category-specific analyses in the coming weeks!

  • Breaking Down Grocery Discounts This Black Friday

    Breaking Down Grocery Discounts This Black Friday

    As shoppers flocked online and to stores during Black Friday and Cyber Monday, the grocery category stood out as a key battleground for retailers. With inflation affecting consumer spending, discounted groceries have become a critical driver for both shopper savings and retailer competitiveness.

    In fact, according to the NRF, one of the top shopping destinations during Thanksgiving weekend were department stores (42%), online (42%),and grocery stores and supermarkets (40%). Clearly, consumers are looking to stock up in bulk on their groceries to maximize their savings.

    To understand the pricing dynamics in the grocery category, DataWeave analyzed grocery discounts across leading grocers, uncovering significant trends that shaped consumer choices during this holiday shopping period.

    Our research encompassed retailers like Amazon, Target, and Walmart, examining their discounting strategies across subcategories, alongside trends in share of search for leading CPG companies.

    Also check out our detailed analysis of discounts and pricing for health & beauty and home & furniture this Black Friday.

    Key Grocery Market Stats for Black Friday-Cyber Monday 2024

    • Retailer Discounts: Walmart offered the highest average absolute discount at 27.6%, followed by Amazon at 20.4% and Target at 14.0%
    • Subcategory Insights: Beverages Category at Walmart saw the deepest discounts, with an average of 33.4%
    • Top Gaining Brands: Cesar experienced the largest increase in share of search during the sales period (+3.89%)

    This blog will dive deeper into grocery discount trends and brand-level strategies, offering insights for retailers looking to stay competitive in the grocery sector.

    Our Methodology

    For this analysis, we tracked the average discounts offered by major U.S. grocery retailers during the Thanksgiving weekend, including Black Friday and Cyber Monday. We focused on key subcategories within the grocery segment, capturing trends in discounting strategies.

    • Sample Size: 18,324 SKUs
    • Retailers Tracked: Amazon, Walmart, Target
    • Subcategories Reported On: Fresh Produce, Dairy & Eggs, Pantry Essentials, Snacks, Frozen Foods, Meat & Seafood, Household Essentials, Beverages, Pet Products, Baby Products
    • Timeline of Analysis: November 10 to 29, 2024

    In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Key Findings

    Retailer-Level Insights

    Average Discounts Across Leading Grocery Retailers - Black Friday Cyber Monday 2024
    • Walmart emerged as the leader in grocery discounting, offering the highest average absolute (27.6%) and additional (18%) discounts.
    • Amazon adopted a mid-tier discounting strategy, with average absolute discounts of 20.4%.
    • Target, while more conservative, maintained competitiveness in select subcategories like baby products.

    Subcategory Insights

    Average Discounts Across Leading Grocery Retailer Subcategories - Black Friday Cyber Monday 2024
    • Pantry Essentials saw Walmart leading with an average discount of 31.2%, appealing to budget-conscious consumers stocking up for the holidays.
    • Fresh Produce showed consistent discounting across retailers, with Amazon slightly ahead at 27%.
    • Beverages stood out for significant discounting at Walmart, with an impressive 33.4% average discount.

    Brand-Level Insights

    Average Discounts Across Leading Grocery Brands - Black Friday Cyber Monday 2024
    • Lay’s led in absolute discounts (37.52%) and additional discounts (26.23%) showcasing aggressive pricing in the snacks subcategory.
    • Good & Gather maintained its competitive edge with strong discounts, appealing to price-conscious consumers seeking value.
    • Brands like Blue Buffalo (pet food brand) offered significant absolute discounts, but with a low additional discount of just 2%, the overall impact of the sale event on effective value was limited.

    Share of Search Insights

    Gains and Losses in Share of Search Across Leading Grocery Brands - Black Friday Cyber Monday 2024
    • Cesar (dog food brand), Tide (laundry staple) and Doritos saw significant gains in share of search, reflecting successful promotional strategies.
    • Brands like Pampers (baby diapers brand), Healthy Choice, (frozen foods brand) and Pedigree (pet food brand) experienced a decline, indicating less effective engagement during the sale period.

    Who offered the lowest prices?

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 1433 matched products across retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Category-Level Analysis

    Retailers Offering Most Value - Lowest Priced - Grocery - Black Friday 2024
    • Walmart is the lowest priced retailer overall for the grocery category, with an impressive average discount of 44.60%. This significant discount advantage makes Walmart a leading option for value-seeking consumers.
    • Target follows with strong discounts of 36.73%, indicating solid pricing in comparison but less aggressive than Walmart.
    • Interestingly, Amazon was the most expensive in Grocery, with an average discount of only 6.3%.

    Subcategory-Level Analysis

    Lowest Priced Retailer Across Major Subcategories- Grocery - Black Friday 2024
    • Walmart leads in various subcategories such as Pet Products (21.12%), Dairy & Eggs (13.79%), Household Essentials (13.05%), Frozen Foods (15.07%), and Meat & Seafood (17.60%), showcasing its extensive value across the board.
    • Target excels in Beverages (14.58%) and Baby Products (15.00%) with competitive discounts, standing out in these specific subcategories.
    • Kroger provides notable value in Pantry Essentials (20.04%) and Fresh Produce (15.85%), although its overall average discount is lower than Walmart’s.
    • Amazon consistently ranks lower in terms of average discounts across most subcategories, highlighting it as less competitive for consumers seeking the lowest prices.

    Brand-Level Analysis

    Lowest Priced Retailer Across Leading Brands- Grocery - Black Friday 2024
    • Walmart also holds the top position for several key brands like Cheetos (14.92%) and Dannon (8.81%), making it the best option for consumers looking for budget-friendly choices across popular brands.
    • Target takes the lead for brands like Betty Crocker (25.20%) and Chobani (11.37%), showing that it can offer value for specific products.
    • Kroger maintains strong discounts for brands such as Delmonte (9.19%), but it does not outpace Walmart in the overall grocery brand comparison.
    • Amazon generally lags behind in average discounts for most brands, with Dannon (1.12%) and Chobani (2.43%) showing significantly lower discounts.

    Walmart is the lowest priced retailer in the grocery category and provides substantial value across a wide range of subcategories and popular brands. This ties in with Walmart’s ELDP pricing strategy. The retailer leads in overall average discounts and maintains its position as the go-to for price-conscious consumers. Target offers strong value in certain subcategories and brands but falls short of Walmart’s broad value based pricing advantages.

    What’s Next

    For grocery retailers, competitive pricing and targeted promotions are critical to driving sales during key shopping events. As consumers continue to prioritize value, staying ahead in the discounting game can significantly impact market share.

    For detailed insights into grocery discounting strategies and to explore how DataWeave’s solutions can help retailers optimize their pricing, contact us today!

    Stay tuned to our blog for further analyses of other categories during Black Friday and Cyber Monday.

  • Black Friday 2024: Home & Furniture Pricing Trends Analyzed

    Black Friday 2024: Home & Furniture Pricing Trends Analyzed

    The Home & Furniture category continues to thrive, propelled by consumer interest in creating personalized and functional living spaces. In 2023, the U.S. furniture and home furnishings market was valued at approximately $641.7 billion in 2023 and is estimated to grow at a CAGR of 5.1% from 2024 to 2032. Black Friday and Cyber Monday play a crucial role in fueling this growth, offering consumers a mix of premium and affordable options across subcategories.

    To better understand market trends and discount strategies this Black Friday, at DataWeave we tracked over 18,149 SKUs across major home & furniture retailers, including Amazon, Walmart, Target, Best Buy, Home Depot, and Overstock, from November 10 to 29, 2024. Using our AI-powered pricing intelligence platform, we focused on the top 500 products in subcategories like kitchenware, furniture, decor, lighting, outdoor items, and bedding.

    In our analysis, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the Black Friday sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Also check out our insights on discounts and pricing for the health & beauty category this Black Friday.

    Retailer Performance: Who Led the Discount Race?

    Retailers showed varying discount strategies for Home & Furniture products. Walmart emerged as the leader in absolute discounts (37.5%) while Amazon offered the highest additional discount of 14%. Best Buy maintained competitive pricing across all subcategories, while Overstock and Home Depot offered relatively modest discounts.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Retailers

    Subcategories in Focus

    Breaking down the discounts by subcategory provides deeper insights into consumer priorities and retailer strategies:

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Subcategories
    • Kitchenware saw strong competition, with Walmart (30.40% absolute discounts) and Amazon (29% absolute discounts) dominating.
    • Lighting became a discount hotspot, with Walmart offering up to 45.8% in absolute discounts and 25.3% additional markdowns.
    • Furniture remained a core focus for Target, delivering an impressive 34% average absolute discount.
    • Bedding stood out at Walmart, where discounts peaked at 49.6%.

    Brand Spotlight: Who Stood Out?

    Among top-performing brands, furniture brand Costway offered the highest discounts, with an average of 48.4%. Meanwhile, Adesso (lighting solutions), Mainstays and Safavieh (both home furnishings brands) balanced discounts and premium appeal.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Brands

    Search Visibility: The Winners and Losers

    Share of search dynamics revealed significant shifts in brand visibility during Black Friday:

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Brands - Share of Search and Visbility
    • Furniture brand Costway (+1.2%) and home improvement player Black+Decker (+1.5%) gained visibility.
    • On the flip side, premium brands like Safavieh known for rugs and home furnishings (-16.8%) and furniture brand Burrow ( -1.7%) saw declines.

    Who Offers the Lowest Prices?

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 735 matched products across Home & Furniture specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Category-Level Highlights

    Retailers Offering Most Value - Lowest Priced - Home & Furniture - Black Friday 2024
    • Amazon emerges as the lowest-priced retailer across Home & Furniture categories, with the highest average discount of 27.50%, closely followed by Walmart (26.09%).
    • Overstock and Wayfair trail with average discounts of 22.93% and 20.71%, respectively, while Home Depot offers the least aggressive pricing at 18.14%. This is notable, as all 3 players are known specialists in the category.

    Subcategory Highlights

    Lowest Priced Retailer Across Major Subcategories- Home & Furniture - Black Friday 2024
    • Amazon stands out as the leader in multiple subcategories, including Appliances, Furniture, Decor, and Outdoor, offering competitive average discounts of around 26-29%.
    • Overstock leads in Bedding and Kitchenware, with strong average discounts of 24.26% and 20.72%, respectively.
    • Wayfair is notable for Lighting, with an average discount of 19.95%, and is also competitive in Outdoor and Furniture categories.
    • Walmart consistently ranks high in several subcategories like Appliances and Bedding, providing solid discounts of around 22-23%.

    What’s Next

    For home & furniture retailers, driving maximum value during mega sale events like Black Friday involves offering bundles and sets to meet customer demands and trend expectations. Gaining insights into competitor discounts and pricing can help furniture retailers get an edge amid this environment.

    Want to know how DataWeave’s intelligence platform can empower your business during peak sales events? Contact us to discover more about competitive insights, price intelligence, and data-driven decision-making.
    Stay tuned to our blog to see more coverage on Black Friday 2024.

  • Redefining Product Attribute Tagging With AI-Powered Retail Domain Language Models

    Redefining Product Attribute Tagging With AI-Powered Retail Domain Language Models

    In online retail, success hinges on more than just offering quality products at competitive prices. As eCommerce catalogs expand and consumer expectations soar, businesses face an increasingly complex challenge: How do you effectively organize, categorize, and present your vast product assortments in a way that enhances discoverability and drives sales?

    Having complete and correct product catalog data is key. Effective product attribute tagging—a crucial yet frequently undervalued capability—helps in achieving this accuracy and completeness in product catalog data. While traditional methods of tagging product attributes have long struggled with issues of scalability, consistency, accuracy, and speed, a new thinking and fundamentally new ways of addressing these challenges are getting established. These follow from the revolution brought in Large Language Models but they fashion themselves as Small Language Models (SLM) or more precisely as Domain Specific Language Models. These can be potentially considered foundational models as they solve a wide variety of downstream tasks albeit within specific domains. They are a lot more efficient and do a much better job in those tasks compared to an LLM. .

    Retail Domain Language Models (RLMs) have the potential to transform the eCommerce customer journey. As always, it’s never a binary choice. In fact, LLMs can be a great starting point since they provide an enhanced semantic understanding of the world at large: they can be used to mine structured information (e.g., product attributes and values) out of unstructured data (e.g., product descriptions), create baseline domain knowledge (e.g, manufacturer-brand mappings), augment information (e.g., image to prompt), and create first cut training datasets.

    Powered by cutting-edge Generative AI and RLMs, next-generation attribute tagging solutions are transforming how online retailers manage their product catalog data, optimize their assortment, and deliver superior shopping experiences. As a new paradigm in search emerges – based more on intent and outcome, powered by natural language queries and GenAI based Search Agents – the capability to create complete catalog information and rich semantics becomes increasingly critical.

    In this post, we’ll explore the crucial role of attribute tagging in eCommerce, delve into the limitations of conventional tagging methods, and unveil how DataWeave’s innovative AI-driven approach is helping businesses stay ahead in the competitive digital marketplace.

    Why Product Attribute Tagging is Important in eCommerce

    As the eCommerce landscape continues to evolve, the importance of attribute tagging will only grow, making it a pertinent focus for forward-thinking online retailers. By investing in robust attribute tagging systems, businesses can gain a competitive edge through improved product comparisons, more accurate matching, understanding intent, and enhanced customer search experiences.

    Taxonomy Comparison and Assortment Gap Analysis

    Products are categorized and organized differently on different retail websites. Comparing taxonomies helps in understanding focus categories and potential gaps in assortment breadth in relation to one’s competitors: missing product categories, sizes, variants or brands. It also gives insights into the navigation patterns and information architecture of one’s competitors. This can help in making search and navigation experience more efficient by fine tuning product descriptions to include more attributes and/or adding additional relevant filters to category listing pages.

    For instance, check out the different Backpack categories on Amazon and Staples in the images below.

    Product Names and Category Names Differ on Different eCommerce Platforms - Here's an Amazon Example
    Product Names and Category Names Differ on Different eCommerce Platforms - Here's a Staples Example

    Or look at the nomenclature of categories for “Pens” on Amazon (left side of the image) and Staples (right side of the image) in the image below.

    Product Names and Category Names Differ on Different eCommerce Platforms -Here's how Staples Vs. Amazon Categories look for Pens

    Assortment Depth Analysis

    Another big challenge in eCommerce is the lack of standardization in retailer taxonomy. This inconsistency makes it difficult to compare the depth of product assortments across different platforms effectively. For instance, to categorize smartphones,

    • Retailer A might organize it under “Electronics > Mobile Phones > Smartphones”
    • Retailer B could use “Technology > Phones & Accessories > Cell Phones”
    • Retailer C might opt for “Consumer Electronics > Smartphones & Tablets”

    Inconsistent nomenclature and grouping create a significant hurdle for businesses trying to gain a competitive edge through assortment analysis. The challenge is exacerbated if you want to do an in-depth assortment depth analysis for one or more product attributes. For instance, look at the image below to get an idea of the several attribute variations for “Desks” on Amazon and Staples.

    With Multiple Attributes Named in a Variety of Ways, Attribute Tagging is Essential to Ensure Accurate Product Matching

    Custom categorization through attribute tagging is essential for conducting granular assortment comparisons, allowing companies to accurately assess their product offerings against those of competitors.

    Enhancing Product Matching Capabilities

    Accurate product matching across different websites is fundamental for competitive pricing intelligence, especially when matching similar and substitute products. Attribute tagging and extraction play a crucial role in this process by narrowing down potential matches more effectively, enabling matching for both exact and similar products, and tagging attributes such as brand, model, color, size, and technical specifications.

    For instance, when choosing to match similar products in the Sofa category for 2-3 seater sofas from Wayfair and Overstock, tagging attributes like brand, color, size, and more is a must for accurate comparisons.

    Attribute Tagging for Home & Furniture Categories Like Sofas Helps Improve Matching Accuracy
    Attribute Tagging for Home & Furniture Categories Like Sofas Helps Improve Matching Accuracy

    Taking a granular approach not only improves pricing strategies but also helps identify gaps in product offerings and opportunities for expansion.

    Fix Content Gaps and improve Product Detail Page (PDP) Content

    Attribute tagging plays a vital role in enhancing PDP content by ensuring adherence to brand integrity standards and content compliance guidelines across retail platforms. Tagging attributes allows for benchmarking against competitor content, identifying catalog gaps, and enriching listings with precise details.

    This strategic tagging process can highlight missing or incomplete information, enabling targeted optimizations or even complete rewrites of PDP content to improve discoverability and drive conversions. With accurate attribute tagging, businesses can ensure each product page is fully optimized to capture consumer attention and meet retail standards.

    Elevating the Search Experience

    In today’s online retail marketplace, a superior search experience can be the difference between a sale and a lost customer. Through in-depth attribute tagging, vendors can enable more accurate filtering to improve search result relevance and facilitate easier product discovery for consumers.

    By integrating rich product attributes extracted by AI into an in-house search platform, retailers can empower customers with refined and user-friendly search functionality. Enhanced search capabilities not only boost customer satisfaction but also increase the likelihood of conversions by helping shoppers find exactly what they’re looking for more quickly and with minimal effort.

    Pitfalls of Conventional Product Tagging Methods

    Traditional methods of attribute tagging, such as manual and rule-based systems, have been significantly enhanced by the advent of machine learning. While these approaches may have sufficed in the past, they are increasingly proving inadequate in the face of today’s dynamic and expansive online marketplaces.

    Scalability

    As eCommerce catalogs expand to include thousands or even millions of products, the limitations of machine learning and rule-based tagging become glaringly apparent. As new product categories emerge, these systems struggle to keep pace, often requiring extensive revisions to existing tagging structures.

    Inconsistencies and Errors

    Not only is reliance on an entirely human-driven tagging process expensive, but it also introduces a significant margin for error. While machine learning can automate the tagging process, it’s not without its limitations. Errors can occur, particularly when dealing with large and diverse product catalogs.

    As inventories grow more complex to handle diverse product ranges, the likelihood of conflicting or erroneous rules increases. These inconsistencies can result in poor search functionality, inaccurate product matching, and ultimately, a frustrating experience for customers, drawing away the benefits of tagging in the first place.

    Speed

    When product information changes or new attributes need to be added, manually updating tags across a large catalog is a time-consuming process. Slow tagging processes make it difficult for businesses to quickly adapt to emerging market trends causing significant delays in listing new products, potentially missing crucial market opportunities.

    How DataWeave’s Advanced AI Capabilities Revolutionize Product Tagging

    Advanced solutions leveraging RLMs and Generative AI offer promising alternatives capable of overcoming these challenges and unlocking new levels of efficiency and accuracy in product tagging.

    DataWeave automates product tagging to address many of the pitfalls of other conventional methods. We offer a powerful suite of capabilities that empower businesses to take their product tagging to new heights of accuracy and scalability with our unparalleled expertise.

    Our sophisticated AI system brings an advanced level of intelligence to the tagging process.

    RLMs for Enhanced Semantic Understanding

    Semantic Understanding of Product Descriptions

    RLMs analyze the meaning and context of product descriptions rather than relying on keyword matching.
    Example: “Smartphone with a 6.5-inch display” and “Phone with a 6.5-inch screen” are semantically similar, though phrased differently.

    Attribute Extraction

    RLMs can identify important product attributes (e.g., brand, size, color, model) even from noisy or unstructured data.
    Example: Extracting “Apple” as a brand, “128GB” as storage, and “Pink” as the color from a mixed description.

    Identifying Implicit Relationships

    RLMs find implicit relationships between products that traditional rule-based systems miss.
    Example: Recognizing that “iPhone 12 Pro” and “Apple iPhone 12” are part of the same product family.

    Synonym Recognition in Product Descriptions

    Synonym Matching with Context

    RLMs identify when different words or phrases describe the same product.
    Examples: “Sneakers” = “Running Shoes”, “Memory” = “RAM” (in electronics)
    Even subtle differences in wording, like “rose gold” vs “pink” are interpreted correctly.

    Overcoming Brand-Specific Terminology

    Some brands use their own terminologies (e.g., “Retina Display” for Apple).
    RLMs can map proprietary terms to more generic ones (e.g., Retina Display = High-Resolution Display).

    Dealing with Ambiguities

    RLMs analyze surrounding text to resolve ambiguities in product descriptions.
    Example: Resolving “charger” to mean a “phone charger” when matched with mobile phones.

    Contextual Understanding for Improved Accuracy and Precision

    By leveraging advanced natural language processing (NLP), DataWeave’s AI can process and understand the context of lengthy product descriptions and customer reviews, minimizing errors that often arise at human touch points. The solution processes and interprets information to extract key information to dramatically improve the overall accuracy of product tags.

    It excels at grasping the subtle differences between similar products, sizes, colors and identifying and tagging minute differences between items, ensuring that each product is uniquely and accurately represented in a retailer’s catalog.

    This has a major impact on product and similarity-based matching that can even help optimize similar and substitute product matching to enhance consumer search. At the same time, our AI can understand that the same term might have different meanings in various product categories, adapting its tagging approach based on the specific context of each item.

    This deep comprehension ensures that even nuanced product attributes are accurately captured and tagged for easy discoverability by consumers.

    Case Study: Niche Jewelry Attributes

    DataWeave’s advanced AI can assist in labeling the subtle attributes of jewelry by analyzing product images and generating prompts to describe the image. In this example, our AI identifies the unique shapes and materials of each item in the prompts.

    The RLM can then extract key attributes from the prompt to generate tags. This assists in accurate product matching for searches as well as enhanced product recommendations based on similarities.

    DataWeave's AI assists in extracting contextual attributes for accuracy in product matching

    This multi-model approach provides the flexibility to adapt as product catalogs expand while remaining consistent with tagging to yield more robust results for consumers.

    Unparalleled Scalability

    DataWeave can rapidly scale tagging for new categories. The solution is built to handle the demands of even the largest eCommerce catalogs enabling:

    • Effortless management of extensive product catalogs: We can process and tag millions of products without compromising on speed or accuracy, allowing businesses to scale without limitations.
    • Automated bulk tagging: New product lines or entire categories can be tagged automatically, significantly reducing the time and resources required for catalog expansion.

    Normalizing Size and Color in Fashion

    Style, color, and size are the core attributes in the fashion and apparel categories. Style attributes, which include design, appearance, and overall aesthetics, can be highly specific to individual product categories.

    Normalizing Size and Color in Fashion for Product Matching

    Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification. By leveraging advanced technology to identify and normalize identical and similar products from competitors, you can optimize your pricing strategy and product assortment to remain competitive. Using Generative AI in normalizing color and size in fashion is key to powering competitive pricing intelligence at DataWeave.

    Continuous Adaptation and Learning

    Our solution evolves with your business, improving continuously through feedback and customization for retailers’ specific product categories. The system can be fine-tuned to understand and apply specialized tagging for niche or industry-specific product categories. This ensures that tags remain relevant and accurate across diverse catalogs and as trends emerge.

    The AI in our platform also continuously learns from user interactions and feedback, refining its tagging algorithms to improve accuracy over time.

    Stay Ahead of the Competition With Accurate Attribute Tagging

    In the current landscape, the ability to accurately and consistently tag product attributes is no longer a luxury—it’s essential for staying competitive. With advancements in Generative AI, companies like DataWeave are revolutionizing the way product tagging is handled, ensuring that every item in a retailer’s catalog is presented with precision and depth. As shoppers demand a more intuitive, seamless experience, next-generation tagging solutions are empowering businesses to meet these expectations head-on.

    DataWeave’s innovative approach to attribute tagging is more than just a technical improvement; it’s a strategic advantage in an increasingly competitive market. By leveraging AI to scale and automate tagging processes, online retailers can keep pace with expansive product assortments, manage content more effectively, and adapt swiftly to changes in consumer behavior. In doing so, they can maintain a competitive edge.

    To learn more, talk to us today!

  • Mastering Grocery Pricing Intelligence: A Strategic Approach for Modern Retailers

    Mastering Grocery Pricing Intelligence: A Strategic Approach for Modern Retailers

    When egg prices surged 70% during the 2023 avian flu outbreak, grocery retailers faced a critical dilemma: maintain margins and risk losing customers, or absorb costs and watch profits evaporate. Similarly, rising olive oil and chocolate prices also had domino effects, cascading down from retailers to consumers. In each of these scenarios, those with sophisticated pricing intelligence systems adapted swiftly, finding the sweet spot between competitiveness and profitability. Others weren’t so fortunate.

    This scenario continues to play out daily across thousands of products in the grocery sector. From breakfast cereals to fresh produce to bottled water, retailers must orchestrate pricing across a variety of categories – each with its own competitive dynamics, margin requirements, and price sensitivity patterns.

    The Evolution of Grocery Pricing Intelligence

    Imagine these scenarios in the grocery industry:

    • Milk prices spike during a supply shortage.
    • Your competitor drops egg prices by 20%.
    • Fresh produce costs fluctuate with an unseasonable frost.

    For grocery retailers, these aren’t occasional challenges—they’re Tuesday. Reacting to each pricing crisis as it comes isn’t just exhausting—it’s a recipe for shrinking margins and missed opportunities.

    Think of it this way: If you’re constantly playing defense with your pricing strategy, you’re already two steps behind. Commoditized items like milk and eggs face intense price competition, while seasonal products and fresh produce demand constant attention. Simply matching competitor prices or adjusting for cost changes isn’t enough anymore. What’s needed is a proactive approach that anticipates market shifts before they happen and turns pricing challenges into competitive advantages. This is where price management comes in.

    Price management has transformed from simple competitor checks into a strategic power play that can make or break a retailer’s market position. Weekly manual adjustments have given way to a long-term strategic view, driven by data analytics and market intelligence. Here are the basics of how price management in grocery retail works today.

    Three Pillars of Grocery Price Management

    1. Smart Data Collection: Building Your Foundation

    The journey begins with comprehensive data collection and storage across your entire product ecosystem. This means:

    • Complete Coverage Of All SKUs Across All Stores: Tracking prices for all SKUs across all stores, with particular attention to high-velocity items and volatile categories.
    • Dynamic Monitoring: Tracking prices across different time frequencies as grocery prices are highly volatile for different categories. So daily tracking for volatile items like dairy and produce, and weekly for more stable categories may be needed.
    • Competitive Intelligence: Gathering data not just on prices, but on promotions, pack sizes, and private label alternatives.
    • Infrastructure to Support Large Volumes of Data: Partnering with external data and analytics providers to bridge the gap when retailers struggle with the scale of digital infrastructure these data sets require.

    2. Intelligent Data Refinement: Making Sense of the Numbers

    Raw data alone isn’t enough—it needs context and structure to become actionable intelligence. This is called Data Refinement—the process of establishing meaningful relationships within the data to facilitate the extraction of valuable insights. This refinement stage is closely tied to the data collection strategy, as the quality and depth of the insights derived depend on the accuracy and coverage of the collected data.

    Data refinement includes several key processes:

    Advanced Product Matching

    Picture this: You’re tracking a competitor’s pricing on organic apples. Simple, right? Not quite. Yes, Universal Product Codes (UPCs) and Price Lookup Codes (PLUs) are present in Grocery to standardize product identification across different retailers—unlike the fashion industry’s endless style variations. Still, product matching isn’t as straightforward as scanning barcodes.

    Grocery Pricing Intelligence data faces a challenge when product names, weights, and details differ

    Here’s the catch: many retailer websites don’t display them. Then there’s the private label puzzle—your “Store’s Best” organic apples need to match against competitors’ house brands, each with their own unique UPC. Throw in different sizes (4 Apples vs. 1Kg of Apples), regional product names (fancy naming for plain old arugula), and international brand variations (like the name for Sprite in the USA and China), and you’ve got yourself a complex matching challenge that would make conventional pricing intelligence providers sweat.

    Grocery Pricing Intelligence data faces a challenge when different naming conventions and languages are used in different geographies

    Custom Product Relationships for Consistent Pricing and Competitive Positioning

    Think like a shopper browsing the dairy aisle. You regularly buy your family’s favorite organic yogurt, the 24oz tub. But today, you notice the larger 32oz size is on sale – except the 24oz isn’t. As you stand there, confused, you wonder: Is the sale only for the bigger size? Did I miss a promotion? Should I buy the 32oz even though it’s more than I need?

    For shoppers, this inconsistent pricing across product variations creates a frustrating experience. Establishing clear relationships between related items in your catalog is essential for maintaining consistent pricing and a coherent competitive strategy.

    Grocery Pricing Intelligence data refinement involves Custom Product Relationships for Consistent Pricing and Competitive Positioning

    Start by linking products based on attributes like size, brand, and packaging. That way, when you adjust the price of the 32oz yogurt, the 24oz version automatically updates too – no more scrambling to ensure uniform pricing across your assortment. Similarly, products of the same brand but with flavor variations should be connected to keep pricing consistent.

    Taking this one step further, mapping your competitors’ exact and similar products is crucial for comprehensive competitive intelligence. Distinguishing between premium and private label tiers, national brands, and regional players gives you a holistic view of the landscape. With this understanding, you can hone your pricing strategies to maintain a clear, compelling position across your entire category lineup.

    Consistent pricing, whether across your own product variations or against competitors, provides clarity and accuracy in your overall competitive positioning. By establishing these logical connections, you avoid the customer confusion of seemingly random, inconsistent discounts – and ensure your pricing strategies work in harmony, not disarray.

    The Role of AI and Data Sciences in Data Refinement

    On the surface, linking products based on attributes like size, brand, and packaging seems like a no-brainer. But developing and maintaining the systems to accurately and automatically identify these connections? That’s a whole different animal.

    Think about it – you’re not just dealing with text-based product titles and UPCs. There are images, videos, regional variations, private labels, and a whole host of other data types and industry nuances to account for.

    Luckily, DataWeave is one of the few companies that’s truly cracked the code. Our multimodal AI models are trained to process all those diverse data formats – from granular product specs to zany regional produce names. And it’s not just about technology; we also harness the power of human intelligence.

    See, in the grocery world, category managers are the real decision makers. They know their shelves inside and out and can spot those tricky connections in product matching, especially when they are not UPC-based. That’s why DataWeave built in a Human-in-the-Loop (HITL) process, where their AI systems continuously learn from expert feedback. It’s a feedback loop that allows our customers to pitch in and keep product relationships accurate, reliable, and always adapting to new market realities.

    So while product mapping may seem straightforward on the surface, the reality is it takes some serious horsepower to do it right. Thankfully, DataWeave has both the technical chops and the grocery industry know-how to make it happen. Because when it comes to pricing intelligence, getting those product connections right is half the battle.

    3. Strategic Implementation: Turning Insights into Action

    The true value of pricing intelligence (PI) is realized through its strategic application. Although many view PI as a technical function, its strategic significance is increasing, particularly in the context of recent economic pressures like inflation. Here’s why:

    Tactical vs Strategic Use of Data: From Standard Reporting to Competitive Analysis

    Pricing intelligence has come a long way from the days of simply reacting to daily price changes. These days, it’s not just about firefighting—it’s about driving long-term strategy.

    You can use pricing data to make quick, tactical adjustments, like matching a competitor’s sudden price drop on milk. Or, you can leverage that same data to predict market trends, optimize your product lineup, and shape your overall pricing strategy. Retailers who take that strategic view can get out ahead of the curve, anticipating shifts instead of just chasing them.

    DataWeave supports both of these approaches. Our Standard Reporting tools give pricing managers the nitty-gritty details they need—current practices, historical patterns, and operational KPIs. It’s all the insights you’d expect for making those tactical, day-to-day tweaks.

    In addition, DataWeave offers something more powerful: Competitive analysis. This is where pricing intelligence becomes a true strategic weapon. By providing a high-level view of market positioning, competitor moves, and untapped opportunities, competitive analysis empowers leadership to make proactive, big-picture decisions.

    Armed with this broader perspective, retailers can start taking a more surgical approach. Maybe you need to adjust pricing zones to better meet customer demands. Or rethink your overall strategies to stay ahead of the competition, not just keep pace. It’s the difference between constantly putting out fires and systematically fortifying your entire pricing fortress.

    Beyond Pricing: Comprehensive Data for Broader Insights

    Pricing intelligence is just the tip of the iceberg. When you really start to refine and harness your data, the possibilities for grocery retailers expand far beyond simple price comparisons. Think about it – all that information you’re collecting on products, markets, and consumer behavior? That’s a goldmine waiting to be tapped. Sure, you can use it to keep a pulse on competitor pricing. But why stop there?

    What if you could leverage that data to optimize your product assortment, making sure you’re stocking the right mix to meet customer demands? Or tap into predictive analytics to get a glimpse of future market shifts, so you can get out ahead of the curve? How about using it to streamline your supply chain, identify availability inefficiencies, and get products to shelves faster?

    Sure, pricing intelligence will always be mission-critical. But when you couple it with these other data-driven insights, that’s when grocery retailing gets really interesting. It’s about evolving from a price-matching robot to a true strategic visionary, armed with the intelligence to take your business to new heights.

    Looking Ahead: The Future of Grocery Pricing Intelligence

    The grocery pricing landscape continues to evolve, driven by:

    • Integration of AI and machine learning for predictive pricing
    • Enhanced focus on omnichannel pricing consistency
    • Growing importance of personalization in pricing strategies

    Pricing intelligence isn’t just about having data—it’s about having the right data and knowing how to use it strategically. Success requires a comprehensive approach that combines robust data collection, sophisticated analysis, and strategic implementation.

    By embracing modern pricing intelligence tools and strategies, grocery retailers can navigate market volatility, maintain competitive positioning, and drive sustainable growth. The key lies in building a pricing ecosystem that’s both sophisticated enough to handle complex data and flexible enough to adapt to changing market conditions.

    Ready to transform your pricing strategy? Check out our grocery price tracker to get month-on-month updates on grocery prices in the real world. Contact us to learn how our advanced pricing intelligence solutions can help your business stay ahead in the competitive grocery market.

  • 10 SEO Tactics to Help Retail Brands Win More Search Visibility on Amazon

    10 SEO Tactics to Help Retail Brands Win More Search Visibility on Amazon

    Today, the first name that comes to anybody’s mind when they hear about online shopping is Amazon. In the US alone, Amazon accounted for over 37.6 percent of total online retail sales in 2023 with the second place Walmart not even managing to win double-digit numbers on the same scale.

    Amazon leads retail eCommerce in the USA

    With such a phenomenal market share, it is not surprising that any retail brand would want to have their products listed on Amazon for sale. However, as enticing as the potential exposure could be, the overwhelming presence of brands selling similar products on Amazon is so huge that getting fair visibility for your products may require some heavy-lifting support.

    Will the Same SEO You Use for Google Work with Amazon?

    Unfortunately, no, as Google and Amazon have different objectives when it comes to search rankings on their respective customer platforms. Google makes the lion’s share of its revenue from search advertising, whereas Amazon makes money when customers buy products listed on its platform by sellers.

    Relying on traditional search engine optimization (SEO) techniques may not get the desired results as they are more optimized for search engines like Google. Amazon embraces its unique DNA when it comes to product display rankings on its search option.

    How Does SEO Work in Amazon?

    Over the years, Amazon amassed data about shopping experiences that billions of customers globally had on its platform. With this data, they developed their custom search algorithm named A9. Contrary to the gazillion objectives that Google has for its intelligent search algorithms, Amazon has tasked A9 with just a simple straightforward target—when a customer keys in a search query, provide the best choice of products that they will most probably purchase, as search results.

    A9 works to fulfill the mission of guiding shoppers to the right product without worrying about semantics, context, intent, mind mapping, etc. of the search query in contrast to what Google does. As with Google search, Amazon does have paid advertising and sponsored results options such as Amazon PPC, Headline ads, etc. but their SEO algorithms are aware of how to support and boost search rankings of genuine products and brands that have taken an effort to follow best practices in Amazon SEO as well as have a great offering with attractive prices.

    As additional knowledge, Amazon also has clear guidelines on what it prioritizes for search rankings. Known in the SEO world as Amazon ranking signals, these are core factors that influence how a product is ranked for search queries. Some of the top Amazon ranking signals that carry heavy influence on search rankings include on-page signals, off-page signals, sales rank, best sellers rank, etc.

    What Brands Need to Strategize to Master the Amazon SEO Algorithms

    From a broad perspective, we can classify the actions brands need to take in this regard in 3 core stages:

    Pre-Optimization

    This deals with getting first-hand knowledge about both customers who are likely to purchase your product and the competitors who are vying for sales from these very same customers. Filtering your target customer or audience is essential to ensure that you get the most ROI from marketing initiatives and that sales cycles are accelerated. For example, if your product is a premium scented candle, there is no point in wasting advertising dollars trying to win attention from customers who are not likely to ever spend on luxury home décor items.

    Knowing how your competitors are performing on Amazon search, the keywords, and SEO strategies they have adapted is critical to ensure that you stay one step ahead.

    Product Listing Page Optimization

    This includes strategies that a brand can adopt so that its product description page gets the much-needed content optimizations to sync with Amazon’s A9 algorithm. It has a mix of keyword-integrated content, relevant images, descriptions in easy-to-understand language, localized content flavors to resonate with target buyers, etc. For example, a kitchen tool like a grater might be used for different kinds of food preparation techniques in different regions of the same country.

    Product Listing Optimization For Amazon SEO

    The brand must ensure that the description adequately localizes the linguistic or usage preference representation of the target audience. If the grater is used for grating coconut shells to extract the fibrous pulp in the Midlands and for grating ginger skin in the Far East, both use cases should be part of the product description if the target customers are from both regions.

    Sales Optimization

    This deals with options that have more sales strategies integrated into their core. For example, blogs on popular websites with the Amazon purchase link embedded in the content, collaboration with social media influencers, paid advertising on Amazon itself as well as on search engines, video ads, banner and display ads, etc.

    The key intent here is to drive organic and inorganic traffic to the Amazon product listing page and ultimately win sales.

    How Can Your Products Rank High in Amazon Search Results? Top 10 Tactics

    Now that you have a clear understanding of the strategies that help in mastering Amazon’s ranking algorithms, here are some great tips to help achieve higher search rankings for your products on Amazon search:

    1. Target Relevant Keywords

    You need to figure out the best keywords that match what customers put as queries into the Amazon search bar. Your brand needs to clearly understand customer behavior when they arrive on Amazon to search for a product or category of products. The best place to begin looking for the same would be on competitor pages on Amazon. The keywords that helped them rank well on Amazon can help you as well. Manually investigating such a large pool of competitors is nearly impossible but with the right tools, you can easily embrace capabilities to know which keywords can help you in mimicking the success of your competitors.

    2. Focus on Product Titles

    Every single part of the content in your brand’s Amazon storefront or product page needs dedicated focus. Beginning with the product titles, effort needs to be made to ensure that they include the brand name, key product category or features, and other relevant keyword information.

    Product Title Optimized for Amazon SEO

    In other words, product titles must be optimized for searchability. This searchability for product titles needs to be optimized for both mobile and desktop screens.

    3. Create Product Descriptions that Resonate with the Audience

    For product descriptions on your Amazon webpage, you need to figure out the optimal quality levels needed for the intended audience. Effective content can help achieve better search ranking visibility and convince the incoming traffic of shoppers to make a purchase. It is important to periodically review and modify your page content to suit the interests of visitors from both web and mobile devices.

    Product Description Optimized for Amazon SEO

    Leveraging solutions like DataWeave can help with regular content audits to ensure you are putting out the best product content that will delight shoppers and deliver on sales conversion targets.

    4. Use High-Quality Media Assets like Images and Videos

    Promoting your product doesn’t have to be restricted to just textual content in Amazon product description sections. You can use other multimedia assets of high quality. These include images, videos, brochure images, etc. Every content asset must aim to educate shoppers on why your product should be their number one choice. For example, look at this detailed product description for the viral K-Beauty product COSRX Mucin Essence.

    Product Description with Images Optimized for Amazon SEO

    Moreover, images can help attract more attention span from visitors, thereby increasing the probability of purchases.

    5. Strengthen the Backend Keywords As Well

    Amazon also supports hidden backend keywords that sellers add to their product listings. They help add more relevance to products similar to meta descriptions and titles in traditional SEO for search engines like Google. A typical backend keyword may comprise synonyms, misspelled keywords, textual variations, etc. However, knowing how to pick the right ones is crucial. By analyzing your keyword rankings against competitors and higher-ranking product results in search, the platform can help you consistently optimize your content backend to help grow visibility.

    6. Focus on Reviews and Ratings

    Reviews and ratings on product pages are key insights that help customers with their purchasing decisions. So, it is natural for brands to keep a close eye on how their products are faring in this regard. Reviews and ratings are a direct indication of the trustworthiness of your product. When previous buyers rate you high and leave favorable reviews on your product, it will directly promote trust and help you secure a better rapport with new customers.

    Reviews with Videos and Images Optimized for Amazon SEO
    Requesting reviews or leveraging user generated reviews and ratings to optimize Amazon SEO

    This upfront advantage can help boost sales conversions better. Leveraging solutions like DataWeave can help you understand the sentiments that customers have for your products by intelligently analyzing reviews and ratings.

    7. Implement Competitive Pricing Strategies

    The goal of most customers when shopping online is to get their desired product at the most affordable prices. The eCommerce price wars every year are growing in scale today and getting your product pricing right is crucial for sales. However, there is a need to gain comprehensive insights into how your competitors are pricing their offerings and how the market responds to specific price ranges. Solutions like DataWeave help your brand access specific insights into pricing. By analyzing competitor pricing, you can create a winning price model that is sustainable for your brand and favorable for target customers.

    8. Track Share of Search

    Content and other SEO activities will help improve your search rankings on Amazon. However, it is equally important to know how well your products are performing periodically against your competitors for the same set of specific keyword searches. You need to understand the share of search that your products are achieving to formulate improvement strategies. DataWeave’s Digital Shelf Analytics solution provides share of search insights helping you uncover deep knowledge on your discoverability on Amazon (and other marketplaces) for your vital search keywords.

    9. Ensure Stock Availability

    To achieve better ranking results, brands need to ensure that the relevant products matching the search keywords are available for quick delivery at the desired ZIP codes where users are more likely to search and order them. Out-of-stock items seldom show up high on search results. Certain products, especially if they’re popular, can get stocked out frequently in certain locations. Keeping a close eye on your stock availability across the map can help minimize these scenarios.

    10. Optimize Your Brand Presence

    While optimizing content and other key areas within the Amazon webpage for your product is critical, there are other avenues to help boost search rankings. One such option includes registering in the Amazon Brand Registry, which provides more beneficial features like protection against counterfeits and ensuring that your brand page is optimized according to Amazon storefront standards.

    The Bottom Line

    Winning the top spot in Amazon search ranking is crucial for brands that aim to capitalize on online sales revenue to grow their business. Knowing your workaround for Amazon’s proprietary SEO frameworks and algorithms is the first step to succeeding. The key element of success is your ability to gain granular insights into the areas we covered in this blog post such as competitor prices, sentiments of customers, market preferences, and content optimization requirements.

    This is where DataWeave’s Digital Shelf Analytics solution becomes the biggest asset for your eCommerce business. Contact us to explore how we can empower your business to build the most visible and discoverable Amazon storefront that guarantees higher search rankings and ultimately increased sales. Talk to us for a demo today.

  • Normalizing Size and Color in Fashion Using AI to Power Competitive Price Intelligence

    Normalizing Size and Color in Fashion Using AI to Power Competitive Price Intelligence

    Fashion is as dynamic a market as any—and more competitive than most others. Consumer trends and customer needs are always evolving, making it challenging for fashion and apparel brands to keep up.

    Despite the inherent difficulties fashion and apparel sellers face, this industry is one of the largest grossing markets in the world, estimated at $1.79 trillion in 2024. Global revenue for apparel is expected to grow at an annual rate of about 3.3% over the next four years. That means companies in this space stand to make significant revenue if they can competitively price their products, keep up with the competition, and win customer loyalty with consistent product availability.

    There are three main categories in fashion and apparel. These include:

    • Apparel and clothing (i.e., shirts, pants, dresses, and other apparel)
    • Footwear (i.e., sneakers, sandals, heels, and other products)
    • Accessories (i.e., bags, belts, watches, and so on)

    If you look at all of these product types across all sorts of retailers, there is a massive amount of overlapping data based on product attributes like style and size that are difficult to normalize.

    Fashion Attributes

    Style, color, and size are the main attribute categories in fashion and apparel. Style attributes include things like design, look, and overall aesthetics of the product. They’re very dependent on the actual product category of fashion as well. A shirt might have a slim fit attribute associated with it, whereas a belt might have a length. All these different attributes are usually labeled within a product listing and affect the consumer’s decision-making process:

    • Color (red, blue, sea green, etc.)
    • Pattern (solid, striped, checked, floral, etc.)
    • Material (cotton, polyester, leather, denim, silk, etc.)
    • Fit (regular, slim, relaxed, oversized, tailored, etc.)
    • Type (casual, formal, sporty, vintage, streetwear)

    Color Complexity in Fashion

    Color is perhaps the most visually distinctive attribute in fashion, yet it presents unique challenges for retailers. This is because color naming can vary across retailers and marketplaces. There are several major differences in color convention:

    • A single color can be labeled differently across brands (e.g., “navy,” “midnight blue,” “deep blue”)
    • Seasonal color names (e.g., “summer sage” vs. “forest green”)
    • Marketing-driven names (e.g., “sunset coral” vs. “pale orange”)
    Differences in color naming - challenges faced by fashion retail intelligence systems

    Size: The Other Critical Dimension

    Size in fashion refers to the dimensions or measurements that determine how fashion products fit. Depending on whether the product is a clothing item, shoes, or a hat, there will be different sizing options. Types of sizes include:

    • Standard sizes (XS, S, M, L, XL, XXL, XXL)
    • Custom sizes (based on brand, retailer, country, etc.)

    A single type of product may have different sizing labels. For instance, one pants listing may use traditional S, M, L, XL sizing, while another pants listing may use 24, 25, or 26, to refer to the waist measurement.

    Size Variations - challenges faced by fashion retail intelligence systems
    Size Variations - challenges faced by fashion retail intelligence systems
    Size Variations - challenges faced by fashion retail intelligence systems

    Size is a dynamic attribute that changes based on current trends. For example, there has recently been a significant shift towards inclusive sizing. Size inclusivity refers to the practice of selling apparel in a wide range of sizes to accommodate people of all body types. Consumers are more aware of this trend and are demanding a broader range of sizing offerings from the brands they shop from.

    In the US market, in particular, some 67% of American women wear a size 14 or above and may be interested in purchasing plus-size clothing. There is a growing demand in the plus-size market for more options and a wider selection. Many brands are considering expanding their sizes to accommodate more shoppers and tap into this growing revenue channel.

    Pricing Based on Size and Color

    Many fashion products are priced differently based on size and color. Let’s take a look at an example of what this can look like.

    Different colors may retail at different price points.

    A popular beauty brand (see image) is known for its viral lip tint. While most of the color variants are priced at $9.90 on Amazon, a specific colorway option, featuring less pigmented options, is priced at $9.57. This price differential is driven by both material costs and market demand.

    Different colorways (any of a range of combinations of colors in which a style or design is available) of the same product often command different prices also. This is based on:

    • Dye costs (some colors require more expensive processes)
    • Seasonal demand (traditional colors vs. trend colors)
    • Exclusivity (limited edition colors)

    An example of price variations by size is a women’s shirt that is being sold on Amazon as shown below. For this product, there are no style attributes to choose from. The only parameter the shopper has to select is the size they’d like to purchase. They can choose from S to XL. On the top, we can see that the product in size S is ₹389. Below, the size XL version of this same shirt is ₹399. This price increase is correlated to the change in size.

    Different sizes may retail at different price points.
    Different sizes may retail at different price points.

    So why are these same products priced differently? In an analysis of One Six, a plus-size clothing brand, several reasons for this difference in plus-size clothing were determined.

    • Extra material is needed, hence an increase in production costs
    • Extra stitching costs, hence an increase in production costs
    • Production of plus-size clothing often means acquiring specialized machinery
    • Smaller scale production runs for plus-size clothing means these initiatives often don’t benefit from cost savings

    Some sizes are sold more than others, meaning that in-demand sizes for certain apparel can affect pricing as well. Brands want to be able to charge as much as possible for their listing without risking losing a sale to a competitor.

    The Competitive Pricing Challenge: Normalizing Product Attributes Across Competitors in Apparel and Fashion

    There are hundreds of possible attribute permutations for every single apparel product. Some retailers may only sell core sizes and basic colors; some may sell a mix of sizes for multiple style types. Most retailers also sell multiple color variants for all styles they have on catalog. Other retailers may only sell a single, in-demand size of the product. Also, when other retailers are selling the product, it’s unlikely that their naming conventions, color options, style options, and sizing match yours one-for-one.

    In one analysis, it was found that there were 800+ unique values for heel sizes and 1000+ unique values for shirts and tops at a single retailer! If you’re looking to compare prices, the effort involved in setting up and managing lookup tables to identify discrepancies when one retailer uses European sizes and another uses USA sizes, for example, is simply too onerous to contemplate doing. Colors only add to the complexity – as similar colors may have new names in different regions and locations as well!

    Even if you managed to find all the discrepancies between product attributes, you would still need to update them any time a competitor changed a convention.

    Still, monitoring your competitors and strategically pricing your listings is essential to maintain and grow market share. So what do you do? You can’t simply eyeball your competitor’s website to check their pricing and naming conventions. Instead, you need advanced algorithms to scan the entire marketplace, identify individual products being sold, and normalize their data and attributes for analysis.

    Getting Color and Size Level Pricing Intelligence

    With DataWeave, size and color are just two of several dimensions of a product instead of an impossible big data problem for teams. Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification.

    This works by using AI built on more than 10 years of product catalog data across thousands of retail websites. It matches common identifiers, like UPC, SKU code, and other attributes for harmonization before employing a large language model (LLM) prompts to normalize color variations and sizing to a single standard.

    The data flow DataWeave uses for product sizing and color normalization

    For example, if a competitor has the smallest size listed as Sm but has your smallest listing identified as S, DataWeave can match those two attributes using AI. Similar classification can be performed on color as well.

    Complex LLM prompts are pre-established so that this process is fast and efficient, taking minutes rather than weeks of manual effort.

    Harmonizing products along with their color and sizing data across different retailers for further analysis has several benefits. Most importantly, product matching helps teams conduct better competitive analysis, allowing them to stay informed about market trends, competitors’ offerings, and how those competitors are pricing various permutations of the same product. It helps ensure that you’re offering the most competitive assortment of sizing in several colors to win more market share as well. Overall, it’s easier for teams to gain insights and exploit their findings when all the data is clean and available at their fingertips.

    Product Matching Size and Color in Apparel and Fashion

    Color and size are crucial attributes for retailers and brands in the apparel and fashion industry. It adds a level of complexity that can’t be overstated. While it’s a necessity to win consumers (more colors and sizes will mean a wider potential reach), the more permutations you add to your listing, the more complicated it will be to track it against your competition. However, This challenge is worth undertaking as long as you have the right solutions at your disposal.

    With a strategy backed by advanced technology to discover identical and similar products across the competitive landscape and normalize their color and sizing attributes, you can ensure that you are competitively pricing your products and offering the best assortment possible. Employing DataWeave’s AI technology to find competitor listings, match products across variants, and track pricing regularly is the way to go.

    Interested in learning more about DataWeave? Click here to get in touch!

  • The Complete Guide to Competitive Pricing Strategies in Retail and E-commerce

    The Complete Guide to Competitive Pricing Strategies in Retail and E-commerce

    Your budget-conscious customers are hunting for value and won’t hesitate to switch brands or shop at other retailers.

    In saturated and fiercely competitive markets, how can you retain customers? And better yet, how can you attract more customers and grow your market share? One thing you can do as a brand or retailer is to set the right prices for your products.

    Competitive or competition-based pricing can help you get there.

    So what exactly is competitive pricing? Let’s dive into this strategy, its advantages and disadvantages, and how it can be used to stay ahead of the competition.

    What is Competitive Pricing?

    Competitive or competition-based pricing is a strategy where brands and retailers set product prices based on what their competitors charge. This method focuses entirely on the market landscape and sets aside the cost of production or consumer demand.

    It is a good pricing model for businesses operating in saturated markets, such as consumer packaged goods (CPGs) or retail.

    Competitive Pricing Models

    Competitive pricing isn’t a one-size-fits-all strategy. The approach includes various pricing models that can be customized to fit your business goals and market positioning.

    Here’s a closer look at five of the most common competition-based pricing models:

    Price Skimming

    If you have a new product entering the market, you can initially set a high price. Price skimming allows you to maximize margins when competition is minimal.

    This strategy taps into early adopters’ willingness to pay a premium for new project categories. As competitors enter the market, you can gradually reduce the price to maintain competitiveness.

    Premium Pricing

    Premium pricing lets you position your product as high-quality or luxurious goods.

    When you charge more than your competitors, you’re not just selling a product—you’re selling status and an experience. This strategy is effective when your offering is of superior quality or has unique features that justify a higher price point.

    Price Matching

    Price matching—also known as parity pricing—is a defensive pricing tactic.

    By consistently matching your competitors’ prices, you can retain customers who might otherwise, be tempted to switch to an alternative.

    This approach signals your customers that they don’t need to look elsewhere for what they need and can feel comfortable remaining loyal to your brand.

    Penetration Pricing

    Penetration pricing is when you set a low price for a new product to gain market share quickly. The opposite of price skimming, this strategy can be particularly effective in price-sensitive or highly competitive industries.

    By attracting customers early, you can also deter some competitors from entering the market. This bold move can establish your product as a market leader from the get-go.

    Loss Leader Pricing

    Loss leader pricing is a strategic sacrifice that can lead to greater gains in the long run.

    By offering a product at a low price—sometimes even below cost—you can attract new customers to your brand and strengthen your current customers’ loyalty.

    Eventually, you can cross-sell other higher-margin products to your loyal customer base to cover the loss from your loss leader pricing and increase sales of other more profitable products.

    Key Advantages of Competitive Pricing

    Although it’s not the only pricing strategy available, competitive pricing has some significant advantages.

    It is Responsive

    Agility is synonymous with profit in industries where consumer preferences and market conditions shift rapidly.

    Competitive pricing allows you to adapt quickly—if a competitor lowers their prices, you can respond promptly to maintain your positioning.

    It is Simple to Execute and Manage

    Competitive pricing is straightforward, unlike cost-based pricing, which requires complex calculations and spans various factors and facets.

    By closely monitoring competitors’ prices and adjusting your prices accordingly, you can implement this pricing strategy with relative ease and speed.

    It Can Be Combined with Other Pricing Strategies

    Competitive pricing is not a standalone strategy—it’s a versatile approach that can easily be combined with other pricing strategies. For example, say you want to use competitive pricing without losing money on a product. In this case, you could use cost-plus pricing to determine a base price that you won’t go below, then use competitive pricing as long as the price stays above your base price.

    Key Disadvantages of Competitive Pricing

    While competition-based pricing has its advantages, it’s not without its pitfalls. Here are some potential disadvantages of competitive pricing.

    It De-emphasizes Consumer Demand

    If you focus solely on what competitors are charging, you could overlook consumer demand.

    For example, you could underprice items that consumers could be willing to purchase for more. Or, you might overprice items that consumers perceive as low-value, which can reduce sales.

    You Risk Price Wars

    If you and your competition undercut each other for customer acquisition and loyalty, you will eventually erode profit margins and harm the industry’s overall profitability. It’s a slippery slope where everyone loses in the end.

    There’s Potential for Complacency

    When you base your prices on beating those of competitors, you might neglect to differentiate your offerings through innovation and product improvements. Over time, this can weaken your brand’s position and lead to a loss of market share. Staying competitive means more than just matching prices—it means continuously evolving and adding value for the consumer.

    4 Tips for a Successful Competitive Pricing Strategy in Retail

    Here are four competition-based pricing tips for retailers:

    Retailer Tip #1. Know Where to Position Your Products in the Market

    For competitive pricing to work, you must understand your optimal product positioning in the overall market. To gain this understanding, you must regularly compare your offerings and prices with those of your key competitors, especially for high-demand products.

    Then, you can decide which competition-based pricing model is suitable for you.

    Retailer Tip #2. Price Dynamically

    Dynamic pricing is a tactic with which you automatically adjust prices on your chosen variables, such as market conditions, competitor actions, or consumer demand.

    When it comes to competitive pricing, a dynamic pricing system can track your competitors’ price changes and update yours in lockstep.

    Price-monitoring tools like DataWeave allow you to stay ahead of the game with seasonal and historical pricing trend data.

    Retailer Tip #3. Combine Competitive Pricing with Other Pricing Strategies

    Competitive pricing can be powerful, but it doesn’t have to stand alone. You can enhance its benefits with complementary marketing tactics.

    To illustrate, you can bundle products to offer greater value than what your competitors are offering. You can also leverage loyalty programs to offer exclusive discounts or rewards so customers keep returning, even when your competitors offer them lower prices.

    Retailer Tip #4. Stay in Tune with Consumer Demand

    Competition-based pricing aligns you with your competitor, but don’t lose sight of what your customers want. Routinely test your pricing strategy against consumer behavior to ensure that your prices reflect the actual value of your offerings.

    5 Tips for a Successful Competitive Pricing Strategy for Consumer Brands

    If you’re thinking about how to create a competitive pricing strategy for your brand, consider these five tips:

    Brand Tip #1. Identify Competing Products for Accurate Comparisons

    The first step in competitive pricing is to know the value of what you’re selling and how it compares to that of your competitors’ products. This extends to private-label products, similar but not identical products, and use-case products.

    Product matching ensures your pricing decisions are based on accurate like-for-like comparisons, allowing you to compete effectively.

    Brand Tip #2. Understand Your Product’s Relative Value

    Knowing how your product competes on value is key to setting the right price. If your product offers higher value, price it higher; if it offers less, price it accordingly. This ensures your pricing strategy reflects your product’s market placement.

    Brand Tip #3. Consider Brand Perception

    Even if your product is virtually the same as a competitor’s, your brand’s perceived value may be different, which plays a crucial role in pricing.

    If your brand is perceived as premium, you can justify higher prices. Conversely, if customers perceive you as a value brand, your pricing should reflect affordability.

    Brand Tip #4. Leverage Value-Based Differentiation

    When your prices are similar to competitors’, you must differentiate your products by expressing your product value through branding, packaging, quality, or something else entirely.

    This differentiation will compel consumers to choose your product over other similarly priced options.

    Brand Tip #5. Stay Vigilant with Price Monitoring

    Your competitors will update their pricing repeatedly, and you will, too.

    It can be difficult and time-consuming to monitor your competitive pricing, so you’ll need a system like DataWeave to monitor competitors’ pricing and manage dynamic pricing changes.

    This vigilance ensures your brand remains competitive and relevant in real time.

    4 Essential Capabilities You Need to Implement Successful Competition-Based Pricing

    You’ll need four key capabilities to implement a competitive pricing strategy effectively.

    AI-Driven Product Matching

    Product matching means you’ll compare many products (sometimes tens or hundreds) with varying details across multiple platforms. Accurate product matching at that scale requires AI.

    For instance, AI can identify similar smartphones to yours by analyzing features like screen size and processor type. DataWeave’s AI product matches start with 80–90% matching accuracy, and then human oversight can fine-tune the data for near-perfect matches.

    You can make informed pricing decisions once you know which competing products to base your prices on.

    Accurate and Comprehensive Data

    A successful competition-based pricing strategy depends on high-quality, comprehensive product and pricing data from many retailers and eCommerce marketplaces.

    By tracking prices on large online platforms and niche eCommerce sites across certain regions, you’ll gain a more comprehensive market view, which enables you to make quick and confident price changes.

    Normalized Measurement Units

    Accurate price comparisons are dependent on normalized unit measurements.

    For example, comparing laundry detergent sold in liters to laundry detergent sold in ounces requires converting either or both products to a common base like price-per-liter or price-per-ounce.

    This normalization ensures accurate pricing analysis.

    Timely Actionable Insights

    Timely and actionable pricing insights empower you to make informed pricing decisions.

    With top-tier competitive pricing intelligence systems, you get customized alerts, intuitive dashboards, and detailed reports to help your team quickly act on insights.

    In Conclusion

    Competitive pricing or competition-based pricing is a powerful strategy for businesses navigating crowded markets, but you must balance competitive pricing with your brand’s unique value proposition.

    Competitive pricing should complement innovation and customer-centric strategies, not replace them. To learn more, talk to us today!

  • DataWeave’s AI Evolution: Delivering Greater Value Faster in the Age of AI and LLMs

    DataWeave’s AI Evolution: Delivering Greater Value Faster in the Age of AI and LLMs

    In retail, competition is fierce, and in its ever-evolving landscape, consumer expectations are higher than ever.

    For years, our AI-driven solutions have been the foundation that empowers businesses to sharpen their competitive pricing and optimize digital shelf performance. But in today’s world, evolution is constant—so is innovation. We now find ourselves at the frontier of a new era in AI. With the dawn of Generative AI and the rise of Large Language Models (LLMs), the possibilities for eCommerce companies are expanding at an unprecedented pace.

    These technologies aren’t just a step forward; they’re a leap—propelling our capabilities to new heights. The insights are deeper, the recommendations more precise, and the competitive and market intelligence we provide is sharper than ever. This synergy between our legacy of AI expertise and the advancements of today positions DataWeave to deliver even greater value, thus helping businesses thrive in a fast-paced, data-driven world.

    This article marks the beginning of a series where we will take you through these transformative AI capabilities, each designed to give retailers and brands a competitive edge.

    In this first piece, we’ll offer a snapshot of how DataWeave aggregates and analyzes billions of publicly available data points to help businesses stay agile, informed, and ahead of the curve. These fall into four broad categories:

    • Product Matching
    • Attribute Tagging
    • Content Analysis
    • Promo Banner Analysis
    • Other Specialized Use Cases

    Product Matching

    Dynamic pricing is an indispensable tool for eCommerce stores to remain competitive. A blessing—and a curse—of online shopping is that users can compare prices of similar products in a few clicks, with most shoppers gravitating toward the lowest price. Consequently, retailers can lose sales over minor discrepancies of $1–2 or even less.

    All major eCommerce platforms compare product prices—especially their top selling products—across competing players and adjust prices to match or undercut competitors. A typical product undergoes 20.4 price changes annually, or roughly once every 18 days. Amazon takes it to the extreme, changing prices approximately every 10 minutes. It helps them maintain a healthy price perception among their consumers.

    However, accurate product matching at scale is a prerequisite for the above, and that poses significant challenges. There is no standardized approach to product cataloging, so even identical products bear different product titles, descriptions, and attributes. Information is often incomplete, noisy, or ambiguous. Image data contains even more variability—the same product can be styled using different backgrounds, lighting, orientations, and quality; images can have multiple overlapping objects of interest or extraneous objects, and at times the images and the text on a single page might belong to completely different products!

    DataWeave leverages advanced technologies, including computer vision, natural language processing (NLP), and deep learning, to achieve highly accurate product matching. Our pricing intelligence solution accurately matches products across hundreds of websites and automatically tracks competitor pricing data.

    Here’s how it works:

    Text Preprocessing

    It identifies relevant text features essential for accurate comparison.

    • Metadata Parsing: Extracts product titles, descriptions, attributes (e.g., color, size), and other structured data elements from Product Description Pages (PDP) that can help in accurately identifying and classifying products.
    • Attribute-Value Normalization: Normalize attributes names (e.g. RAM vs Memory) and their values (e.g., 16 giga bytes vs 16 gigs vs 16 GB); brand names (e.g., Benetton vs UCB vs United Colors of Benetton); mapping category hierarchies a standard taxonomy.
    • Noise Removal: Removes stop words and other elements with no descriptive value; this focuses keyword extraction on meaningful terms that contribute to product identification.

    Image Preprocessing

    Image processing algorithms use feature extraction to define visual attributes. For example, when comparing images of a red T-shirt, the algorithm might extract features such as “crew neck,” “red,” or “striped.”

    Image Preprocessing using advanced AI and other tech for product matching in retail analytics.

    Image hashing techniques create a unique representation (or “hash”) of an image, allowing for efficient comparison and matching of product images. This process transforms an image into a concise string or sequence of numbers that captures its essential features even if the image has been resized, rotated, or edited.

    Before we perform these activities there is a need to preprocess images to prepare them for downstream operations. These include object detection to identify objects of interest, background removal, face/skin detection and removal, pose estimation and correction, and so forth.

    Embeddings

    We have built a hybrid or a multimodal product-matching engine that uses image features, text features, and domain heuristics. For every product we process we create and store multiple text and image embeddings in a vector database. These include a combination of basic feature vectors (e.g. tf-idf based, colour histograms, share vectors) to more advanced deep learning algorithms-based embeddings (e.g., BERT, CLIP) to the latest LLM-based embeddings.

    Classification

    Classification algorithms enhance product attribute tagging by designating match types. For example, the product might be identified as an “exact match”, “variant”, “similar”, or “substitute.” The algorithm can also identify identical product combinations or “baskets” of items typically purchased together.

    What is the Business Impact of Product Matching?

    • Pricing Intelligence: Businesses can strategically adjust pricing to remain competitive while maintaining profitability. High-accuracy price comparisons help businesses analyze their competitive price position, identify opportunities to improve pricing, and reclaim market share from competitors.
    • Similarity-Based Matching: Products are matched based on a range of similarity features, such as product type, color, price range, specific features, etc., leading to more accurate matches.
    • Counterfeit Detection: Businesses can identify counterfeit or unauthorized versions of branded products by comparing them against authentic product listings. This helps safeguard brand identity and enables brands to take legal action against counterfeiters.

    Attribute Tagging

    Attribute tagging involves assigning standardized tags for product attributes, such as brand, model, size, color, or material. These naming conventions form the basis for accurate product matching. Tagging detailed attributes, such as specifications, features, and dimensions, helps match products that meet similar criteria. For example, tags like “collar” or “pockets” for apparel ensure high-fidelity product matches for hard-to-distinguish items with minor stylistic variations.

    Attributes that are tagged when images are matched for retail ecommerce analytcis.

    Including tags for synonyms, variants, and long-tail keywords (e.g., “denim” and “jeans”) improves the matching process by recognizing different terms used for similar products. Metadata tags categorize similar items according to SKU numbers, manufacturer details, and other identifiers.

    Altogether, these capabilities provide high-quality product matches and valuable metadata for retailers to classify their products and compare their product assortment to competitors.

    User-Generated Content (UGC) Analysis

    Customer reviews and ratings are rich sources of information, enabling brands to gauge consumer sentiment and identify shortcomings regarding product quality or service delivery. However, while informative, reviews constitute unstructured “noisy” data that is actionable only if parsed correctly.

    Here’s where DataWeave’s UGC analysis capability steps in.

    • Feature Extractor: Automatically pulls specific product attributes mentioned in the review (e.g., “battery life,” “design” and “comfort”)
    • Feature Opinion Pair: Pairs each product attribute with a corresponding sentiment from the review (e.g., “battery life” is “excellent,” “design” is “modern,” and “comfort” is “poor”)
    • Calculate Sentiment: Calculates an overall sentiment score for each product attribute
    The user generated content analysis framework used by DataWeave to calculate sentiment.

    The final output combines the information extracted from each of these features, which looks something like this:

    • Battery life is excellent
    • Design is modern
    • Not satisfied with the comfort

    The algorithm also recognizes spammy reviews and distinguishes subjective reviews (i.e., those fueled by emotion) from objective ones.

    DataWeave's image processing tool also analyses promo banners.

    Promo Banner Analysis

    Our image processing tool can interpret promotional banners and extract information regarding product highlights, discounts, and special offers. This provides insights into pricing strategies and promotional tactics used by other online stores.

    For example, if a competitor offers a 20% discount on a popular product, you can match or exceed this discount to attract more customers.

    The banner reader identifies successful promotional trends and patterns from competitors, such as the timing of discounts, frequently promoted product categories or brands, and the duration of sales events. Ecommerce stores can use this information to optimize their promotion strategies, ensuring they launch compelling and timely offers.

    Other Specialized Use Cases

    While these generalized AI tools are highly useful in various industries, we’ve created other category—and attribute-specific capabilities for specialty goods (e.g., those requiring certifications or approval by federal agencies) and food items. These use cases help our customers adhere to compliance requirements.

    Certification Mark Detector

    This detector lets retailers match items based on official certification marks. These marks represent compliance with industry standards, safety regulations, and quality benchmarks.

    Example:

    • USDA Organic: Certification for organic food production and handling
    • ISO 9001: Quality Management System Certification

    By detecting these certification marks, the system can accurately match products with their certified counterparts. By identifying which competitor products are certified, retailers can identify products that may benefit from certification.

    Image analysis based product matching at DataWeave also detects certificate marks.

    Nutrition Fact Table Reader

    Product attributes alone are insufficient for comparing food items. Differences in nutrition content can influence product category (e.g., “health food” versus regular food items), price point, and consumer choice. DataWeave’s nutrition fact table reader scans nutrition information on packaging, capturing details such as calorie count, macronutrient distribution (proteins, fats, carbohydrates), vitamins, and minerals.

    The solution ensures items with similar nutritional profiles are correctly identified and grouped based on specific dietary requirements or preferences. This helps with price comparisons and enables eCommerce stores to maintain a reliable database of product information and build trust among health-conscious consumers.

    Image processing for product matching also extracts nutrition table data at DataWeave.

    Building Next-Generation Competitive and Market Intelligence

    Moving forward, breakthroughs in generative AI and LLMs have fueled substantial innovation, which has enabled us to introduce powerful new capabilities for our customers.

    How Gen AI and LLMs are used by DataWeave to glean insights for analytics

    These include:

    • Building Enhanced Products, Solutions, and Capabilities: Generative AI and LLMs can significantly elevate the performance of existing solutions by improving the accuracy, relevance, and depth of insights. By leveraging these advanced AI technologies, DataWeave can enhance its product offerings, such as pricing intelligence, product matching, and sentiment analysis. These tools will become more intuitive, allowing for real-time updates and deeper contextual understanding. Additionally, AI can help create entirely new solutions tailored to specific use cases, such as automating competitive analysis or identifying emerging market trends. This positions DataWeave to remain at the forefront of innovation, offering cutting-edge solutions that meet the evolving needs of retailers and brands.
    • Reducing Turnaround Time (TAT) to Go-to-Market Faster: Generative AI and LLMs streamline data processing and analysis workflows, enabling faster decision-making. By automating tasks like data aggregation, sentiment analysis, and report generation, AI dramatically reduces the time required to derive actionable insights. This efficiency means that businesses can respond to market changes more swiftly, adjusting pricing or promotional strategies in near real-time. Faster insights translate into reduced turnaround times for product development, testing, and launch cycles, allowing DataWeave to bring new solutions to market quickly and give clients a competitive advantage.
    • Improving Data Quality to Achieve Higher Performance Metrics: AI-driven technologies are exceptionally skilled at cleaning, organizing, and structuring large datasets. Generative AI and LLMs can refine the data input process, reducing errors and ensuring more accurate, high-quality data across all touchpoints. Improved data quality enhances the precision of insights drawn from it, leading to higher performance metrics like better product matching, more accurate price comparisons, and more effective consumer sentiment analysis. With higher-quality data, businesses can make smarter, more informed decisions, resulting in improved revenue, market share, and customer satisfaction.
    • Augmenting Human Bandwidth with AI to Enhance Productivity: Generative AI and LLMs serve as powerful tools that augment human capabilities by automating routine, time-consuming tasks such as data entry, classification, and preliminary analysis. This allows human teams to focus on more strategic, high-value activities like interpreting insights, building relationships with clients, and developing new business strategies. By offloading these repetitive tasks to AI, human productivity is significantly enhanced. Employees can achieve more in less time, increasing overall efficiency and enabling teams to scale their operations without needing a proportional increase in human resources.

    In our ongoing series, we will dive deep into each of these capabilities, exploring how DataWeave leverages cutting-edge AI technologies like Generative AI and LLMs to solve complex challenges for retailers and brands.

    In the meantime, talk to us to learn more!

  • Competitor Price Monitoring in E-commerce: Everything You Need to Know

    Competitor Price Monitoring in E-commerce: Everything You Need to Know

    Picture this: You wake up one morning to discover that your top competitor reduced their prices overnight. And now your shopper traffic has tanked and your sales have taken a hit.

    Unfortunately, this is a common scenario because your customers can compare prices online in seconds—and loyalty lies with the budget.

    So, how can you protect your business? Price monitoring.

    Price monitoring solutions can help you keep abreast of competitor price changes—which, of course, will help you improve your pricing strategies, retain your customers, and maximize your profits.

    How? In this article, we’ll explore:

    • What is price monitoring
    • The key benefits of price monitoring for retailers and brands
    • What a capable price monitoring solution can do

    What Is Price Monitoring?

    Price monitoring is the process of tracking and analyzing your competitor’s prices across various online and offline platforms. By monitoring competitors’ prices, you can understand market price trends and adjust your prices strategically—which, in turn, helps you remain competitive, increase margins, and improve customer retention.

    5 Benefits of Price Monitoring

    Competitor price monitoring can help you:

    1. Gain a competitive edge: Competitor price tracking allows you to adjust your prices to remain attractive to consumers.
    2. Maximize revenue: With timely pricing data, you’re empowered to identify optimum price points that strike a delicate balance between maximizing revenue and maintaining customer loyalty.
    3. Retain customers: Consumers are looking for the most value for their dollar, so maintaining consistently competitive pricing is crucial for retaining loyal customers.
    4. Understand promotional effectiveness: Price monitoring helps businesses evaluate the effectiveness of their promotions and discounts. By comparing the impact of different pricing strategies, businesses can refine their promotional tactics to maximize sales and customer engagement.
    5. Understand market movements: By analyzing historical pricing data, you’re better positioned to anticipate future pricing changes — and adjust your strategies accordingly.

    4 Essential Capabilities of Price Monitoring Software

    Here are four capabilities to look for when choosing a price monitoring system.

    1. AI-Driven Product Matching

    Product matching is the process of identifying identical or similar products across different platforms to ensure accurate price comparisons.

    If your price monitoring solution can’t reliably match your products with competitors’ across various sales channels at scale, you’ll end up with poor data. Inaccurate data will then lead you to make misinformed pricing decisions.

    Product matching needs to be accurate and comprehensive, covering a wide range of products and product variations—even for including private label products.

    For example, AI-driven product matching can recognize a specific brand and model of sneakers across multiple online stores—even if product descriptions and images differ. Here’s how it works in a nutshell:

    • Sophisticated algorithms and deep learning architecture enable AI to identify and match products that aren’t identical but share key characteristics and features.
    • Using unified systems for text and image recognition, the AI matches similar SKUs across hundreds of eCommerce stores and millions of products.
      The AI zeroes in on critical product elements in images, like a t-shirt’s shape, sleeve length, and color.
    • The AI also extracts unique signatures from photos for rapid, efficient identification and grouping across billions of indexed items.

    DataWeave’s AI algorithm can initially match products with 80–90% accuracy. Then, humans can bring contextual judgement and make nuanced decisions that the AI might miss to correct errors quickly and push for accuracy closer to 100%. By integrating AI automation with human validation, you can achieve accurate and reliable product-matching coverage at scale.

    2. Accurate and Comprehensive Data Collection and Aggregation

    The insights you derive are only as good as the data you collect. However, capturing comprehensive pricing data is tough when your competitors operate on multiple platforms.

    For truly effective price monitoring insights, you need consistent, comprehensive, and highly accurate data. This means your chosen price monitoring system should:

    • Scrape data from various sources, such as desktop and mobile sites and mobile applications.
    • Pull data from various online platforms like aggregators, omnichannel retailers, delivery intermediaries, online marketplaces, and more.
    • Handle data from different regions and languages.
    • Collect data at regular intervals to ensure timeliness.

    DataWeave’s online price monitoring software covers all of these bases and more with a fast, automated data source configuration system. It also allows you to painlessly add new data sources to scrape.

    Instead of incomplete or inaccurate data, you’ll have comprehensive and up-to-date data, allowing you to respond quickly to market changes with confidence.

    3. Seamless Normalization of Product Measurement Units

    You can’t compare apples to oranges—or price-per-kilogram to price-per-pound.

    For price monitoring to be accurate, there must be a way to normalize measurement units—so that we’re always comparing price-per-gram to price-per-gram. If we compare prices without taking into account measurement units, our data will be misleading at best.

    Let’s take a closer look. Say that your top competitor sells 12oz cans of beans for $3, and you sell 15oz cans for $3.20. At first glance, your larger cans of beans will appear more expensive—but that’s not true. If we normalize the measurement unit—in this example, an oz—the larger can of beans offers more value to customers.

    Unit of measure normalization facilitates sound price adjustments based on accurate and reliable data. For this reason, every business needs a price tracking tool that can guarantee accurate comparisons by normalizing unit measurements—including weight, volume, and quantity.

    4. Actionable Data and an Intuitive User Experience

    Knowledge is only powerful when applied—and price monitoring insights are only useful when they’re accessible and actionable.

    For this reason, the best price monitoring software doesn’t just provide insights based on accurate and comprehensive data, but it also provides several ways to understand and deploy those insights.

    Ideal price monitoring solutions provide customized pricing alerts, intuitive dashboards, detailed reports, and visuals that are easy to interpret—all tailored to each particular team or a team member’s needs. These features should make it easy for team members to compare prices against those of competitors in specific categories and product groupings.

    Your price tracking tool should also permit flexible API integrations and offer straightforward data export options. This way, you can integrate competitive pricing data with your pricing software, Business Intelligence (BI) tools, or Enterprise Resource Planning (ERP) system.

    4 Ways Retailers Can Leverage Price Monitoring

    Retailers can use price monitoring tools to remain competitive without compromising profitability—here’s how:

    1. Track Competitors’ Prices

    Competitor price monitoring helps you avoid being undercut—and, as a result, maintain market share. By tracking competitor prices in real-time, you can adjust prices to remain competitive, especially in dynamic markets. Ideally, you should monitor both direct competitors selling the same products and indirect competitors selling similar or alternative products. This way, you’ll have a complete picture of market prices and can make more informed pricing adjustments.

    2. Understand Historical and Seasonal Price Trends

    As a retailer, you may want to analyze historical data to identify price patterns and predict future price movements—especially in relation to holidays and seasonal products. Knowing what’s coming, you’re better positioned to plan for pricing changes and promotional campaigns.

    3. Implement Dynamic Pricing

    Dynamic pricing is the process of adjusting prices based on real-time market conditions, product demand, and competitors’ prices—allowing you to respond faster to market changes to maintain optimized prices.

    4. Optimize Promotional Strategies

    Price monitoring tools can track retail promotions across numerous online and offline sales avenues, providing insight into the nature and timing of competitors’ promotions. This data can help you determine which promotions are most effective—and which aren’t—allowing you to improve your own promotions and discounts, and allocate marketing resources where it matters most. This is especially beneficial during peak sales periods.

    3 Ways Brands Can Employ Price Monitoring

    Here are three ways brands can use price monitoring to remain profitable, protect brand equity, and gain a competitive edge.

    1. Maintain Consistent Retail Prices

    Minimum advertised price (MAP) policies are designed to prevent retailers from devaluing a brand while ensuring fair competition among retailers. Price monitoring applications allow your brand to track retailers’ prices to detect MAP policy violations. Data in hand, you can maintain consistent pricing across online sales channels, physical stores, and retail stores’ digital shelves — and, critically, protect your brand equity.

    2. Improve Product and Brand Positioning

    When you understand how your products’ prices compare to those of competitors, you can set prices to improve brand positioning. For example, if you want to position your brand as luxurious and high-quality, you need to set higher product prices than budget-friendly alternative products.

    3. Ensure Product Availability

    You can use a price monitoring solution to track product availability to ensure products are always in stock, even across different physical stores and online marketplaces. If a product is frequently sold out, you can adjust production levels or help retailers to improve their inventory management.

    Key Takeaways: E-commerce Price Monitoring

    Price monitoring software allows you to compare your products’ prices with competitors. This valuable data can help you:

    • Optimize revenue through timely price changes and dynamic pricing
      Avoid being undercut by competitors
    • Improve pricing strategies and promotions to increase sales and retain customers
    • Maintain consistent prices across sales channels

    To learn more, check out our article, What is Competitive Pricing Intelligence: The Ultimate Guide here or reach out and talk to us today!

  • Cracking the Code: How Retailers Can Adapt to Plummeting Egg Prices in 2024

    Cracking the Code: How Retailers Can Adapt to Plummeting Egg Prices in 2024

    Virtually every cuisine in the world uses eggs. They’re in your breakfast, lunch, dinner, and dessert — which is perhaps why the global egg market is expected to generate $130.70 billion in revenue in 2024 and is projected to grow to approximately $193.56 billion by 2029.

    More specifically, the United States is the fourth-largest egg producer worldwide. The country’s egg market is projected to generate $15.75 billion in 2024 and increase to $22.51 billion by 2029.

    This growth is driven by several factors, most notably:

    • Health-consciousness among consumers: Consumers value eggs for their essential nutrients and rich protein content.
    • Demand for convenience foods: Consumers’ preferences are shifting toward quick and easy foods, which drives demand for shell eggs and pre-packaged boiled or scrambled eggs.
    • Population Growth: A growing worldwide population increases the demand for eggs.
    • Affordability and accessibility: Eggs are an affordable and accessible nutrient-dense food source for many.

    Despite these factors contributing to the U.S. egg market’s growth, recent times have seen egg prices fall dramatically.

    Based on a sample of 450 SKUs, DataWeave discovered that egg prices in the U.S. fell by 6.7% between April 2023 and April 2024, dipping to its lowest (-12.6%) in December 2023.

    Egg Price Chart: Egg Prices USA Going Down 98.95% between April 2023 and April 2024

    So, what’s causing the decrease in egg prices?

    The Rise and Fall of Egg Prices: A Recent History

    In 2022, avian influenza severely impacted the United States. The disease affected wild birds in nearly every state and devastated commercial flocks in approximately half of the country.

    The 2022 incident was the first major outbreak since 2015 and led to the culling of more than 52.6 million birds, mainly poultry, to prevent the disease from spreading uncontrollably.

    With almost 12 million fewer egg-laying hens, the United States produced around 109.5 billion eggs in 2022 — a drop of nearly two billion from the previous year.

    Consequently, the cost of eggs soared, peaking at $4.82 a dozen — more than double the price of eggs in the previous year.

    The avian flu continues to affect egg-laying hens and other poultry birds across the United States. As of April 2024, farms have killed a total of 85 million poultry birds in an attempt to contain the disease.

    Despite the disease’s effects, production facilities have made significant efforts to repopulate flocks, leading to a steady increase in supply – and a much anticipated decrease in egg prices.

    According to the U.S. Bureau of Labor Statistics, there was an increase in producer egg prices in 2022, reaching a peak in November 2022, at which point they began to fall.

    Retailer’s egg prices followed suit. The egg price chart below depicts retailers’ declining egg prices over one year, from April 2023 to April 2024, with Giant Eagle showing the most significant price reductions and Walmart the least.

    Egg Price Chart Featuring Leading Retailers 2023-2024

    What Does the Future Hold for Egg Prices?

    The USDA reported recent severe avian flu outbreaks in June 2024. These outbreaks are estimated to have affected 6.23 million birds.

    With a reduction in egg-laying hens, egg prices are likely to increase — time will tell.

    Nonetheless, the annual per capita consumption of eggs in the U.S. is projected to reach 284.4 per person in 2024 from 281.3 per person in 2023. So for now, producers and retailers can rest assured of the growing demand for eggs.

    How Can Retailers Adapt to the Unpredictability of Egg Prices?

    Egg prices were down to $2.69 for a dozen in May 2024. However, they are still significantly higher than consumers were used to just a few years ago—eggs were, on average, $1.46 a dozen in early 2020.

    Additionally, while the avian flu puts pressure on producers, inflation and supply chain disruptions exert pressure on retailers.

    With such challenging egg market conditions, what can retailers do to maintain customer loyalty amid reduced consumer spending while maintaining profitability?

    1. Give the Customer What They Want: Increase Offerings of Organic, Cage-Free, and Free-Range Eggs

    As mentioned, Data Bridge Market Research’s trends and forecast report highlighted a significant increase in consumer health consciousness. Additionally, animal welfare increasingly influences consumers’ purchasing decisions when buying meat and dairy products.

    DataWeave data shows that the prices of organic, cage-free, and free-range eggs—such as those by brands like Happy Eggs and Marketside—have fallen less than those of non-organic, caged egg brands.

    Egg Price Chart Featuring Leading Egg Brand Prices 2023-2024

    2. Increase Private-Label Offerings

    Private labels typically offer retailers higher margins than national brands. These margins can shield consumers from sudden wholesale egg price swings, helping to preserve brand trust and consumer loyalty without sacrificing profitability.

    Moreover, eggs are particularly suited to private labeling, given their uniform appearance and taste and the lack of product innovation opportunities.

    Undoubtedly, this is why sales of private-label eggs dwarf sales of national egg brands in the United States. Statista reports that across three months in 2024, private label egg sales amounted to $1.55 billion U.S. dollars, while the combined sales of the top nine national egg brands totaled just $617.88 million U.S. dollars.

    3. Price Intelligently

    With the current and predicted fluctuations in egg prices over the foreseeable future, price competitiveness is paramount to margin management and customer loyalty.

    This is especially true when lower prices are the primary factor influencing the average consumer’s choice of supermarket for daily essentials purchases.

    AI-driven pricing intelligence tools like DataWeave give retailers valuable highly granular and reliable insights on competitor pricing and market dynamics. In today’s data-motivated environment, these insights are necessary for competitiveness and profitability.

    Final Thoughts

    Egg prices have fluctuated significantly due to the impact of avian flu. Despite recent price drops, future egg price increases are possible due to ongoing outbreaks. Retailers should adapt to unstable egg prices by increasing organic, free-range, cage-free, and private-label egg offerings while leveraging AI-driven pricing tools to maintain margins and customer loyalty.

    Speak to us today to learn more!

  • How Healthy is Your Assortment?

    How Healthy is Your Assortment?

    In 2025, both consumers and retailers continue to prioritize better health – albeit with evolving definitions and expectations.

    The pandemic fundamentally transformed how consumers approach wellness, with this shift becoming entrenched in shopping behaviors years later. As shopping habits have permanently altered, retailers now face increased pressure to rapidly adapt their assortments with in-demand health and wellness products that enhance customer experience across various channels – online and offline.

    Let’s explore how leading retailers are keeping consumers – and their own bottom lines – healthy by responding effectively to market trends to drive online sales and market share.

    Health & Wellness Influence The Product Mix Across Categories

    Consumption habits have changed dramatically since the onset of the pandemic. A McKinsey study shows that 82% and 73% of US, and UK consumers respectively now consider health & wellness a top priority. Typically shoppers adjust grocery shopping and meal planning at the start of the year, with many focusing on fresh, organic, and nutrient-rich foods.

    The influential health and wellness mega-trend spans diverse retail channels, including grocery, pharmacy and mass. It extends across numerous categories like:

    • Food and beverage (natural, organic, vegan, plant-based food)
    • Health and personal care
    • Beauty
    • Cleaning products
    • Fitness equipment 
    • Athleisure (apparel)
    • Consumer electronics like health wearables.

    Today’s health movement is so powerful and compelling that retailers have revised their business strategies to better serve health-conscious consumers. For instance, drugstores are reinventing themselves as healthcare destinations, with CVS and Kroger expanding into personalized care delivery and value-based clinics to enhance their health offerings.

    Major retailers like Amazon, Walmart, and Target report robust sales in health and wellness categories. For example, Walmart saw a 4.6% increase in comparable sales in early 2024, driven significantly by grocery, consumables, and health-related products.

    New product categories are gaining traction:

    • Functional foods and beverages are seeing unprecedented growth, with Target launching over 2,000 wellness items in the category, including exclusive products priced under $10.
    • Personalized nutrition and mental health products are surging, including tailored dietary solutions and stress-reducing items.
    • Health wearables and wellness tech continue to rise in popularity, with over 150 new wellness tech items launched at Target this year, including innovative red-light therapy devices.
    • Transparency and sustainability certifications like organic, non-GMO, and vegan labels are increasingly driving purchasing decisions.
    • Clinically proven benefits offered by health & wellness products are gaining traction among Gen Z.

    Retail’s Survival Of The Fittest Moves Online

    As the omnichannel retail sector continues to grow, more shoppers now make purchase decisions within minutes using just a few clicks rather than physically visiting brick-and-mortar stores. In some cases, AI agents like Operator from Chat-GPT or Gemini (Google’s Chatbot) even make personalized, curated lists and reduce the time taken to make purchase decisions. Traditional retail paradigms are rapidly becoming obsolete as consumers grow savvier, more empowered, and better informed than ever before.

    To stay competitive, more retailers are embracing AI-driven data insights to adjust their assortments to reflect consumer demand for health and wellness products.

    According to industry experts, data insights have emerged as a critical retail strategy that continues to gain momentum. This is because retailers can no longer afford to guess how to approach their omnichannel strategy. They need the accuracy, clarity, and efficiency of data insights to guide their assortment and pricing decisions to outmaneuver competitors, maximize sales, and win market share as shopping evolves online.

    Among its retail best practices, Bain & Company recommends retailers “lead with superior assortments that use a customer-centric lens to reduce complexity and increase space for the products customers love.” Insights can help retailers discover the optimal mix of national brands, private labels, limited-time offers, and value-added bundles.

    “Lead with superior assortments …
    increase space for the products consumers love    “
    ~ Bain & Company

    Determining the optimal mix of products also includes bestsellers and unique items that help retailers distinguish their offerings. Assortment insights help retail executives track competitors’ assortment changes and spot gaps in their own product assortment to adapt to emerging consumer trends and in-demand products.

    Why Effective Assortment Planning Matters

    Assortment planning sits at the heart of retail success, directly influencing profitability, customer satisfaction, and competitive differentiation. In today’s health-conscious market, getting your assortment right means:

    • Meeting Customer Expectations: Today’s health-conscious consumers expect relevant, high-quality products that match their wellness goals. A well-planned assortment signals that a retailer understands its customers’ evolving needs.
    • Optimizing Inventory Investment: Strategic assortment planning ensures capital is allocated to products with the highest return potential while minimizing investments in slow-moving items.
    • Creating Competitive Advantage: A distinctive assortment that includes popular health and wellness products alongside unique offerings helps retailers stand out in a crowded marketplace.
    • Reducing Lost Sales: Effective assortment planning minimizes the risk of stockouts on high-demand health and wellness items, preventing customers from shopping elsewhere.
    • Supporting Omnichannel Strategies: Well-executed assortment planning ensures consistency across physical and digital touchpoints, creating a seamless customer experience.
    • Improving Operational Efficiency: A thoughtfully curated assortment reduces complexity throughout the supply chain, from procurement to warehouse management to in-store operations.

    As health and wellness continues to drive consumer spending, retailers who excel at assortment planning can capitalize on these trends more effectively than their competitors, turning market insights into tangible business results.

    AI-Powered Assortment Analytics Driving Retail Success

    The synergy of AI and data analytics into retail assortment planning is changing how businesses approach inventory management. Retailers using AI-driven predictive analytics have achieved a 36% SKU reduction while increasing sales by 1-2%, showcasing the efficiency of data-driven approaches according to a McKinsey report.

    Retailers face several challenges that can hinder strategic assortment planning:

    • Limited Understanding of Competition: Retailers struggle to gain comprehensive insights into their product assortments relative to competitors, often lacking visibility into their strengths and weaknesses across categories.
    • Data Overload: Assortment planning involves handling vast amounts of data, making it challenging for category managers to extract actionable insights without user-friendly tools and visualization.
    • Cross-Channel Consistency: With omnichannel retailing, ensuring consistency across physical stores, e-commerce, and other channels is complex. Misalignment can lead to customer dissatisfaction and loss of loyalty.
    • Adapting to Changing Market Trends: Identifying top-selling products and tracking consumer preferences is challenging. Balancing the right mix of products is crucial; without analytics, retailers risk lost sales or excess slow-moving inventory.
    • Scalability and Efficiency: As retailers expand into new markets or categories, scaling their assortment planning processes efficiently becomes a challenge. Legacy systems and manual methods often fail to support the agility needed for quick decision-making at scale.

    DataWeave’s Assortment Analytics helps retailers address these challenges by providing a robust, easy-to-use platform that delivers actionable insights into product assortments and competitive positioning. With AI-driven, contextual insights and alerts, retailers can effortlessly identify high-demand, unique products, capitalize on catalog strengths, optimize pricing and promotions, improve stock availability, and refine assortments to maintain a competitive edge.

    Beyond Data: Actionable Insights That Drive Results

    DataWeave’s platform provides a comprehensive, insight-led view into assortments through several key dimensions:

    • Stock Insights: Monitor stock changes across retailers to stay updated on availability.
    • Category and Sub-Category Insights: Analyze assortment changes, identify newly introduced or discontinued categories, and track leading retailers in specific segments.
    • Brand Insights: Identify newly introduced, missing, or discontinued brands, as well as leading brands within chosen categories.
    • Product Insights: Identify bestsellers and evaluate their impact on your portfolio, analyzing pricing and promotions.
    • Personalized Recommendations: Receive suggestions tailored to your behavior and user profile to refine decision-making.
    • User-Configured Alerts: Stay informed with alerts designed to highlight significant changes or opportunities.

    The platform addresses data overload by providing an intuitive, insight-driven view of your assortment. Category managers gain a comprehensive, bird’s-eye perspective of key changes within specified timeframes, allowing them to focus on what matters most.

    Preparing for the Future of Retail Health

    To avoid supply chain bottlenecks, inventory shortages, and out-of-stock scenarios, retailers are strategically using data insights to anticipate fluctuations in demand and proactively plan how to manage disruptions that could affect their assortments.

    For variety that satisfies consumers’ diverse product needs, retailers are using data insights to determine whether to collaborate with nimble suppliers to promptly fill any gaps.

    To further strengthen their assortments’ attractiveness, retailers are using AI-powered pricing analytics to offer the right product at the right price. These analytics help retailers know exactly how they compare to rivals’ pricing moves with relevant data so they can keep up with market fluctuations and stay competitive by earning consumer engagement, sales, and trust.

    To Conclude…

    Like nourishing habits that improve consumers’ health, data insights improve retailers’ e-commerce health. Advanced assortment and pricing analytics, powered by artificial intelligence, help retailers make better decisions faster to boost their agility, outmaneuver rivals, and fuel online growth.

    In a retail landscape where consumer preferences for health and wellness continue to evolve rapidly, the retailers who thrive will be those who leverage data and AI to understand, anticipate, and meet these changing demands with the right products at the right time. Reach out to us to know more.

  • Using Siamese Networks to Power Accurate Product Matching in eCommerce

    Using Siamese Networks to Power Accurate Product Matching in eCommerce

    Retailers often compete on price to gain market share in high performance product categories. Brands too must ensure that their in-demand assortment is competitively priced across retailers. Commerce and digital shelf analytics solutions offer competitive pricing insights at both granular and SKU levels. Central to this intelligence gathering is a vital process: product matching.

    Product matching or product mapping involves associating identical or similar products across diverse online platforms or marketplaces. The matching process leverages the capabilities of Artificial Intelligence (AI) to automatically create connections between various representations of identical or similar products. AI models create groups or clusters of products that are exactly the same or “similar” (based on some objectively defined similarity criteria) to solve different use cases for retailers and consumer brands.

    Accurate product matching offers several key benefits for brands and retailers:

    • Competitive Pricing: By identifying identical products across platforms, businesses can compare prices and adjust their strategies to remain competitive.
    • Market Intelligence: Product matching enables brands to track their products’ performance across various retailers, providing valuable insights into market trends and consumer preferences.
    • Assortment Planning: Retailers can analyze their product range against competitors, identifying gaps or opportunities in their offerings.

    Why Product Matching is Incredibly Hard

    But product matching stands out as one of the most demanding technical processes for commerce intelligence tools. Here’s why:

    Data Complexity

    Product information comes in various (multimodal) formats – text, images, and sometimes video. Each format presents its own set of challenges, from inconsistent naming conventions to varying image quality.

    Data Variance

    The considerable fluctuations in both data quality and quantity across diverse product categories, geographical regions, and websites introduce an additional layer of complexity to the product matching process.

    Industry Specific Nuances

    Industry specific nuances introduce unique challenges to product matching. Exact matching may make sense in certain verticals, such as matching part numbers in industrial equipment or identifying substitute products in pharmaceuticals. But for other industries, exactly matched products may not offer accurate comparisons.

    • In the Fashion and Apparel industry, style-to-style matching, accommodating variants and distinguishing between core sizes and non-core sizes and age groups become essential for accurate results.
    • In Home Improvement, the presence of unbranded products, private labels, and the preference for matching sets rather than individual items complicates the process.
    • On the other hand, for grocery, product matching becomes intricate due to the distinction between item pricing and unit pricing. Managing the diverse landscape of different pack sizes, quantities, and packaging adds further layers of complexity.

    Diverse Downstream Use Cases

    The diverse downstream business applications give rise to various flavors of product matching tailored to meet specific needs and objectives.

    In essence, while product matching is a critical component in eCommerce, its intricacies demand sophisticated solutions that address the above challenges.

    To solve these challenges, at DataWeave, we’ve developed an advanced product matching system using Siamese Networks, a type of machine learning model particularly suited for comparison tasks.

    Siamese Networks for Product Matching

    Our methodology involves the use of ensemble deep learning architectures. In such cases, multiple AI models are trained and used simultaneously to ensure highly accurate matches. These models tackle NLP (natural language processing) and Computer Vision challenges specific to eCommerce. This technology helps us efficiently narrow down millions of product candidates to just 5-15 highly relevant matches.

    The Tech Powering Siamese Networks

    The key to our approach is creating what we call “embeddings” – think of these as unique digital fingerprints for each product. These embeddings are designed to capture the essence of a product in a way that makes similar products easy to identify, even when they look slightly different or have different names.

    Our system learns to create these embeddings by looking at millions of product pairs. It learns to make the embeddings for similar products very close to each other while keeping the embeddings for different products far apart. This process, known as metric learning, allows our system to recognize product similarities without needing to put every product into a rigid category.

    This approach is particularly powerful for eCommerce, where we often need to match products across different websites that might use different names or images for the same item. By focusing on the key features that make each product unique, our system can accurately match products even in challenging situations.

    How Siamese Networks Work?

    Imagine having a pair of identical twins who are experts at spotting similarities and differences. That’s essentially what a Siamese network is – a pair of identical AI systems working together to compare things.

    How it works:

    • Twin AI systems: Two identical AI systems look at two different products.
    • Creating ‘fingerprints’ or ‘embedding’: Each system creates a unique ‘fingerprint’ of the product it’s looking at.
    • Comparison: These ‘fingerprints’ are then compared to see how similar the products are.

    Architecture

    The architecture of a Siamese network typically consists of three main components: the shared network, the similarity metric, and the contrastive loss function.

    • Shared Network: This is the ‘brain’ that creates the product ‘fingerprints’ or ‘embeddings.’ It is responsible for extracting meaningful feature representations from the input samples. This network is composed of layers of neural units that work together. Weight sharing between the twin networks ensures that the model learns to extract comparable features for similar inputs, providing a basis for comparison.
    • Similarity Metric: After the shared network processes the inputs, a similarity metric is employed. This decides how alike two ‘fingerprints’ or ‘embeddings’ are. The selection of a similarity metric depends on the specific task and characteristics of the input data. Frequently used similarity metrics include the Euclidean distance, cosine similarity, or correlation coefficient, each chosen based on its suitability for the given context and desired outcomes.
    • Loss Function: For training the Siamese network, a specialized loss function is used. This helps the system improve its comparison skills over time. It guides and trains the network to generate akin embeddings for similar inputs and disparate embeddings for dissimilar inputs.

      This is achieved by imposing penalties on the model when the distance or dissimilarity between similar pairs surpasses a designated threshold, or when the distance between dissimilar pairs falls below another predefined threshold. This training strategy ensures that the network becomes adept at discerning and encoding the desired level of similarity or dissimilarity in its learned embeddings.

    How DataWeave Uses Siamese Networks for Product Matching

    At DataWeave, we use Siamese Networks to match products across different retailer websites. Here’s how it works:

    Pre-processing (Image Preparation)

    • We collect product images from various websites.
    • We clean these images up to make them easier for our AI to understand.
    • We use techniques like cropping, flipping, and adjusting colors to help our AI recognize products even if the images are slightly different.

    Training The AI

    • We show our AI system millions of product images, teaching it to recognize similarities and differences.
    • We use a special learning method called “Triplet Loss” to help our AI understand which products are the same and which are different.
    • We’ve tested different AI structures to find the one that works best for product matching, including ResNet, EfficientNet, NFNet, and ViT. 

    Image Retrieval 

    • Once trained, our AI creates a unique “fingerprint” for each product image.
    • We store these fingerprints in a smart database.
    • When we need to find a match for a product, we:
      • Create a fingerprint for the new product.
      • Quickly search our database for the most similar fingerprints.
      • Return the top matching products.

    Matches are then assigned a high or a low similarity score and segregated into “Exact Matches” or “Similar Matches.” For example, check out the image of this white shoe on the left. It has a low similarity score with the pink shoe (below) and so these SKUs are categorized as a “Similar Match.” Meanwhile, the shoe on the right is categorized as an “Exact Match.”

    Similarly, in the following image of the dress for a young girl, the matched SKU has a high similarity score and so this pair is categorized as an “Exact Match.”

    Siamese Networks play a pivotal role in DataWeave’s Product Matching Engine. Amid the millions of images and product descriptions online, our Siamese Networks act as an equalizing force, efficiently narrowing down millions of candidates to a curated selection of 10-15 potential matches. 

    In addition, these networks also find application in several other contexts at DataWeave. They are used to train our system to understand text-only data from product titles and joint multimodal content from product descriptions.

    Leverage Our AI-Driven Product Matching To Get Insightful Data

    In summary, accurate and efficient product matching is no longer a luxury – it’s a necessity. DataWeave’s advanced product matching solution provides brands and retailers with the tools they need to navigate this complex landscape, turning the challenge of product matching into a competitive advantage.

    By leveraging cutting-edge technology and simplifying it for practical use, we empower businesses to make informed decisions, optimize their operations, and stay ahead in the ever-evolving eCommerce market. To learn more, reach out to us today!

  • Why Strategic Competitive Insights Are Key to Optimizing Your Product Assortment

    Why Strategic Competitive Insights Are Key to Optimizing Your Product Assortment

    For retailers, the breadth and relevance of their product assortment are critical for success. Amid a crowded market filled with countless products clamoring for consumer attention, retailers must find innovative ways to distinguish themselves. While pricing undeniably impacts purchasing decisions, the diversity and distinctiveness of a retailer’s product range can provide a crucial competitive advantage.

    Creating an attractive and profitable assortment that resonates with your target audience requires more than intuition; it demands deep insights into both your own and your competitors’ offerings. A well-curated assortment aligned with current trends can drive higher conversions and foster customer loyalty. However, achieving this perfect balance is a formidable challenge without the right insights.

    This is where a data-driven strategy becomes essential, enabling you to curate a product mix that captivates and converts.

    However, retailers often encounter significant challenges when attempting to strategically plan their assortments:

    • Limited Competitive Insights: Gaining a clear understanding of your competitors’ assortment strengths and weaknesses across various categories is challenging. Without this visibility, it’s difficult to know where you have an edge or where you might be falling behind.
    • Tracking Demand Patterns: Identifying top-sellers and monitoring shifts in consumer demand can be a struggle. Without the ability to easily detect trends or changes in demand, you risk missing opportunities to stock trending items.

    Attempting to navigate these challenges manually is not only arduous but also susceptible to substantial errors.

    How Assortment Analytics Solutions Help

    The ideal Assortment Analytics solution must offer a fact-based approach to:

    • Identify Strengths and Weaknesses: Understand how your assortment measures up against the competition.
    • Stay Trend-Responsive: Keep your product mix fresh and aligned with the latest consumer trends.
    • Boost Conversions: Create a relatively unique, customer-focused assortment that enhances conversions.

    Many retailers attempt to analyze competitor assortments using manual, in-house methods, which inevitably leads to significant blind spots:

    • Variations in product classifications and taxonomies across competitors make meaningful comparisons challenging.
    • Gathering complete and accurate data across a vast competitive landscape is difficult.
    • Inconsistent titles and descriptions hinder reliable product matching without AI assistance.
    • Capturing and comparing detailed product attributes efficiently is nearly impossible without advanced tools.

    To overcome these challenges, retailers need a scalable, accurate Assortment Analysis solution designed specifically for the complexities of modern retail needs.

    DataWeave’s Assortment Analytics Solution

    DataWeave addresses these challenges by providing retailers with a robust platform to gain actionable insights into their product assortments and the competitive landscape. Leveraging advanced analytics and AI-driven algorithms, Assortment Analytics empowers retailers to make informed assortment management decisions, optimize their product offerings, and stay competitive.

    Armed with our insights, retailers can lead with their strengths and stock unique and in-demand products in their assortment. Further, by recognizing the strengths in their product catalog, they can craft effective pricing strategies and optimize their logistics, creating a more competitive and appealing shopping experience for their customers. Here are a few capabilities of DataWeave’s solution:

    In-Depth Competitive Analysis Across Retailers

    The solution offers detailed competitive analysis, revealing insights into competitors’ assortments. It maps competitor products to a common taxonomy, making comparisons accurate and meaningful. Retailers can visualize assortments at granular levels like category, sub-category, and product type.

    The data for these insights is collected at configurable intervals, typically monthly or quarterly, and is consumed not only via dashboard summaries but also raw data files to enable in-depth analysis. Retailers have the flexibility to choose specific competitors, brands, products, and categories for tracking, allowing for a tailored and strategic approach to assortment optimization.

    Brand and Category Views to Assess Your Portfolio

    The solution provides a comprehensive evaluation of your product assortment through brand and category views. In brand views, your portfolio is assessed against competitors at the brand level, highlighting:

    • Newly Introduced Brands: Insights into recently introduced brands, revealing shifts in the brand landscape.
    • Absence or Limited Presence: Identification of brands lacking representation or with minimal presence compared to competitors, indicating areas for improvement.
    • Strong Presence and Exclusivity: Recognition of brands where you excel, including exclusive offerings, showcasing your competitive edge.

    Identifying Top-Selling Competitive Products To Boost Assortment Strategy

    Beyond just comparing assortment numbers, the DataWeave solution surfaces insights into which competitor products are actually performing well. It equips category and assortment managers with indicators that assess competitor products in terms of their popularity and shelf velocity.

    It analyzes metrics like pricing fluctuations, ratings, customer reviews, search rankings, and replenishment rates to pinpoint hot sellers you may want to stock. With these insights, merchandizing managers can pinpoint top-selling products among competitors, enabling informed decisions to enhance their assortment in comparison.

    Sophisticated Attribute Tagging and Analysis

    Using AI-powered attribute tagging, the solution simplifies granular product analysis within specific categories. An Apparel retailer, for instance, can filter the data to compare assortments based on attributes like material, pattern, color, etc.

    Retailers can select attributes relevant to their products and gain detailed insights. These custom filter attributes dynamically populate the panel, facilitating targeted data exploration. Category and merchandizing managers can delve into critical details swiftly, enabling strategic decision-making and comprehensive competitive analysis within their categories.

    You can capitalize on opportunities by stocking in-demand, on-trend items and address assortment gaps quickly. At the same time, you can double down on your strengths by enhancing your exclusive or top-performing product sets.

    In summary, DataWeave’s Assortment Analytics solution provides an invaluable competitive edge. The insights enable evidence-based decisions to attract more customers, encourage bigger baskets, and maximize the value of every assortment choice.

    To learn more, read our detailed product guide here or get on a exploratory call with one of our experts today!

  • Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    In today’s expansive omnichannel commerce landscape, pricing intelligence has become indispensable for retailers seeking to stay competitive and refine their pricing strategies. The sheer magnitude of eCommerce, spanning thousands of websites, billions of SKUs, and various form factors, adds layers of complexity. Consequently, ensuring the accuracy and reliability of competitive insights presents a formidable challenge for retailers aiming to leverage pricing data effectively.

    At the core of any robust pricing intelligence system lies product matching. This process enables retailers to recognize identical or similar products across competitors. Once these matches are identified, tracking prices is a relatively more straightforward task, facilitating ongoing analysis and informed decision-making.

    Accurate matching is crucial for meaningful price comparisons and tailoring product assortments. The challenge is matching products is often complicated, especially for non-local brands, niche categories, or items lacking consistent global identifiers. It becomes even trickier when trying to match very similar but not identical products. A comprehensive approach that compares and analyzes multiple attributes like product titles, descriptions, images and more is essential.

    Artificial intelligence algorithms are commonly used to automate product matching, leveraging machine learning techniques to analyze patterns in images and text data. While AI can adapt and improve over time, the question remains: Can it fully address the complexities of product matching on its own?

    The reality is that many retailers still struggle with incomplete, inaccurate, or outdated product data, despite these AI-powered product matching solutions. This can lead to suboptimal pricing decisions, missed opportunities, and reduced competitiveness.

    Challenges in an ‘AI-only’ Approach to Product Matching

    While AI plays a vital role in automated product matching solutions, there are complexities that AI alone cannot fully address:

    Subjectivity in Matching Criteria

    Some product categories have subjective or hard-to-quantify criteria for determining similarity. AI learns from historical data, so it may struggle with nuanced aspects like:

    Aesthetics, style, and design: In the Fashion and Jewellery vertical, for example, products are matched according to attributes like style, aesthetics, design – all of which have some subjectivity involved.

    Quantity/packaging variations: In the grocery sector, variations in product packaging and quantities can introduce complexities that require subjective decision-making. For example, apples may be sold in different packaging like a 0.5 kg bag or a pack of 4 individual apples. Determining if these different packaging options should be considered equivalent often involves making a qualitative judgment call, rather than a clear-cut objective decision.

    Matching product sets: For categories like home furnishings, the focus is often on matching coordinated sets rather than individual items. For example, in the bedroom category, matching may involve grouping together an entire set of complementary furniture like a bed frame, dresser, and wardrobe based on their cohesive design and style. This goes beyond simply making one-to-one product associations, requiring more nuanced judgments about aesthetic coordination.

    Contextual Factor

    Products can have regional preferences, cultural differences, or evolving trends that impact how they are matched. AI may miss important context like Local/regional product names or distinct brand names across countries.

    For instance, in the image we see Sprite (in the US) is branded Xubei in China. Continuous human curation is needed to help AI adapt to this context.

    High Accuracy & Coverage Expectations

    Retailers rely on AI powered and automated pricing adjustments based on product matching for insight. To ensure that pricing recommendations and updates are accurate, accurate product matching is crucial. For this, simply identifying similar top results is not enough – the process must comprehensively capture all relevant matches. While AI excels at finding the top groupings with around 80% accuracy, even small matching errors can have significant consequences.

    As AI matching improves, customer expectations may rise even higher. If AI achieves 90% accuracy, for instance, SLAs may demand over 95%. Reaching such a high level of accuracy is very challenging for AI alone, especially when faced with incomplete data, contextual nuances, evolving trends, and subjective matching criteria across products and categories.

    The solution is to combine the power of AI with human expertise. This is the key to achieving true data veracity – the accuracy, freshness, and comprehensive coverage required for precise and reliable product matching.

    Human-in-the-Loop Approach for Elevated Product Matching

    Human intelligence and quality testing can elevate the AI powered product matching process by addressing key challenges:

    • Matching Validation: AI algorithms may identify product matches with 80-90% accuracy initially. Having humans validate these AI-suggested matches allows for correcting errors and pushing the accuracy close to 100%. As humans flag issues, provide context, and re-label incorrect predictions, it allows the AI model to learn and enhance its reliability for complex, high-stakes decisions.
    • Applying Contextual Judgment: For subjective matching criteria like aesthetics, design, and categorizing product sets, human discernment is needed. Humans can make nuanced judgments beyond just quantitative rules, ensuring meaningful apples-to-apples product comparisons. Their contextual understanding augments AI’s capabilities.
    • Continuous Learning Via Feedback Loop: Product experts possess rich category knowledge across markets. Integrating this human insight through an iterative feedback loop helps AI models quickly learn and adapt to changing trends, preferences, and context. As humans explain their match assessments, the AI continuously enhances its precision over time.

    By combining AI’s automation and scale with human validation, judgment, and knowledge curation, pricing intelligence solutions can achieve the accuracy and coverage demanded for actionable competitive pricing insights.

    DataWeave’s Data Veracity Framework: A Scalable Workflow Combining AI and Human Expertise

    Given the vast number of products, retailers, and brands that exist today, any product matching solution must be highly scalable. At DataWeave, we bring you such a scalable workflow to address these complexities by integrating human expertise with AI-driven automation. The image below outlines our approach for combining AI with human intelligence in a seamless, scalable workflow for accurate product matching:

    Retailers and brands can benefit in several ways with this workflow, as listed below.

    Several Rounds of Data Verification Due to Hierarchical Validation Teams

    The workflow employs a hierarchical validation team of Leads and Executives to efficiently integrate human expertise without creating bottlenecks. Verification Leads play a pivotal role in managing the distribution of product matches identified by DataWeave’s AI model to the Verification Executives.

    The Executives then meticulously validate these AI-suggested matches, adding any missing product associations and removing inaccurate matches. After validation, the matched product groups are sent back to the Leads, who perform random sampling checks to ensure quality.

    Throughout this entire workflow, feedback and suggestions are continuously gathered from both the Executives and Leads. This curated input is then incorporated back into DataWeave’s AI model, allowing it to learn and improve its matching accuracy on an ongoing basis.

    This hierarchical structure ensures that human validation seamlessly scales alongside the AI’s matching capabilities. Leveraging the respective strengths of AI automation and human expertise in an iterative feedback loop prevents operational bottlenecks while steadily elevating overall accuracy.

    Confidence-based Distribution of Matched Articles for Validation

    The AI model assigns confidence scores, differentiating high-confidence (>95%) and low-confidence matches. For high-confidence groups, executives simply remove incorrect matches – a quicker process. Low-confidence matches require more human effort in adding/removing matches.

    As the AI model improves over time with feedback, the share of high-confidence matches increases, making validation more efficient and swift.

    Automated, Standardized Process with Iterative Feedback Loop

    The entire workflow is standardized and automated, with verification metrics seamlessly tracked. At each step, feedback captured from both leads and executives flows back into the AI, enhancing its matching accuracy and coverage iteratively.

    DataWeave’s closed-loop system of AI automation with hierarchical human validation allows product matching to achieve comprehensive accuracy at a vast scale.

    Unleash the Power Accurate and Comprehensive Product Matching

    In summary, combining AI and human expertise in product matching is crucial for retailers navigating the complexities of omnichannel retail. While AI algorithms excel in automation, they often struggle with subjective criteria and contextual nuances. DataWeave’s approach integrates AI-driven automation with human validation, delivering the industry’s most accurate product matching capabilities, enabling actionable competitive pricing insights.

    To learn more, reach out to us today!

  • How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    As eCommerce increasingly becomes a prolific channel of sales for consumer brands, they find that maintaining a consistent and trustworthy brand image is a constant struggle. In an ecosystem filled with dozens of marketplaces and hundreds of third-party merchants, ensuring that customers see what aligns with a brand’s intended image is quite tricky. With many fakes and counterfeit products doing the rounds, brands may further struggle to get the right representation.

    One way brands can track and identify inconsistencies in their brand representation across marketplaces is to use Digital Shelf Analytics solutions like DataWeave’s – specifically the Content Audit module.

    This solution uses advanced AI models to identify image similarities and dissimilarities compared with the original brand image. Brands could then use their PIM platform or work with the retailer to replace inaccurate images.

    But here’s the catch – AI can’t always accurately predict all the differences. Relying solely on scores given by these models poses a challenge in tracking the subtle differences between images. Often, image pairs with seemingly high match scores fail to catch important distinctions. Fake or counterfeit products and variations that slip past the AI’s scrutiny can lead to significant inaccuracies. Ultimately, it puts the reliability of the insights that brands depend on for crucial decisions at risk, impacting both top and bottom lines.

    Dealing with this challenge means finding a balance between the number-based assessments of AI models and the human touch needed for accurate decision-making. However, giving auditors the ability to pinpoint variations precisely goes beyond simply sharing numerical values of the match scores with them. Visualizing model-generated scores is important as it provides human auditors with a tangible and intuitive understanding of the differences between two images. While numerical scores are comparable in the relative sense, they lack specificity. Visual interpretation empowers auditors to identify precisely where variations occur, aiding in efficient decision-making.

    How AI-Powered Image Scoring Works

    At DataWeave, our approach involves employing sophisticated computer vision models to conduct extensive image comparisons. Convolutional Neural Network (CNN) models such as Resnet-50 or YOLO, in conjunction with feature extraction models, analyze images quantitatively. This AI-powered image scoring process yields scores that indicate the level of similarity between images.

    However, interpreting these scores and understanding the specific areas of difference can be challenging for human auditors. While computer vision models excel at processing vast amounts of data quickly, translating their output into actionable insights can be a stumbling block. A numerical score may not immediately convey the nature or extent of the differences between images

    In the assessment of these images, all fall within the 70 to 80 range of scores (out of a maximum of 100). However, discerning the nature of differences—whether they are apparent or subtle—poses a challenge for the AI models and human auditors. For example, there are differences in the placement or type of images in the packaging, as well as packing text that are often in an extremely small font size. It is, of course, possible for human auditors to identify the differences in these images, but it’s a slow, error-prone, and tiring process, especially when auditors often have to check hundreds of image pairs each day.

    So how do we ensure that we identify differences in images accurately? The answer lies in the process of visual highlighting.

    How Visual Highlighting Works

    Visual highlighting is a method that enhances our ability to comprehend differences in images by combining sophisticated algorithms with human understanding. Instead of relying solely on numerical scores, this approach introduces a visual layer, resembling a heatmap, guiding human auditors to specific areas where discrepancies are present.

    Consider the scenario depicted in the images above: a computer vision model assigns a score of 70-85 for these images. While this score suggests relatively high similarity, it fails to uncover major differences between the images. Visual highlighting comes into play to overcome this limitation, precisely indicating regions where even subtle differences are seen.

    Visual highlighting entails overlaying compared images and emphasizing areas of difference, achieved through techniques like color coding, outlining, or shading specific regions. The significance of the difference in a particular area determines the intensity of the visual highlight.

    For instance, if there’s a change in the product’s color or a discrepancy in the packaging, these variations will be visually emphasized. This not only streamlines the auditing process but also enables human evaluators to make well-informed decisions quickly.

    Benefits of Visual Highlighting

    • Intuitive Understanding: Visual highlighting offers an intuitive method for interpreting and acting upon the outcomes of computer vision models. Instead of delving into numerical scores, auditors can concentrate on the highlighted areas, enhancing the efficiency and accuracy of the decision-making process.
    • Accelerated Auditing: By bringing attention to specific regions of concern, visual highlighting speeds up the auditing process. Human evaluators can swiftly identify and address discrepancies without the need for exhaustive image analysis.
    • Seamless Communication: Visual highlighting promotes clearer communication between automated systems and human auditors. Serving as a visual guide, it enhances collaboration, ensuring that the subtleties captured by computer vision models are effectively conveyed.

    The Way Forward

    As technology continues to evolve, the integration of visual highlighting methodologies is likely to become more sophisticated. Artificial intelligence and machine learning algorithms may play an even more prominent role in not only detecting differences but also in refining the visual highlighting process.

    The collaboration between human auditors and AI ensures a comprehensive approach to maintaining brand integrity in the ever-expanding digital marketplace. By visually highlighting differences in images, brands can safeguard their visual identity, foster consumer trust, and deliver a consistent and reliable online shopping experience. In the intricate dance between technology and human intuition, visual highlighting emerges as a powerful tool, paving the way for brands to uphold their image with precision and efficiency.

    To learn more, reach out to us today!


    (This article was co-authored by Apurva Naik)

  • How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    Retailers heavily depend on pricing intelligence solutions to consistently achieve and uphold their desired competitive pricing positions in the market. The effectiveness of these solutions, however, hinges on the quality of the underlying data, along with the coverage of product matches across websites.

    As a retailer, gaining complete confidence in your pricing intelligence system requires a focus on the trinity of data quality:

    • Accuracy: Accurate product matching ensures that the right set of competitor product(s) are correctly grouped together along with yours. It ensures that decisions taken by pricing managers to drive competitive pricing and the desired price image are based on reliable apples-to-apples product comparisons.
    • Freshness: Timely data is paramount in navigating the dynamic market landscape. Up-to-date SKU data from competitors enables retailers to promptly adjust pricing strategies in response to market shifts, competitor promotions, or changes in customer demand.
    • Product matching coverage: Comprehensive product matching coverage ensures that products are thoroughly matched with similar or identical competitor products. This involves accurately matching variations in size, weight, color, and other attributes. A higher coverage ensures that retailers seize all available opportunities for price improvement at any given time, directly impacting revenues and margins.

    However, the reality is that untimely data and incomplete product matches have been persistent challenges for pricing teams, compromising their pricing actions. Inaccurate or incomplete data can lead to suboptimal decisions, missed opportunities, and reduced competitiveness in the market.

    What’s worse than poor-quality data? Poor-quality data masquerading as accurate data.

    In many instances, retailers face a significant challenge in obtaining comprehensive visibility into crucial data quality parameters. If they suspect the data quality of their provider is not up to the mark, they are often compelled to manually request reports from their provider to investigate further. This lack of transparency not only hampers their pricing operations but also impedes the troubleshooting process and decision-making, slowing down crucial aspects of their business.

    We’ve heard about this problem from dozens of our retail customers for a while. Now, we’ve solved it.

    DataWeave’s Data Statistics and SKU Management Capability Enhances Data Transparency

    DataWeave’s Data Statistics Dashboard, offered as part of our Pricing Intelligence solution, enables pricing teams to gain unparalleled visibility into their product matches, SKU data freshness, and accuracy.

    It enables retailers to autonomously assess and manage SKU data quality and product matches independently—a crucial aspect of ensuring the best outcomes in the dynamic landscape of eCommerce.

    Beyond providing transparency and visibility into data quality and product matches, the dashboard facilitates proactive data quality management. Users can flag incorrect matches and address various data quality issues, ensuring a proactive approach to maintaining the highest standards.

    Retailers can benefit in several ways with this dashboard, as listed below.

    View Product Match Rates Across Websites

    The dashboard helps retailers track match rates to gauge their health. High product match rates signify that pricing teams can move forward in their pricing actions with confidence. Low match rates would be a cause for further investigation, to better understand the underlying challenges, perhaps within a specific category or competitor website.

    Our dashboard presents both summary statistics on matches and data crawls as well as detailed snapshots and trend charts, providing users with a holistic and detailed perspective of their product matches.

    Additionally, the dashboard provides category-wise snapshots of reference products and their matching counterparts across various retailers, allowing users to focus on areas with lower match rates, investigate underlying reasons, and develop strategies for speedy resolution.

    Track Data Freshness Easily

    The dashboard enables pricing teams to monitor the timeliness of pricing data and assess its recency. In the dynamic realm of eCommerce, having up-to-date data is essential for making impactful pricing decisions. The dashboard’s presentation of freshness rates ensures that pricing teams are armed with the latest product details and pricing information across competitors.

    Within the dashboard, users can readily observe the count of products updated with the most recent pricing data. This feature provides insights into any temporary data capture failures that may have led to a decrease in data freshness. Armed with this information, users can adapt their pricing decisions accordingly, taking into consideration these temporary gaps in fresh data. This proactive approach ensures that pricing strategies remain agile and responsive to fluctuations in data quality.

    Proactively Manage Product Matches

    The dashboard provides users with proactive control over managing product matches within their current bundles via the ‘Data Management’ panel. This functionality empowers users to verify, add, flag, or delete product matches, offering a hands-on approach to refining the matching process. Despite the deployment of robust matching algorithms that achieve industry-leading match rates, occasional instances may arise where specific matches are overlooked or misclassified. In such cases, users play a pivotal role in fine-tuning the matching process to ensure accuracy.

    The interface’s flexibility extends to accommodating product variants and enables users to manage product matches based on store location. Additionally, the platform facilitates bulk match uploads, streamlining the process for users to efficiently handle large volumes of matching data. This versatility ensures that users have the tools they need to navigate and customize the matching process according to the nuances of their specific product landscape.

    Gain Unparalleled Visibility into your Data Quality

    With DataWeave’s Pricing Intelligence, users gain the capability to delve deep into their product data, scrutinize match rates, assess data freshness, and independently manage their product matches. This approach is instrumental in fostering informed and effective decisions, optimizing inventory management, and securing a competitive edge in the dynamic world of online retail.

    To learn more, reach out to us today!

  • Capturing and Analyzing Retail Mobile App Data for Digital Shelf Analytics: Are Brands Missing Out?

    Capturing and Analyzing Retail Mobile App Data for Digital Shelf Analytics: Are Brands Missing Out?

    Consumer brands around the world increasingly recognize the vital role of tracking and optimizing their digital shelf KPIs, such as Content Quality, Share of Search, Availability, etc. These metrics play a crucial role in boosting eCommerce sales and securing a larger online market share. With the escalating requirements of brands, the sophistication of top Digital Shelf Analytics providers is also on the rise. Consequently, the adoption of digital shelf solutions has become an essential prerequisite for today’s leading brands.

    As brands and vendors continue to delve further and deeper into the world of Digital Shelf Analytics, a significant and often overlooked aspect is the analysis of digital shelf data on mobile apps. The ability of solution providers to effectively track and analyze this mobile-specific data is crucial.

    Why is this emphasis on mobile apps important?

    Today, the battle for consumer attention unfolds not only on desktop web platforms but also within the palm of our hands – on mobile devices. As highlighted in a recent Insider Intelligence report, customers will buy more on mobile, exceeding 4 in 10 retail eCommerce dollars for the first time.

    Moreover, thanks to the growth of delivery intermediaries like Instacart, DoorDash, Uber Eats, etc., shopping on mobile apps has received a tremendous organic boost. According to an eMarketer report, US grocery delivery intermediary sales are expected to reach $68.2 billion in 2025, from only $8.8 billion in 2019.

    In essence, mobile is increasingly gaining share as the form factor of choice for consumers, especially in CPG. In fact, one of our customers, a leading multinational CPG company, revealed to us that it sees up to 70% of its online sales come through mobile apps. That’s a staggering number!

    The surge in app usage reflects a fundamental change in consumer behavior, emphasizing the need for brands to adapt their digital shelf strategies accordingly.

    Why Brands Need To Look at Apps and Desktop Data Differently

    Conventionally, brands that leverage digital shelf analytics rely on data harnessed from desktop sites of online marketplaces. This is because capturing data reliably and accurately from mobile apps is inherently complex. Data aggregation systems designed to scrape data from web applications cannot easily be repurposed to capture data on mobile apps. It requires dedicated effort and exceptional tech prowess to pull off in a meaningful and consistent way.

    In reality, it is extremely important for brands to track and optimize their mobile digital shelf. Several digital shelf metrics vary significantly between desktop sites and mobile apps. These differences are natural outcomes of differences in user behavior between the two form factors.

    One of these metrics that has a huge impact on a brand’s performance on retail mobile apps is their search discoverability. Ecommerce teams are well aware of the adverse impact of the loss of even a few ranks on search results.

    Anyone can easily test this. Searching something as simple as “running shoes” on the Amazon website and doing the same on its mobile app shows at least a few differences in product listings among the top 20-25 ranks. There are other variances too, such as the number of sponsored listings at the top, as well as the products being sponsored. These variations often result in significant differences in a brand’s Share of Search between desktop and mobile.

    Share of Search is the share of a brand’s products among the top 20 ranked products in a category or subcategory, providing insight into a brand’s visibility on online marketplaces.

    Picture a scenario in which a brand heavily depends on desktop digital shelf data, confidently assuming it holds a robust Share of Search based on reports from its Digital Shelf Analytics partner. However, unbeknownst to the team, the Share of Search on mobile is notably lower, causing a detrimental effect on sales.

    To fully understand the scale of these differences, we decided to run a small experiment using our proprietary data analysis and aggregation platform. We restricted our analysis to just Amazon.com and Amazon’s mobile app. However, we did cover over 13,000 SKUs across several shopping categories to ensure the sample size is strong.

    Below, we provide details of our key findings.

    Share of Search on The Digital Shelf – App Versus Desktop

    Our analysis focused on three popular consumer categories – Electronics, CPG, and Health & Beauty.

    In the electronics category, brands like Apple, Motorola, and Samsung, known for their mobile phones, earbuds, headphones, and more, have a higher Share of Search on the Amazon mobile app compared to the desktop.

    Meanwhile, Laptop brands like Dell, Acer, and Lenovo, as well as other leading brands like Google have a higher Share of Search on the desktop site compared to the app. This is the scenario that brands need to be careful about. When their Share of Search on mobile apps is lower, they might miss the chance to take corrective measures since they lack the necessary data from their provider.

    In the CPG category, Ramen brand Samyang, with a lot of popularity on Tiktok and Instagram, shows a higher Share of Search on Amazon’s mobile app. Speciality brands like 365 By Whole Foods, pasta and Italian food brands La Moderna, Divinia, and Bauducco too have a significantly higher Share of Search on the app.

    Cheese and dessert brands like Happy Belly, Atlanta Cheesecake Company, among others, have a lower Share of Search on the mobile app. Ramen brand Sapporo is also more easily discovered on Amazon’s desktop site. Here, we see a difference of more than 5% in the Share of Search of some brands, which is likely to have a huge impact on the brand’s mobile eCommerce sales levels and overall performance.

    Lastly, in the Health & Beauty category, Shampoos and hair care brands like Olaplex, Dove, and Tresemme exhibited a higher Share of Search on the mobile app compared to the desktop.

    On the other hand, body care brands like Neutrogena and Hawaiian Tropic, as well as Beardcare brand Viking Revolution displayed a higher Share of Search on Amazon’s desktop site.

    Based on our data, it is clear that there are several examples of brands that do better in either one of Amazon’s desktop sites or mobile apps. In many cases, the difference is stark.

    So What Must Brands Do?

    Our findings emphasize the imperative for brands to move beyond a one-size-fits-all approach to digital shelf analytics. The striking variations in Share of Search between mobile apps and desktops conclusively demonstrate that relying solely on desktop data for digital shelf optimization is inadequate.

    If brands see that they’re falling behind on the mobile digital shelf, there are a few things they can do to help boost their performance:

    • If a brand’s Share of Search is lower on the mobile app, they can divert their retail spend to mobile in order to inorganically compensate for this difference. This way, any short-term impact due to lower discoverability is mitigated. This is also likely to result in optimized budget allocation and ROAS.
    • Brands also need to ensure their content is optimized for the mobile form factor, with images that are easy to view on smaller screens, and tailored product titles that are shorter than on desktops, highlighting the most important product attributes from the consumer’s perspective. Not only will this help brands gain more clicks from mobile shoppers, but this will also gradually lead to a boost in their organic Share of Search on mobile.
    • CPG brands, specifically, need to optimize their digital shelf for delivery intermediary apps (along with marketplaces). The grocery delivery ecosystem is booming with companies like DoorDash, Delivery Hero, Uber Eats, Swiggy, etc. leading the way. Using Digital Shelf Analytics to optimize performance on delivery apps is quite an involved process with a lot of bells and whistles to consider. Read our recently published whitepaper that specifically details how brands can successfully boost their visibility and conversions on delivery apps.

    But first, brands need to identify and work with a Digital Shelf Analytics partner that is able to capture and analyze mobile app data, enabling tailored optimization approaches for all eCommerce platforms.

    DataWeave leads the way here, providing the world’s most comprehensive and sophisticated digital shelf analytics solution, rising above all other providers to provide digital shelf insights for both web applications and mobile apps. Our data aggregation platform successfully navigates the intricacies of capturing public data accurately and reliably from mobile apps, thereby delivering a comprehensive cross-device view of digital shelf KPIs to our brand customers.

    So reach out to us today to find out more about our digital shelf solutions for mobile apps!

  • Why Unit of Measure Normalization is Critical For Accurate and Actionable Competitive Pricing Intelligence

    Why Unit of Measure Normalization is Critical For Accurate and Actionable Competitive Pricing Intelligence

    Competitive pricing intelligence is pivotal for retailers seeking to analyze their product pricing in relation to competitors. This practice is essential for ensuring that their product range maintains a competitive edge, meeting both customer expectations and market demands consistently.

    Product matching serves as a foundational element within any competitive pricing intelligence solution. Products are frequently presented in varying formats across different websites, featuring distinct titles, images, and descriptions. Undertaking this process at a significant scale is highly intricate due to numerous factors. One such complication arises from the fact that products are often displayed with differing units of measurement on various websites.

    The Challenge of Varying Units

    In certain product categories, retailers often offer the same item in varying volumes, quantities, or weights. For instance, a clothing item might be available as a single piece or in packs of 2 or 3, while grocery brands commonly sell eggs in counts of 6, 12, or 24.

    Consider this example: a quick glance might suggest that an 850g pack of Kellogg’s Corn Flakes priced at $5 is a better deal than a 980g pack of Nestle Cornflakes priced at $5.2. However, this assumption can be deceptive. In reality, the latter offers better value for your money, a fact that only becomes evident through price comparisons after standardizing the units.

    This issue is particularly relevant due to the prevalence of “shrinkflation,” where brands adjust packaging sizes or quantities to offset inflation while keeping prices seemingly low. When quantities, pack sizes, weight, etc. reduce instead of prices increasing, it’s important that this change is considered while analyzing competitive pricing.

    Normalizing Units of Measure

    In order to effectively compare prices among different competitors, retailers must standardize the diverse units of measurement they encounter. This standardization (or normalization) is crucial because price comparisons should extend beyond individual product SKUs to accommodate variations in package sizes and quantities. It’s essential to normalize units, ranging from “each” (ea) for individual items to “dozen” (dz) for sets, and from “pounds” (lb), “kilograms” (kg), “liters” (ltr), to “gallons” (gal) for various product types.

    For example, a predetermined base unit of measure, such as 100 grams for a specific product like cornflakes, serves as the reference point. The unit-normalized price for any cornflake product would then be the price per 100 grams. In the example provided, this reveals that Kellogg’s is priced at $0.59 per 100 grams, while Nestle is priced at $0.53 per 100 grams.

    Various Categories of Unit Normalization

    1. Weight Normalization

    Retailers frequently feature products with weight measurements expressed in grams (g), kilograms (kg), pounds (lbs), or ounces (oz).

    2. Quantity or Pack Size Normalization

    Products are also often featured with varying pick sizes or quantities in each SKU.

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    3. Volume or Capacity Normalization

    Products can also vary in volumes or capacities with units like liters (L) or fluid ounces (fl oz).

    DataWeave’s Unit Normalized Pricing Intelligence Solution

    DataWeave’s highly sophisticated product matching engine can match the same or similar products and normalize their units of measurement, leading to highly accurate and actionable competitive pricing insights. It standardizes different units of measurement, like weight, quantity, and volume, ensuring fair comparisons across similar and exact matched products.

    Retailers have the flexibility to view pricing insights either with retailer units or normalized units. This capability empowers retailers and analysts to perform accurate, in-depth analyses of pricing information at a product level.

    In some scenarios, analyzing unit normalized pricing reflects pricing trends and competitiveness more accurately than retail price alone. This is particularly true for categories like CPG, where products are sold in diverse units of measure. For instance, in the example shown here, we can view a comparison of price position trends for the category of Fruits and Vegetables based on both retail price and unit price.

    The difference is striking: the original retail price based analysis shows a stagnation in price position, whereas unit normalized pricing analysis reflects a more dynamic pricing scenario.

    With DataWeave, retailers can specify which units to compare, ensuring that comparisons are made accurately. For example, a retailer can specify that unit price comparisons apply only to 8, 12, or 16-ounce packs, as well as 1 or 3-pound packs, but not to 10 and 25-pound bags. This precision ensures that products are matched correctly, and prices are represented for appropriately normalized units, leading to more accurate pricing insights.

    To learn more about this capability, write to us at contact@dataweave.com or visit our website today!

  • From Data to Dollars: How Digital Shelf Analytics Drives Tangible Business Impact and ROI for Brands

    From Data to Dollars: How Digital Shelf Analytics Drives Tangible Business Impact and ROI for Brands

    For consumer brands, the digital marketplace presents an unparalleled landscape of opportunities for engaging with consumers and expanding their market presence. Within this dynamic environment, Digital Shelf Analytics has emerged as a crucial pillar in a brand’s eCommerce strategy. This technology provides valuable insights into a brand’s organic and paid visibility on marketplaces, content quality, pricing strategies, promotional efforts, and product availability. These insights help brands gain a comprehensive understanding of their competitive positioning and overall market performance.

    Nevertheless, many brands often grapple with the question of whether this understanding translates into tangible actions that drive real business impact and return on investment (ROI). This uncertainty stems from a lack of clarity about the direct correlation between digital shelf insights and key metrics such as enhanced sales conversions.

    Nonetheless, there is compelling evidence that when these insights are effectively harnessed and strategic actions are taken, brands can realize significant, measurable benefits.

    So, the question arises: does Digital Shelf Analytics genuinely deliver on its promises?

    At DataWeave, we’ve partnered with numerous brands to fuel their eCommerce growth through the application of digital shelf analytics. In this article, we will delve into these insights, uncovering the concrete and quantifiable results that brands can achieve through their investments in digital shelf analytics.

    Digital Shelf KPIs and Their Impact

    Digital Shelf Analytics is a robust system that analyzes specific key performance indicators (KPIs) about the digital shelf, furnishing brands with precise recommendations to not only bolster these KPIs but also to monitor the enhancements over time. The following is a brief explanation of digital shelf KPis and their expected impact areas:

    Product Availability: Ensuring Shoppers Never Hear “Out of Stock” Again

    Timely insights on the availability of products ensures brands reduce replenishment times at scale, which can significantly impact sales, creating an unbreakable link between product availability and revenue. With Digital Shelf Analytics, procurement and replenishment teams can set up notifications to promptly identify low or out-of-stock items and take swift action. This can also be done for specific ZIP codes or individual stores. In addition, availability plays a crucial role in a brand’s Share of Search and search rankings, as online marketplaces often ensure only in-stock products are shown among the top ranks.

    Share of Search: Dominating the Digital Aisles

    If a product isn’t visible, does it even exist? In fact, 70% of consumers never go beyond the first page of search results on major online marketplaces. Therefore, as a brand, the visibility of your products for relevant search keywords and their appearance on the first page can heavily determine your awareness metrics. This is where the concept of Share of Search comes into play. Think of it as securing prime shelf space in a physical store. Digital shelf insights and benchmarking with category leaders for Share of Search help ensure your products command relevant attention on the digital shelf.

    Content Quality: Crafting the Perfect Product Story

    Creating engaging product descriptions and visuals is akin to giving your products a megaphone in a crowded marketplace. By enhancing content quality, including product names, titles, descriptions, and images, brands can climb the search result rankings, leading to increased visibility and subsequently, more sales.

    Ratings and Reviews: The Power of Social Proof

    Public opinion holds immense sway. Research indicates that a single positive review can trigger a 10% surge in sales, while a multitude of favorable reviews can propel your product to a 44% higher trajectory. The correlation between ratings and sales is not surprising—each step up the rating ladder can translate to substantial revenue growth.

    While it’s reasonable to anticipate a connection between these KPIs and downstream impact metrics such as impressions, clicks, and conversions, we were driven to explore this correlation through the lens of real-world data. To do so, we meticulously monitored the digital shelf KPIs for one of our clients and analyzed the improvements in these metrics.

    It’s essential to acknowledge that not all observed impact areas can be solely attributed to enhancements in digital shelf KPIs. Still, it’s evident that a robust correlation exists. The following section presents an in-depth case study, shedding light on the results of this analysis.

    A Success Story: Real-World Impact of Digital Shelf Analytics

    Let’s dive into the journey of one of our clients – a prominent CPG brand specializing in the sale of baked goods and desserts. Through their experience, we will illustrate the transformative impact of our DataWeave Digital Shelf Analytics product suite.

    Over a period of one year, from August 2022 to July 2023, the brand leveraged several key modules of Digital Shelf Analytics for Amazon, including Share of Search, Share of Category, Availability, Ratings and Reviews, and Content Audit. Each of these digital shelf KPIs played a vital role in shaping the brand’s performance across various stages of the buyer’s journey.

    The buyer’s journey is typically delineated into three key stages:

    • Awareness: At this stage, shoppers peruse multiple product options presented on search and category listing pages, gaining an initial understanding of the available choices.
    • Consideration: Here, shoppers narrow down their selections and evaluate a handful of products, moving closer to a purchase decision.
    • Conversion: In this final stage, shoppers make their ultimate product choice and proceed to complete the purchase.

    Let’s now examine the data to understand how digital shelf KPIs helped drive tangible ROI on Amazon for the brand across the stages of the buyer journey.

    Stage 1: Raising Awareness

    Enhancing Share of Search and Share of Category can help brands boost product visibility and raise brand awareness. The following chart demonstrates the steady, incremental improvements in our client’s Share of Search and Share of Category (in the top 20 ranks of each listing page) throughout the analyzed period. These enhancements were achieved through various measures, including product sponsorship, content enhancement, price optimization, promotional initiatives, and more.

    This amplified Share of Search and Share of Category directly translates into improved product discoverability, as evident from the surge in impressions depicted in the chart below.

    Stage 2: All Things Considered

    In the consideration stage, shoppers make their product selections by clicking on items that meet their criteria, which may include factors like average rating, number of ratings, price, product title, and images. For brands, this underscores the importance of crafting meticulously detailed product content and accumulating a substantial number of ratings.

    The subsequent chart illustrates the year-long trend in both average ratings and the number of ratings, both of which have displayed steady improvement over time.

    The enhancements in the number of ratings and the average rating have a direct and positive impact on product consideration. This, in turn, has led to a noticeable year-over-year increase in page views, as indicated in the chart below.

    These improvements are likely to have also been influenced by the overall enhancement of content quality, which is detailed separately in the section below.

    Stage 3: Driving Decisions

    As buyers progress to the next stage, they reach the pivotal point of making a purchase decision. This decision is influenced by multiple factors, including product availability, content quality, and the quality of reviews, reflecting customer sentiment.

    Our client effectively harnessed our Availability insights, significantly reducing the likelihood of potential out-of-stock scenarios and enhancing replenishment rates, as highlighted in the chart below. The same chart also indicates improvements in content quality, measured by the degree to which the content on Amazon aligns with the brand’s ideal content standards.

    Below, you’ll find the year-over-year growth in conversion rates for the brand on Amazon. This metric stands as the ultimate measure of business impact, directly translating into increased revenue for brands.

    As the data uncovers, growth in key digital shelf KPIs cumulatively had a strong correlation with impressions, page views, and conversion rates.

    It is also important to note that the effect of each KPI cannot be viewed in isolation, since they are often interdependent. For example, improvement in content and availability could boost Share of Search. Accurate content could also influence more positive customer feedback. Brands need to consider optimizing digital shelf KPIs holistically to create sustained business impact.

    Impact on eCommerce Sales

    After the implementation of digital shelf analytics, the results spoke for themselves. Sales consistently outperformed the previous year’s records month after month. As shown in the chart below, the diligent application of DataWeave’s recommendations paved the way for an impressive 8.5% year-over-year increase in sales, leaving an indelible mark on the brand’s eCommerce success.

    From boosting product visibility to catapulting conversion rates, Digital Shelf Analytics serves as the key to unlocking unparalleled online success.

    While the success story detailed above does not establish a direct causation between Digital Shelf Analytics and sales revenue, there is undoubtedly a strong correlation. It’s evident that digital shelf KPIs play a pivotal role in optimizing a brand’s eCommerce performance across all stages of the buyer journey. Hence, for brands, it is vital that they collaborate with the right partner and harness digital shelf insights to fine-tune their eCommerce strategies and tactics.

    That said, the eCommerce landscape is in a constant state of flux, and there is still much to learn about how each digital shelf KPI influences brand performance in the online realm. With more data and an increasing number of brands embracing Digital Shelf Analytics, it’s only a matter of time before a direct causation is firmly established.

    Reach out to us today to know more about how your brand can leverage Digital Shelf Analytics to drive higher sales and market share in eCommerce.

  • Revolutionizing Fuel Pricing: How Fuel Retailers and Convenience Stores Can Gain a Winning Edge with DataWeave

    Revolutionizing Fuel Pricing: How Fuel Retailers and Convenience Stores Can Gain a Winning Edge with DataWeave

    Consider this scenario: A retailer establishes its fuel prices using pricing data that’s a few days old, only to subsequently discover that a nearby competitor is offering substantially lower prices. The result? Lost customers, decreased foot traffic, and diminished sales. This serves as a stark reality that retailers must confront and address today.

    In the fiercely competitive realm of retail, where every decision holds weight, maintaining a competitive edge is paramount. The fuel category, frequently underestimated, has the potential to significantly impact a retailer’s revenue stream. This challenge is not unique; retailers worldwide, particularly in North America, grapple with a common hurdle: mastering the intricate art of real-time fuel pricing.

    The Quest For Reliable, Real-Time Fuel Pricing Data

    For retailers, traditional methods for procuring and analyzing fuel price data have proven to be both expensive and error-prone, often relying on manual data collection or third-party data providers. These outdated approaches yield frustrating delays, inaccuracies, and missed opportunities. When it comes to obtaining timely fuel pricing intelligence, the majority of fuel retailers grapple with three central challenges:

    • Low Accuracy: Ensuring that fuel pricing information remains up-to-date, dependable, and actionable, even when sourced from complex web-based platforms.
    • Less Coverage: Acquiring comprehensive data that encompasses all of North America, spanning across retailers, convenience stores, fuel stations, and beyond.
    • High Cost: Effectively managing the substantial costs associated with acquiring and processing this vital information.

    DataWeave’s Fuel Pricing Intelligence Solution

    Comprehensive, accurate, and real-time fuel pricing intelligence can play a huge role in the profitability of retailers throughout North America. DataWeave takes the forefront in delivering this transformative Data-as-a-Service (DaaS) solution to some of the most prominent retailers in the region, including the top 20 fuel retail behemoths.

    With a rich and extensive history spanning over a decade in the realm of competitive intelligence, DataWeave boasts an impressive track record of empowering well-informed decision-making in retail. We leverage state-of-the-art technology to bring an unparalleled level of accuracy, timeliness, and coverage to fuel pricing intelligence.

    The following are some compelling advantages offered by our solution:

    Accurate and Real-Time First Party Data

    We deliver retailers an unparalleled advantage through real-time, first-party fuel price data. Our data originates directly from the retailer’s own channels, encompassing websites and mobile apps, rendering it the industry’s foremost and most reliable source.

    Imagine having access to fuel pricing information that updates as frequently as every 30 minutes. This rapid update cadence guarantees that you, as a retailer, constantly possess the latest pricing insights at your fingertips, empowering you to respond swiftly to market fluctuations and competitor manoeuvres. Our comprehensive data spans a wide spectrum of fuel types, including:

    • Gasoline: Be it regular, mid-grade, super, premium, ethanol-free, ethanol blends, methanol blends, or reformulated gasoline, we have got you covered.
    • Diesel: Our data encompasses biodiesel, biodiesel off-road, biodiesel blends, biodiesel ultra-low sulfur (ULS), diesel ultra-low sulfur (ULS), diesel off-road, standard diesel, and premium diesel.

    Armed with our real-time, first-party data, you can make pricing decisions with unwavering confidence, secure in the knowledge that you possess access to the most current, authoritative, and extensive fuel pricing intelligence in North America.

    The data points we capture directly from relevant web sources include: gas station postal code, store name and code, location, city, state, ZIP code, fuel type, competitor name, regular price, member price (if available), time and date of data capture, and more.

    Click here if you wish to access a sample report of our fuel pricing data.

    Unrivaled Geographical Coverage

    Our extensive coverage of fuel data spans over 30,000 ZIP codes and encompasses the top 100 retailers across the western, mid-western, and eastern regions of the United States.

    Retailers benefit from the flexibility to configure and tailor the solution to their precise needs, whether it involves adding more locations or selectively acquiring specific segments of the data. This far-reaching coverage guarantees that retailers, whether situated in bustling urban centers or remote areas, can readily access the essential data required to maintain their competitive edge.

    Moreover, if you currently source your fuel pricing data from alternative providers, our solution seamlessly integrates, amplifies, and complements your existing array of data sources, ensuring a harmonious and unified approach to data acquisition.

    Optimization of Dynamic Pricing Strategies

    In the world of retail, the importance of timing cannot be overstated. Even a mere difference of a few cents can translate into millions of dollars in revenue impact. With DataWeave, retailers gain the capability to make data-driven decisions that provide them with a competitive edge around the clock, every single day.

    Our platform empowers you to unearth margin gaps by pinpointing opportunities to raise prices while maintaining your competitive pricing position. It also identifies instances where you may be substantially overpriced, prompting necessary price adjustments to ensure competitiveness within the market. All these valuable insights are available at a hyperlocal level, facilitating pricing efficiency and optimization across your various regions of coverage. Equipped with this real-time data, you can swiftly adapt to ever-changing market conditions.

    Furthermore, our comprehensive competitive data seamlessly integrates into your existing pricing systems through APIs, facilitating quick and informed pricing actions based on robust data.

    Reliable and Customer-First Tech Platform

    Our platform boasts a remarkable level of sophistication when it comes to data aggregation, normalization, visualization, and integration capabilities. It stands as a massively scalable system with the capacity to aggregate billions of data points daily, spanning thousands of web sources. This includes the intricate handling of sources like mobile apps and websites known for frequently altering their site structures, among others.

    What truly sets us apart is our proficiency in addressing these challenges through a blend of human expertise and large-scale machine learning. Additionally, our commitment to delivering unmatched service extends to round-the-clock, 24/7 support. This comprehensive approach makes our fuel pricing intelligence solution not only effective but also cost-efficient in meeting your fuel data requirements.

    We also provide a variety of options for you to consume our data, which includes receiving our reports via email, SFTP, S3 buckets, data lakes like Snowflake, and APIs.

    Enhance your Fuel Pricing Strategies with DataWeave

    In the ever-competitive world of retail, staying ahead is not just a goal; it’s a necessity. The fuel pricing landscape, often overlooked, holds immense power to impact a retailer’s profitability. DataWeave’s real-time, comprehensive, and accurate fuel pricing intelligence solution is the key to securing this advantage. Retailers and convenience stores now have a powerful platform at their disposal, offering unparalleled precision, comprehensive coverage, and the agility needed to navigate this landscape.

    Join the ranks of industry leaders who have already harnessed the potential of DataWeave. Reach out to us today to redefine your approach to fuel pricing and propel your business to new heights!

  • 5 Ways DataWeave Helps Brands Drive Growth With Amazon Ads

    5 Ways DataWeave Helps Brands Drive Growth With Amazon Ads

    Consumers are discovering and trialing new eCommerce marketplaces, brands and products at a faster rate than ever before, given the vast amount of choices encountered browsing for products online. A recent analysis shows how events like Amazon Prime Day, Black Friday, and Cyber Monday are especially fruitful for new-to-brand customer advertising, encouraging B2C marketers to increase their digital advertising spend to fuel product discovery, sales and market share for their brands.

    Amazon advertisers grow market share and brand loyalty with ecommerce intelligence
    DataWeave joins Amazon Advertising partner network

    The majority of eCommerce consumers are discovering products via relevant keywords attributable to their needs, with most clicks happening on page one results for the first few products listed. Simplifying the digital shopping experience is critical for brands to be in the consideration set for the majority of consumers who won’t venture past page one results. 

    An internal analysis conducted shows getting a product to page one on retailer websites can improve sales by as much as 50 percent, but figuring out the right levers to pull to get there organically—without paid advertising—is a real challenge, especially given fast-changing algorithms. While more than half of all retail related online browsing sessions are “organic”, sometimes brands need to boost their product visibility by investing in sponsored (paid) opportunities to improve a product’s rank.

    Data analytics can equip brands with intelligence to help them decide when, where, and how to make digital advertising investments profitably, while simultaneously acting on insights that help drive organic growth. Considering a majority of U.S. consumers begin their product discovery on marketplaces like Amazon, it makes sense for brands to prioritize digital advertising opportunities with Amazon.

    Maximize Return on Ad Spend (ROAS) with Amazon Ads

    Brands use Amazon Ads to drive brand awareness, acquire new customers, drive sales and gain market share, with the goal of furthering their marketing return on investment. Top performing advertisers average 40 percent greater year-on-year (YoY) sales growth, 50 percent greater YoY growth in customer product page viewership on Amazon, and 30 percent higher returns on ad spend (ROAS) with Amazon Ads, according to a recent analysis. Sponsored Products, Sponsored Brands, Amazon DSP and Sponsored Display are among the types of Amazon Ads options cited that produce maximum return.

    Ensuring your product listings appear at the top of page one results on Amazon for the most relevant discovery keywords is therefore the most important determinant for maximizing ROAS. DataWeave has become a vetted partner and measurement provider in the Amazon Advertising Partner Network, with the goal of supporting brands to optimize digital advertising campaigns by providing visibility to Digital Shelf Analytics (DSA) key performance indicators (KPIs), like Share of Search, Pricing and Product Availability, Content Audits, Ratings and Reviews, and Sales Performance and Market Share.

    Below is a summary of how our Digital Shelf solutions, in partnership with Amazon Ads, can improve the performance of your Amazon Ads campaigns

    1. Keyword Recommendations Improve Share of Search

    With the DataWeave Share of Search solution, brands can monitor their placement of both organic and paid discovery keywords relative to their competition. Once your keywords are determined, you are also provided a weighted Share of Search score that helps measure how well each keyword performs relative to product discoverability. Below is an example of insights you’d gain.

    Share of Keyword Search

    Brands can provide their own list of keywords to monitor, or through our Amazon Ads collaborative solution, learn which keywords are the “best” for them to measure in the realm of Amazon. Performance results are based on data that shows which keywords consumers are actually using when browsing online alongside other keywords brands request to measure. Users are able to see exactly which keywords are most popular, competitive (and even unexpected), and relevant at an Amazon Standard Identification Number (ASIN) level of granularity. 

    We can also estimate the degree of relevance and estimated traffic for the recommended keywords. Brands can then use these insights to adjust campaign strategies based on these parameters, which can boost product discoverability and rank visibility. A brand could assume people find its products by brand name, yet traffic insights may reveal a majority of people look for a generic product type before they end up buying that particular brand. 

    2. Content Audits Increase Discovery Relevancy Scores

    Strong product content is critical to succeeding on Amazon. Thorough, accurate, and descriptive content leads to better click through rates (CTR), conversion rates, more positive reviews, and fewer returns, which results in increased discoverability. DataWeave’s Content Audit solution reviews existing copy and images on a per-attribute basis to highlight any gaps essential to improving visibility, as seen in the example below.

    Content Analysis

    To further growth, it is equally as important that your product content aligns with your advertising strategy. With Amazon Ads partner add-on, our solution can also audit your content to measure how effectively you are incorporating Amazon Ads keywords into your product content to enhance discovery relevancy.

    3. Discover More Opportunities with Pricing and Product Availability Insights

    Quality content and keyword updates will only get you so far if your products are not consistently available and priced competitively. With DataWeave’s Pricing and Promotions and Product Availability modules, advertisers can monitor their selling prices and availability trends alongside their competitors to uncover more opportunities to incorporate into advertising campaigns, as seen in the Pricing and Promotions dashboard example below.

    Promotion Analysis

    Additionally, product targeting recommendations can be utilized to target a competitor’s ASIN that may be overpriced or that is having issues staying in stock. Alternatively, broaden your strategy to target specific brands, complementary products, or category listing pages.

    You can also create alerts on your own products to monitor when items are low on inventory or out of stock to ensure key products are consistently available when customers are shopping.

    4. Leverage Ratings and Reviews to Increase Conversion

    Product ratings and reviews are also a critical component to running a successful Amazon Ads campaign. A large number of reviews and a positive star rating will provide customers with the confidence to purchase, resulting in higher conversion rates. Conversely, negative feedback can have a detrimental impact, resulting in lost sales and wasted ad spend. DataWeave’s Ratings and Reviews module can help you monitor your reviews and extract attribute-level insights on your products. This information can then be utilized to further optimize your advertising strategy.

    If you see consistent feedback in your reviews on aspects of a product not meeting customer expectations, address them in your product content to prevent potential misplaced expectations. Alternatively, if customer reviews are raving about certain product features, ensure these are promoted and relevant keywords are populated throughout your descriptions and feature bullets. Below is an example of insights seen within the DSA Ratings & Reviews dashboard.

    Ratings and Reviews

    5. Correlate Digital Shelf KPIs to Sales Performance and Market Share

    The newest DSA module, Sales Performance and Market Share, provides SKU, sub-category, and brand-level sales and market share estimates on Amazon for brands and their competitors, via customer defined taxonomies, to easily benchmark performance results.

    This data can also be correlated with other Digital Shelf KPIs, like Content Audit and Product Availability, giving brands an easy way to check the effect of attribute changes and how they impact sales and market share. Similarly, brands can see how search rank, both organic and sponsored, affects sales and market share estimates.

    Understanding the correlation between your advertising campaigns and your Digital Shelf brand visibility will help you identify which areas to prioritize to drive sales and win more market share.

    Digital Shelf Insights Help Brands Win with Amazon Ads

    The need for access to flexible, actionable eCommerce insights is growing exponentially as a way to help brands drive growth, increase their Share of Voice, and to gain a competitive edge. As a result, more global brands are seeking Digital Shelf Analytics for access to near real-time marketplace changes and to develop data-driven growth strategies that leverage pricing, merchandising, and competitive insights at scale.

    By monitoring, measuring and analyzing key performance indicators (KPIs) like Sales Performance and Market Share, Share of Search, Content Audits, Product Availability, Pricing and Promotions and Ratings and Reviews alongside competitors, brands will know what actions to take to boost brand visibility, customer satisfaction, and online sales. 

    DataWeave’s acceptance into the Amazon Advertising Partner Network enables Amazon advertisers to effectively build their Amazon growth strategies and determine systems that enable faster and smarter advertising and marketing decision-making to optimize product discoverability and overall results.

    Connect with us now to learn how we can scale with your brand’s analytical needs, or for access to more details regarding our Amazon Ads Partnership or Digital Shelf solutions.

    UPDATED: Read the full press release here

  • Prime Day India 2022 – highlights from the 2 day annual shopping festival!

    Prime Day India 2022 – highlights from the 2 day annual shopping festival!

    Amazon India’s much-awaited annual two-day shopping event, Prime Day, kicked off with a bang on July 23rd & 24th this year & was one of the most successful Prime Day events yet! Amazon reported that more than 32,000 sellers saw their highest ever sales day during the event. Interestingly 70% of these sellers who received orders during Prime Day were based in Tier 2 cities in India, further validating how the post-pandemic eCommerce boom has spread across the country. Also, Indian exporters saw 50% business growth on Amazon on Prime Day as customers across markets like North America, Europe, Australia, and Japan continued to purchase Made In India products.
    It was a great 2 days for Indian sellers, but what about customers who were waiting in anticipation for the great deals typically offered on Prime Day? We dug into our data to take a look at the deals, discounts, and brands that shone bright on Prime Day in India.

    Methodology

    • In addition to Amazon IN, we also tracked Flipkart on 23 & 24th July 2022, on Prime Day.
    • Categories tracked – Electronics, Grocery, Fashion & Beauty.
    • We looked at Additional Discounts offered on Prime Day: Additional Discount is the extra discount on an item during Prime Day when compared to the Pre-Prime Day price. 
    • We also looked at Post Prime Day Discounts, which were the discounts offered after the 2-day event ended.

    Amazon v/s Flipkart – who offered better discounts?

    Prime Day discounts are legendary. And across the globe, during Prime Day retailers try and compete to see if they can offer better deals than Amazon. Forbes even published an article on the 36 Prime Day competitor sales that were way more enticing than what Amazon had to offer. In India, we wanted to see if Amazon’s homegrown rival Flipkart might give it a tough fight, so we tracked the volume of discounts across categories on both retailers. 

    Discounts on Amazon & Flipkart across categories
    Discounts on Amazon & Flipkart across categories
    • Out of the 4 categories we tracked, in spite of Prime Day, Amazon offered discounts higher than Flipkart in only 2 categories – Electronics & Beauty. 
    • … while Flipkart offered higher discounts than Amazon in the Grocery & Fashion category. For groceries, Flipkart offered a 3.2% additional discount v/s 2.2% on Amazon. However, in the Fashion category, the difference was marginal – 8.1% on Amazon v/s 8.6% on Flipkart
    • Post-event, both Amazon & Flipkart went back to the original pre-event prices. This made it clear that Flipkart was tracking and making price changes based on their closest competitor. It’s what smart eCommerce businesses do to stay ahead in the race. 
    • Interestingly, post-event, in the fashion category, not only did Amazon revert to the original pre-event price, they even increased prices by close to 2%.

    Let’s take a look at discounts across 4 categories & the Brands that WON in each category.

    From Electronics to Fashion, Beauty & Groceries, let’s deep dive into the data to see which products were highly discounted within each category and brands that sprinted ahead to win the race on Amazon on Prime Day 2022.

    ELECTRONICS

    Tech publication Gadgets360 reported on the biggest Smartphone deals right from Brands like Samsung, Redmi, Oppo, and more. There were some fab deals on earphones too with Boat taking the lead. We wanted to take a look at electronics on Amazon and see which products had the heaviest discounts & if discounts were more lucrative than Prime Day 2021

    Discounts on Electronics on Prime Day
    Discounts on Electronics on Prime Day
    • Amazon India released highlights from Prime Day and reported that Smartphones & Electronics were among the categories that saw the most success in terms of units sold.
    • From the 6 product categories we tracked within electronics, we saw the highest additional discounts on Smartwatches (13.4%), followed by Bluetooth headphones (10.5%)
    • TV, Smartphones, cameras, and laptops had an additional discount of between 3 – 5.5%

    ELECTRONICS Brands that had the highest Share of Search on Amazon during Prime Day

    Research shows that on Amazon, the first 3 products garner 64% of business generated. This is why it is critical for brands to appear in the top few listings when consumers are searching for products. Being on top helps shoppers find your brand with ease & increases the chances of a sale. 

    On Prime Day 2022, Amazon India reported that the top-selling consumer electronics brands were HP, Lenovo, Asus, and Boat to name a few. Our assumption is, these brands must’ve had a high Share of Search (SoS), which played a massive role in increasing sales, so we looked into our data to see which brands had the highest SoS against specific keywords related to electronics. 

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords
    • Our data aligned with what Amazon reported. HP had high sales, perhaps because they occupied the premium #1 spot in the laptop category with a 44% SoS! Simply put, this means of the 100 laptops that appeared on a page, against a search for the keyword laptop, 44 products were listed by HP! Consumers always gravitate towards buying products they can find with ease. 
    • Lenovo had a 32% SoS for Laptops. Asus at 14% 
    • The top selling smartphone brands reported by Amazon included OnePlus, Redmi, Samsung, Realme & iQOO – our data showed that 3 out of these 5 brands were in the top 5 listings on Prime Day! Redmi had a whopping 30% SoS against the keyword smartphone, Samsung at 15%, and iQOO at 5% – clear validation that a high SoS can positively impact sales.

    BEAUTY & GROOMING

    Now let’s look at discounts in the beauty & grooming category. 

    Discounts on Beauty Products on Prime Day
    Discounts on Beauty Products on Prime Day
    • The highest additional discounts were given on shampoos (9.3%), followed by Lipsticks (6.6%)
    • Shaving kits for men were at an additional discount of 3.4%. Hair gel at 4.9% & Face Masks at 4.3%

    BEAUTY Brands that had the highest Share of Search on Amazon during Prime Day

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords

    In the beauty category, Amazon India reported that top-selling brands included Head & Shoulders, Dove, Biotique, L’Oreal, Sugar Cosmetics, and Mamaearth to name a few. Once again, we looked into our data to see the sort of brand visibility & SoS each of these brands had.

    • All the top-selling brand’s Amazon reported on we noticed appeared in the top 5 search results. 
    • Head & Shoulders & Dove were the top 2 listings against the keyword Shampoo at 26% & 16% SoS respectively. Biotique came in at #5 with a 7% SoS
    • Bombay Shaving Company, Gillette, and Axe were the top grooming brands for men in the Shaving Kit category. 
    • Lakme made a clean sweep with a 19% SoS against the keyword lipstick, which speaks volumes, considering the aggressive competition from D2C beauty brands in India today.

    GROCERY

    According to the New eCommerce in India report by consulting firm Redseer, grocery has been a major contributor to the growth of ecommerce in India & Amazon Fresh used Prime Day to grab a larger piece of that pie! As part of the Prime Day sale, Amazon Fresh also pushed discounts on groceries, as well as fruits and vegetables. We tracked products that fell into the “snack” category, and here’s what we saw.

    Discounts on Snacks on Prime Day
    Discounts on Snacks on Prime Day
    • Given changing lifestyles & healthy food fads, it was no surprise that we saw the highest additional discounts were given on Healthy Snacks (3.2%) & Diet Food (2.7%)
    • Chocolates and chips saw much lower additional discounts at 1.2% each.
    • Drinks were additionally discounted by 2.5% during Prime Day.

    SNACK Brands that had the highest Share of Search on Amazon during Prime Day

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords
    • Cadbury had a 69% share of search against the keyword Chocolate, leaving some of its key competitors way behind. Amul had a 20% SoS, while Hershey’s was at just 4%. 
    • According to an article in the Economic times, YogaBar tripled sales in FY22, which is why we were not surprised to see the brand at #1 when users were searching for “Healthy Snacks” during Prime Day. YogaBar products typically enjoy high visibility year-round, which clearly helped with brand awareness on Amazon & sales.

    FASHION

    Amazon reported that Men’s t-shirts and polos, denims, Kurtis, tops, and dresses for women, designer wear, and clothing for kids were some of the most-loved fashion categories on Prime Day. We looked into our data to see the trends that emerged.

    Discounts on Fashion on Prime Day
    Discounts on Fashion on Prime Day
    • From the categories we tracked, women’s handbags had the highest additional discount (11.8%), followed by watches (9.1%)
    • Sneakers & jeans had additional discounts in the ballpark of 7% and sunglasses at 4.4%

    FASHION Brands that had the highest Share of Search on Amazon during Prime Day

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords
    • Some of the usual suspects made it to the top 5, but what really stood out for us were brands that popped up against the keyword Jeans. While Levi’s came in at #2 with an 11% SoS, 2 Private Label Amazon brands featured in the top 5! Symbol at 27% SoS and Inkast Denim at 9%
    • Against the keyword Handbag, Lavie had a massive lead at 38% v/s the #2 brand – Caprese, at 13%
    • Boat found a #2 spot against the keyword watches, racing way ahead of the age-old popular brand Fastrack at #5 with a 4% SoS.

    Conclusion

    Amazon Prime Day 2022 in India came to a successful close as shoppers across India discovered the joy of the 2 day celebration with the best deals, savings, new launches, and more. Prime members from 95% of pin codes in India made purchases, there were 1000’s of deals and 500+ new product launches from brand partners & sellers. Nearly 18% more sellers grossed sales over INR 1 crore, and close to 38% more sellers grossed sales of over 1 lakh vs Prime Day 2021. Local neighborhood shops that sell on Amazon witnessed 4x sales growth. And start-ups and brands under the Amazon Launchpad program witnessed a growth of 3x. All in all, a successful event for everyone involved! 

  • The Role of eCommerce in Sustainable Fashion

    The Role of eCommerce in Sustainable Fashion

    Today, environmental damage is rapidly occurring on a global scale. And there are many reasons and causes for this. Global warming is one, deforestation, over population are some others. The list is long. In a small way, the retail & clothing industry contributes to environmental damage too. The good news is that sustainable fashion addresses this issue. Sustainable clothing is designed using sustainable fabrics like organic cotton, hemp, and Pima cotton that have less of a negative impact on the planet. 

    sustainable clothing and its benefits
    Sustainable clothing and its benefits

    In this blog, we will discuss the rise of sustainable clothing and its benefits. We will also discuss marketplaces for sustainable fashion.

    Benefits of Sustainable Fashion

    a. Reduces carbon footprint

    The fashion industry emits numerous greenhouse gases annually. Most clothes are made from fossil fuels and require significantly more energy in production. Sustainable brands often use natural or recycled fabrics that require less chemical treatment, water, and energy. Organic fabrics such as linen, hemp, and organic cotton are biodegradable and environmentally sound.

    b. Saves animal lives

    Leather isn’t a by-product of the meat industry, and it’s estimated that it alone is slaughtering and killing over 430 million animals annually. Sustainable fashion brands are increasingly embracing the use of cruelty-free alternatives. Various alternatives include polyester made with ocean trash, plant-based compostable sneakers, bags from recycled seatbelts, silk created from yeast, and bio-fabricated vegan wool. Another interesting leather alternative comes from pineapples, where the fabric is produced using the leaves of pineapples.

    c. Requires less water

    Water is used in the dyeing and finishing process for nearly all items in the fashion industry. It takes 2,700 liters of water to produce a single T-shirt. Cotton is highly dependent on water but is usually grown in hot and dry areas. Linen, hemp, Refibra, and recycled fibers are some other sustainable fabrics that require little to no water during production.

    d. Supports safer working conditions

    Endless working hours, unacceptable health & safety conditions, and minimum wages, are the reality for most garment workers in the fast fashion sector. A few informative documentaries like “The True Cost” or “Fashion Factories Undercover” document the social injustices of the fast fashion industry. Eco-ethical brands advocate for sustainable fashion, health care, humane working conditions, and fair wages for their workers. 

    e. Healthy for people and the environment

    Fast fashion products often undergo an intense chemical process where 8,000 types of chemicals are used to bleach, dye, and wet process garments. Those chemicals often lead to diseases or fatal accidents for workers and inflict serious congenital disabilities on their children. These chemicals harm our health, as our skin absorbs anything we put on it.

    5 Sustainable & Ethical Online Marketplaces

    Here is a list of five earth-minded and socially responsible marketplaces that have sustainable and fair trade brands for the discerning and mindful shopper:

    1. thegreenlabels

    Netherlands-based webshop thegreenlabels is a sustainable fashion retailer that sells sneakers, womenswear, and accessories from various “green labels” brands. Founded in 2018, this is a marketplace where people can buy products from brands that care about a positive impact on the environment. All brands featured here guarantee fair working conditions and represent at least one of these 4 values – “CLEAN PROCESS” environmentally friendly production, clothes that support “LOCAL” communities, “VEGAN” brands to assure no animals were harmed and “WASTE REDUCTION”

    2. LVRSustainable

    LVRSustainable
    LVRSustainable

    Luisa Via Roma started as a family-owned boutique in the early 1900s. They have grown into a luxury e-retailer and created an LVRSustainable section for people trying to insert sustainability into their wardrobes. They have brands rated ‘Good’ or ‘Great.’ The site offers a wide range of products like bags, accessories, sports, shoes, lingerie, and much more for men, women, and kids. You can find organic, vegan, eco-friendly, ethical, and recycled & upcycled items here.

    3. Brothers We Stand

    Brothers We Stand
    Brothers We Stand

    Brothers We Stand is a retailer set up in solidarity with the people who make our clothes. This retailer conducts rigorous research to ensure that every product in their collection meets the following three standards: designed to please, ethical production, and created to last. It’s a great platform to shop for ethical and sustainable menswear. They also have their private clothing line along with other brands.

    4. Labell-D 

    Labell-D was launched with a clear mission to reduce the negative impact of fast fashion on the planet. This retailer wants to make Responsible Fashion the new norm. They intend to make sustainable clothing and fashion easy for both brands and consumers. Labell-D has a transparent accreditation process where they evaluate the brand’s carbon footprint and environmental impact. Their verification assessment includes animal welfare, emissions, materials, production processes, chemical usage, waste management, and traceability.

    5. Cerqular

    Cerqular wants to make sustainable shopping affordable and accessible for all. The retailer promises that every product and seller is verified as organic, recycled, sustainable, carbon-neutral, eco-friendly, vegan, or circular. They have a wide range of sellers and do not limit products only from luxury brands, so sustainable shopping is no longer expensive or inconvenient.

    Conclusion

    The fashion industry is a contributor to worldwide carbon emissions. Sustainable fashion is the new big thing giving rise to more and more sustainable brands and marketplaces. 

    To stand out and shine in the crowded eCommerce space is not easy. Having a robust Digital Shelf becomes critical for brands. A brand’s Digital Shelf is all of the ways their customers digitally interact with the brand, not only on marketplaces but on the brand’s DTC website & shoppable social media. This is why brands need to closely track & optimize their Digital Shelf KPIs like assortment, availability, pricing, ratings & reviews, product discoverability & product content to increase their online sales.

    Want to learn how DataWeave can help you win the Digital Shelf? Sign up for a demo with our team to know more.

  • U.S. Prime Day Deals 2022: Promotion Intelligence First Look

    U.S. Prime Day Deals 2022: Promotion Intelligence First Look

    As inflation hits another 40-year high at 9.1 percent, U.S. consumers geared up for their first sign of hope and relief in the form of anticipated discount buys – 2022 Amazon Prime Days, or so we thought. While Prime Days have grown to become a promotional period almost as important as Black Friday to digital shoppers, the combination of economic uncertainty, inflationary pressures, and supply chain challenges seemed to alter the discount strategy expected given activity seen during 2021 Prime Days.

    Our analyst team has been hard at work aiming to provide a ‘first look’ at 2022 Prime Day Promotional Insights, tracking discounts offered across 46,000+ SKUs within key categories like Electronics, Clothing, Health & Beauty and Home, on seven major retailer websites – Amazon, Target, Best Buy, Sephora, Ulta, Lowe’s and Home Depot. Our analysis compares prices seen during Amazon Prime Day 2022 on July 12th, to pre-Prime Day maximum value prices seen in the ten days leading up to Prime Days, to determine the average change in discounts offered during the promotional period. Below is a summary of our findings.

    Competitive Promotions Give Amazon a Run for their Money

    Amazon offered the greatest average discount enhancements for Electronics at 5.6 percent followed by Health & Beauty items at 5.1 percent, and Home products at 4.2 percent versus pre-Prime Day discounts seen across the categories considered within our analysis. The only category reviewed where average discounts were greater on a competitor’s website was on Target.com within the Clothing category. As seen below, Clothing on Target.com average discounts were 6.8 percent greater than pre-Prime Day offers, which was 2.6 percent higher than the average discounts offered for Clothing on Amazon.

    Target Capitalizes on Growth Opportunity in Clothing Category

    Diving deeper into the details of where Target won within the Clothing category, you can see a majority of their promotional activity took place within Women’s Accessories where discounts offered were 18.5 percent greater than those seen pre-Prime Day 2022, which was almost 15 percent greater than the discount enhancements seen on Amazon for Women’s Accessories. In fact, Women’s Shoes and Sneakers were the only two categories where the average discounts offered were greater on Amazon than on Target.com.

    Overall, the discounts offered on Target.com within the Clothing category were primarily concentrated within items priced $40 and lower, but what was most interesting is that within the $10 and under price bucket, Target offered average discounts of over 11 percent whereas Amazon increased prices for these items on average by over 9 percent.

    While most of the Clothing available on both Amazon and Target.com during Prime Days 2022 were offered without a price change, the greatest discount percentages tracked were within the range of 10-25 percent off on Amazon whereas Target chose to offer the bulk of their promotions at 25 percent off an up.

    Strategic Promotional Strategies Defined at the Electronics Subcategory Level

    When it comes to the Electronics category on Prime Day, the big question is always who will win the battle of the brands. Below shows the difference in average pricing and promotions discounts offered between products manufactured by Samsung versus Apple across each retailer platform, noting discounts were almost 3 percent greater on average for Apple versus Samsung products on Amazon, and Apple discounts were almost 5 percent greater on Amazon versus than those seen on Target.com.

    Amazon wasn’t going all in on Apple however, as we saw ‘Alexa’ devices (Amazon products) available on Best Buy and Target websites also, but the discounts were almost 4 percent greater on Amazon versus Target and over 7 percent greater than the discounts seen on BestBuy.com.

    While the average discounts offered within the Electronics category were greatest on Amazon (5.6 percent) versus Best Buy (3.9 percent) and Target (3.4 percent) as noted within the first chart of this blog and across brands and technologies considered above, the discounts offered on Amazon were strategically focused between 10-25 percent as seen below.

    Amazon’s Electronics promotions were also targeted at smaller price points, items priced between $20-500, whereas Best Buy and Target offered greater promotions for electronics priced $500 and up than Amazon.

    Below is a snapshot of price buckets tracked for Electronics available on BestBuy.com, highlighting where most of the promotional activity was targeted at products priced $50 and up during Prime Days 2022, with discounts ranging from 10 percent up to greater than 25 percent greater than pre-Prime day prices.

    The standout categories were TVs on Target.com with discounts averaging nearly 12 percent greater than those seen pre-Prime day, and smartphones on BestBuy.com with discounts averaging just over 11 percent greater than those seen pre-Prime Day. The category with the greatest average discount enhancements seen on Amazon during Prime Days 2022 was for Wireless Headphones with an average discount of 8.7 percent.

    Home is Where Amazon’s Heart Was on Prime Day

    Amazon dominated offers within the Home categories, especially for products within mid ($40-100) and higher price ranges (items priced $200-500), with the bulk of the discounts offered between 10-25 percent. There was little to no promotional activity seen across all price points on Lowe’s or Home Depot’s websites within the categories we tracked, and most other competitive offers on Home products were seen on BestBuy.com for products priced from $50-500. Even a subcategory like Tools offered deeper average discounts on Amazon (4.7 percent) than discounts seen on HomeDepot.com (1.1 percent) or Lowes.com (0 percent).

    For Large Appliances, Amazon was the only retailer to off any significant discount across each major subcategory with the greatest average discount being on Ovens at 6 percent, followed by Refrigerators at 4 percent. One caveat with this category, when we reviewed Large Appliance prices two weeks prior to Prime Days, we saw average price increases around 16.7 percent occurring on Amazon.

    During Prime Days 2022 however, Amazon also offered top average discounts for small appliances, except for on Instant Pots which appeared to have greater average discounts on Target.com (5.9 percent versus 4.2 percent on Amazon), and Vacuum Cleaners which appeared to have the best promotion of appliances small and large at 13.8 percent average discount on BestBuy.com. Another subcategory deeply discounted on BestBuy.com was weighted blankets, which averaged discounts around 18.5 percent versus Amazon’s average discount at only 6.2 percent.

    Health & Beauty Retailer Pricing Strategies Revealed

    Given the importance Health & Beauty Brands placed on Prime Day sales last year, we had anticipated to see more offers, especially within pure-play beauty retail channels, than we did for this booming category.

    Amazon drove most of the Health & Beauty offers seen averaging 5.1% discounts versus other retailers only offering less than 1% on average, but discounts were aimed at a targeted group of SKUs on Amazon, bringing the average discount lower overall. Most of the promotions offered on Amazon fell within mid-range price points ($20-50) and were discounted between 10-25 percent versus pre-Prime Day prices.

    Target.com offered the most comparable discounts to Amazon for Health & Beauty products on average, but their strategy primarily focused on items within the $20 and lower price range with discounts ranging primarily between 10-25 percent.

    More 2022 Prime Day Insights Coming Soon

    We know the significance visibility to critical pricing and promotional insights play in enabling retailers and brands to offer the right discounts to stay competitive, especially during promotional periods like Prime Days. While this blog is intended to provide a ‘sneak peek’ into 2022 Prime Day insights for the U.S. market, we will be providing more extensive, global coverage and will proactively share new insights with the marketplace as they become available throughout the month of July.

    Be sure to also check out our Press page for access to the latest media coverage on Prime Day insights and more. Don’t hesitate to reach out to our team if there is any particular category you are interested in seeing in more detail, or for access to more information on our Commerce Intelligence and Digital Shelf solutions.

  • 7 Key Metrics that QSRs want (but may not get) from Food Delivery Apps

    7 Key Metrics that QSRs want (but may not get) from Food Delivery Apps

    The Quick Service Restaurant market is projected to be valued at $691 billion by 2022. As the QSR industry grows and the market becomes even more competitive, restaurant chains continuously seek ways to increase sales via food aggregators to market their business. To improve ROI and sales, having data and insights into key metrics could help QSRs to boost their success rate.

    QSRs would like to know how they stack up against their competition regarding discoverability on cluttered food aggregator apps. Restaurants want to know the gaps in their product assortment to understand what drives customers to their competitors. Getting insights into delivery time and competitors’ delivery fees will help QSR improve delivery ETAs and optimize fees. They can also set competitive pricing with insights into their competitors’ pricing. In addition, they can use data to optimize their ad spending on food apps and improve marketing ROI.

    In this blog, we will discuss the relationship between QSRs and food aggregators and how getting data about key metrics from these food delivery platforms can help QSRs scale their revenue. 

    Data: The Key Ingredient to increasing sales

    According to Statista, online food ordering revenue is expected to grow at a robust CAGR of 10.39% between 2021 and 2025. Food Aggregators apps like Uber Eats, DoorDash, and GrubHub offer convenient meal delivery options from various QSRs within a single app. Food aggregators provide a multitude of benefits for QSRs. They give access to a huge customer base, quick delivery, and an easy entry into quick commerce, helping QSRs increase visibility. Although QSRs rely on food aggregator platforms for hassle-free ordering, tracking, and delivery, they can’t always rely on them to share critical data that could help them optimize their operations & increase sales. 

    Online food ordering revenue
    Online food ordering revenue

    1. Data on Product Assortment

    QSRs need assortment insights to understand their competitor’s menu assortment. Assortment analytics plays a crucial role in ensuring that QSRs aren’t losing sales because their competitors are offering cuisines and dishes that they aren’t. Understanding gaps in menus helps QSRs to better plan their menu. However, food aggregator apps can’t share competitors’ assortment data with QSRs for a multitude of reasons, guidelines, and privacy laws. Thankfully, at DataWeave, our QSR intelligence solution can! We help restaurants improve their assortment by sharing insights into the dishes and cuisines their competitors’ have on display.

    Menu Assortment
    Menu Assortment

    2. Data on QSR Discoverability

    QSRs would love to know how to increase discoverability on food aggregators, as it will help them to appear ahead in search results and beat the competition. Improving visibility on these apps directly impacts sales and drives more orders for restaurants. Some aggregators offer discoverability information but give it on demand, usually after 20-30 days, making it irrelevant due to the enormous time gap. They also don’t provide information about the change in the discoverability of your competition. All these data points are so critical, and understandably so, Food Apps can’t share this level of information with restaurants. However, DataWeave’s QSR Intelligence solution can! It provides real-time discoverability insights into your restaurant and competitor’s visibility so that the data is actionable, and QSRs can use insights to improve visibility. 

    Read how DataWeave’s QSR Intelligence helped an American QSR Chain and how their ranking on search results page on Ube rEats, DoorDash & Grubhub impacted outlet discoverability & sales!

    3. Data on Pricing & Promotions

    Pricing a QSR’s menu is tricky. If you price too high, you’ll turn off new customers. If you price too low, you’ll cut margins & may even come off as low-qualify. Customer Price Perception is greatly influenced by the Price-Quality relationship. To add to this, restaurants are often up against stiff competition from restaurants with similar cuisine offerings so it’s critical that prices are competitive. Understanding competitor pricing doesn’t imply that you have to beat their prices. You can compensate for any price differences by offering higher quality cuisines, better customer service, and quicker delivery. Once again, food apps can’t share competitors’ pricing data with QSRs. But DataWeave’s QSR & Pricing Intelligence solution can! QSRs can use these insights to drive more revenue & margins by pricing their menu right.

    4. Data on Delivery Time

    QSRs must be able to deliver hot meals, in a timely manner to customers because customers want to quickly dig into the delicious food they ordered. Quicker deliveries within the ETA will also help earn the trust and loyalty of customers. However, food aggregators don’t share information on the delivery times with restaurants – not their own delivery time or their competitors. DataWeave can help QSRs to understand their peak hours and optimize their service to ensure quick ETAs. They can also get detailed insights into competitors’ delivery times to make sure they’re competitive. This is important because customers will often pick restaurants with quicker ETAs.


    Read how DataWeave’s QSR Intelligence helped an American QSR Chain understand the correlation between delivery time & sales volumes

    Delivery time trend by urbanity
    Delivery time trend by urbanity

    5. Data on Delivery Fee

    As a thumb rule, customers will always compare delivery fees across apps. They’re conscious of delivery dollars included in their bill and often choose a restaurant with lesser delivery fees. This makes it even more critical for restaurants to understand how they stack up against their competitors. Understanding competitors’ delivery fees could potentially help QSRs to optimize their rates. And once again, food aggregators can’t share information on competitors’ delivery fees with restaurants. However, DataWeave’s QSR Intelligence can provide all delivery-related insights – be it Delivery etas or fees. 

    Delivery fee trend by urbanity
    Delivery fee trend by urbanity

    6. Data on Ad Performance & ROI

    Getting ad analytics will help QSRs better manage their budgets & increase the ROI on their Ad spends. For example, wouldn’t it be great if QSRs were able to understand which ad formats or promotions led to the most sales? Or which carousal ads had the most visibility in key zip codes where your QSR is expected to do maximum business? Or even insights into a competitor’s ads and promotions on food apps. Knowing this information will help restaurants spend sensibly when buying media on Food Apps & get the most bang for their advertising buck. Food apps do provide standard ad analytics – a number of clicks, CTR, and so on, but for more complex, insightful & actionable insights, there’s DataWeave’s QSR Intelligence! 

    Read how DataWeave’s QSR Intelligence helped an American QSR Chain understand the ROI delivered on ad spends across Food Delivery apps.

    Insightful & actionable insights for QSR Chains
    Insightful & actionable insights for QSR Chains
    Insightful & actionable insights for QSR Chains
    Insightful & actionable insights for QSR Chains

    7. Data on Outlet Availability / Availability Audit

    To avoid lost sales, being available & “open for business” on Food Apps during peak lunch & dinner hours is critical. Also on weekends, when order volumes are usually high. Sometimes because of technical glitches, QSR outlets appear unavailable on Food Apps. A glitch like that can lead to lost business, and the longer the glitch stays undiscovered, the greater the impact on revenue. While Food Aggregators do their best to make sure all QSRs are up and running on their app, using DataWeave’s QSR Intelligence, restaurants can now do an outlet audit to make sure that’s the case. With just a mere 2.8% unavailability, we saw a 28% drop in the sales for one of our QSR customers! That’s how critical Availability insights are. 

    Conclusion

    Analyzing and optimizing sales, delivery, discoverability, availability & customer data is one of the fastest ways to help grow your QSRs revenue. However, the biggest challenge QSRs face is that it isn’t always easy to get this information. With DataWeave’s QSR Intelligence now some of that data is a little more accessible as we discussed in this blog. And additionally, here are the 7 Tricks we recommend QSRs to use to win on Food Apps. 

  • eCommerce in South Africa: Data-Driven approach to getting ahead

    eCommerce in South Africa: Data-Driven approach to getting ahead

    What an exciting month we’ve had at DataWeave! Our team flew down to gorgeous Cape Town, South Africa to attend the 8th edition of #EcomAfrica! After months of Zoom calls and virtual events, it was a refreshing change to see our customers in person and meet some of the movers and shakers in eCommerce and some of the top South African brands. 

    Top eCommerce Companies in South Africa
    Top eCommerce Companies in South Africa

    My last visit to South Africa was before the pandemic. Things have changed since then, & the difference was stark! The eCommerce landscape had a paradigm shift during Covid-19 and grew exponentially. My customers spoke to me about the new opportunities, growth potential as well as challenges that came in because of this boom. For one, eCommerce in South Africa has become more competitive than ever – from online retail to grocery and food delivery to even alcohol delivery! All retail businesses seem to have jumped onto the eCommerce bandwagon.

    A recent Deloitte report found that over 70% of South Africans shop online at least once a month & 2 out of 3 respondents said they plan to increase their frequency of online shopping. 65% said they know what they want, search online & check all stores that stock the product to compare prices. Price is one of the key factors that influence consumer purchase decisions. Other critical factors include delivery fee, delivery time, promotions & discounts & product assortment to name a few. In order to stay ahead in this highly competitive arena, both retailers and brands need to make data-driven decisions about critical KPIs like pricing to stay ahead of the competition.

    Increased Online Shopping & Online Shopping Frequency
    Increased Online Shopping & Online Shopping Frequency

    We’ve been working with customers in South Africa for over 4 years now, even before the pandemic. So on Day 2 of the event – S.Krishnan Thyagarajan “Krish”, President & COO, Dataweave had a chance to share our learnings and experience from all these years and how user data is critical to getting ahead & winning the eCommerce race in South Africa.

    For the purpose of Krish’s keynote address, we tracked pricing insights for a finite set of categories across key South African retailers like Checkers, Pick n Pay, EveryShop, Incredible, Makro, Waltons, Shoprite & Dis-Chem to name a few over a period of 16 months from Dec 2020 to April 2022. We highlighted price increase and decrease opportunities and how each retailer reacted in order to stay competitive, increase sales and protect margins. 

    BATTLE of the eCommerce GIANTS!

    Key Highlights from the Keynote

    • Increasing prices where an opportunity exists helps retailers increase their margins exponentially. Pick n Pay had the highest action rate (73%) when it came to capitalizing on price increase opportunities v/s Dis-Chem at 11%. 
    • When it came to price decrease opportunities (in order to stay competitive with rival brands) Takealot was the most responsive retailer – they capitalized on 30% of the opportunities, followed by Pick n Pay at a close second (28%) and Shoprite & Dis-Chem at just 4%.
    • Most retailers took between 1 – 5 days maximum to make price changes which means responsiveness to the market among all retailers is high making it more important for online retailers to always be on their toes.  
    • The 2 categories where most retailers capitalized on Price Increase Opportunities were Sauces & Condiments and Crackers & Biscuits.

    Want to watch the Keynote video on Demand? Click here to register & watch.

    Price Increase & Decrease Opportunities
    Price Increase & Decrease Opportunities

    Bonus video content! 

    • Watch the Impact of price increase & decrease opportunities on Private Label brands! 
    • See how product stock availability impacts price changes over a 16-month period. 
    • Find out which brands are in the lead in the Skin Care, Pet, Baby, Laundry & Cleaning Aid categories 

    If you’re an online retailer in South Africa & need insights on staying competitive with the right pricing, product assortment, delivery time, delivery rates, and the other key influencers that affect customers’ choice of online retailers, sign up for a demo with our team at DataWeave to know how we can help!  

  • The Future of eCommerce is Social: Demystifying the Social Commerce Revolution

    The Future of eCommerce is Social: Demystifying the Social Commerce Revolution

    Social commerce is the selling of goods and services within a social media platform. Brands use social platforms such as Instagram, Facebook, Snapchat, and Twitter to promote and sell products. These platforms have become an integral part of consumers’ everyday life because they continue to engage users with relatable content, making them scroll their feeds for hours. 

    The Social Commerce model capitalizes on this high user engagement & moves social media beyond its traditional role in the top-of-the-funnel marketing process by encouraging users to shop without leaving their preferred apps. According to the Social Media Investment Report, 91% of executives agree that social commerce is driving an increasing portion of their marketing revenue, and 85% report that social data will be a primary source of business intelligence.

    Let’s talk a little bit about why brands should consider selling via social media platforms:

    Social Commerce vs. eCommerce vs. QCommerce

    While they may fall under the same umbrella of online selling, social commerce, quick commerce, and eCommerce are three very different concepts

    • eCommerce refers to online shopping via a (retailer or brand) website or app. Customers can access these platforms via desktop or mobile devices. However, the sales funnel generally looks the same. These brands and retailers use top-of-the-funnel tactics like social media content, digital ads, and other marketing strategies to encourage customers to visit the online store. There are three main types of eCommerce businesses: Business-to-Business (Alibaba, Amazon Business, eWorldTrade), Business-to-Consumer (websites such as Amazon, Rakuten, and Zalando), and Consumer-to-Consumer (platforms such as eBay & Etsy).
    • Quick Commerce (or QCommerce) refers to eCommerce businesses that deliver goods within a couple of hours or even minutes. Although it’s sometimes used interchangeably with on-demand delivery or instant commerce, the idea of quick commerce has been around in the food industry for ages now. It has been recently ushered into the mainstream by evolving consumer preferences for quicker delivery of groceries and FMCG goods.
    • Social commerce brings the store to the customer rather than redirecting customers to an online store. It removes unnecessary steps and simplifies the buying process by letting the customer checkout directly through social media platforms, creating a frictionless buying journey for the customer. Additionally, social media platforms are mobile-friendly, a huge benefit for brands because increasingly more and more customers are accessing the internet through mobile devices.
    Social Commerce
    Social Commerce

    Rise of Social Commerce

    First used in 2005 by Yahoo!, ‘social commerce’ refers to collaborative shopping tools such as user ratings, shared pick lists, and user-generated content. Social media networks snowballed throughout the 2000s and 2010s, alongside a general increase in eCommerce, leading customers and merchants to quickly recognize the benefits of buying and selling through social media networks. Social media platforms have since evolved from merely a showcase tool for brands. They now serve as virtual storefronts and extensions of a company’s website or brick and mortar stores, capable of handling the buying experience.

    Top Social Commerce Platforms

    Social media platforms aim to keep visitors engaged on their platforms for as long as possible. Increased time in-app or on-site maximizes their opportunity to serve ads, a primary source of revenue generation. Social media platforms have millions of active users and they have a great power to help companies and individuals build their brands, interact with consumers, and support after-sales. Here are the top social commerce platforms:

    • Facebook

    Facebook introduced Facebook Shops to capitalize on the commercial opportunity by allowing vendors to advertise and sell directly through the platform. Facebook integrates social commerce with shopping, allowing users to purchase products smoothly. Facebook shops offer a smooth user experience where users can review products and get recommendations from trusted acquaintances. Customers can directly interact with the merchant’s customer service department post-purchase. 

    • Instagram

    60% of people discover new products on Instagram. Owned by Facebook, Instagram facilitates in-app shopping and handles the entire transactions within the app itself. Users scrolling on Instagram often wants to follow trends and replicate the looks of their role models or favorite influencers. By offering purchasing options in the app, Instagram benefits from the platform’s rich visual imagery and videos, allowing businesses to sell an idea rather than the traditional process of selling a product. 

    • TikTok

    Shopify partnered with TikTok to introduce shopping and drive sales through the younger and seemingly ever-expanding TikTok audience. With TikTok for Business Ads Manager, brands and merchants can create in-feed video-based content depending on their product offering. This partnership allows Shopify merchants to expand to the TikTok audience.

    • Snapchat

    Snapchat has recently launched Brand profiles, a feature that allows users to scroll through a merchant’s products and buy them in-app. This new experience is powered by Shopify too. Merchants can create Brand Profiles or Native Stores that allow users to purchase products from the app. 

    Pinterest users are there for Shopping Inspiration
    Pinterest users are there for Shopping Inspiration
    • Pinterest

    Pinterest is also an image-based platform where users create boards of their favorite wedding accessories, home decor, fashion trends, etc. Pinterest doesn’t specifically offer social commerce for the global audience. Rather, it allows business accounts to create ‘Product Pins’ that are displayed in the brand’s Pinterest shop. Only U.S. customers can purchase within the app. Users from other countries are redirected to the eCommerce site to complete the sale. We have added Pinterest to this list because 89% of Pinterest users are there for shopping inspiration.

    Pinterest is an image-based platform where users create boards
    Pinterest is an image-based platform where users create boards

    Why Should Brands Care About Social Commerce

    • To enhance social media presence and brand awareness

    If your target demographic is in the 18-to-34 age range, they’re already on social media and waiting to shop while they scroll. According to Sprout Social, over 68% of consumers have already purchased directly from social media and nearly all (98%) consumers plan to make at least one purchase through social or influencer commerce this year. You can enhance brand awareness by selling on social media platforms. Influencer marketing is an amazing way to build brand awareness since customers are now seeking authenticity from micro-influencers rather than big-name celebrities. 

    • To generate social proof

    90% of online shoppers say that they read online reviews before making an online purchase. Whether it’s an automated follow-up email or a message through the social media platform, ask for a review after your product has been delivered to the customer. You can also offer incentives like a contest to encourage previous customers to weigh in and share their experiences. These steps will allow you to collect social proof since it’s vital to build a positive reputation online. You can also ask customers to create small product review videos that you can share on your social feeds in creative ways. You can also post user-generated content, create a carousel of positive comments, or host a live video with happy customers.

    Social Proof
    Social Proof
    • To simplify the buying process for consumers

    Traditional eCommerce involves several steps. It starts with displaying ads on social media platforms and customers being redirected to the business website for completing the transaction. To complete the transaction, customers also have to create an account or manually fill in the credit card details and delivery address. On the other hand, social is only a three-step process — find, click and buy. 

    Counterfeit Products
    Counterfeit Products

    Conclusion

    While social commerce is proliferating, it also has a few setbacks like the rise of counterfeit products. Counterfeiting has expanded into social media and has become an under-reported but vital hub for counterfeiters. A counterfeit detection solution can help brands and merchants identify & remove fake and unauthorized products. Technologies like image recognition can help in counterfeit detection by capturing fake logos and discrepancies. Removing counterfeit products will help brands safeguard customer loyalty and prevent fake products from harming your bottom line. 

    Here’s how DataWeave helped Classic Accessories, a leading manufacturer of high-quality furnishings & accessories identify counterfeit products across multiple retail marketplace websites eliminating 22 hours of time spent per week conducting manual audits – read the case study here. 

    Are you a brand or a retailer worried about counterfeits? Sign up for a demo with our team to know how we can help you track, identify and eliminate fakes! 

  • How Inflation has hit the Retail Industry

    How Inflation has hit the Retail Industry

    Inflation has resurfaced after a decade of tranquil price increases. The persistent COVID-related supply chain disruptions have been a driving factor in increasing consumer costs since some commodities are harder to come by. While inflation is a normal economic phenomenon, the current 3.81% inflation rate has increased the cost of living for families across the globe.

    Global Inflation Rate
    Global Inflation Rate. Source: Statista

    Worldwide inflation is expected to remain near 5.0% in early 2022 before gradually easing in response to industrial and agricultural commodity price declines. Additionally, the global consumer price inflation peaked from 2.2% in 2020 to 3.8% in 2021 and will average 4.1% in 2022 before subsiding to 2.8% in 2023.

    In this blog, you’ll learn about the impact of inflation on the Retail Industry. 

    What is Inflation?

    Inflation is an economic term that describes an overall increase in the price of goods and services in an economy, and a by-product of inflation is the devaluation of the currency used within that economy. For example, a clothing retailer that used to pay $8 for a t-shirt two years ago will now have to pay $10 for that exact product. The t-shirt hasn’t changed at all. However, it has become 25% more expensive. Inflation and the devaluation of currency are part of the reasons why they’d now pay $2 more for that same T-shirt.

    Also Read: Top 7 strategies to sell effectively on Amazon

    Impact of inflation on Retail

    FMCG

    The Fast-moving consumer goods (FMCG) sector will continue to grow because there is growth in household goods spending despite the Russia-Ukrainian crisis, global interest rate, and rising fuel prices. In fact, the demand for consumer packaged goods rose sharply in countries heavily affected by the pandemic. However, the FMCG sector will see a rise in prices of commodities because crucial resources such as cooking oil, tea, cocoa, etc., become scarce. The persistent shock to the supply chain has forced various FMCG companies to increase their prices. For instance, Mondelez, a Fortune 500–listed snack and beverage company, announced a 6-7% price increase. 

    Inflation for Fashion & Pharma Industry
    Inflation for Fashion & Pharma Industry

    Fashion

    The global fashion industry posted a 20% decline in revenues in 2019–20. Inflation in fashion is caused by transportation bottlenecks, material shortages, rising shipping costs, and straining supply and demand. The global fashion industry will see complete recovery in 2022. COVID-caused supply and demand constraints have eased, but shoppers will have to reconcile to price jumps in everything from bags to shoes.

    Pharma

    Pharmaceuticals are recognized as an essential commodity and therefore have a massive impact on the household budget. Vizient has projected a 3.09% increase in the inflation rate in drug prices from July 1, 2022 – June 30, 2023. It shows how inflation has a direct impact on prescription drug costs. Notably, retail prices for some of the most widely used prescription drugs are expected to increase 2x as much as inflation. The demand for pharmaceutical drugs has been higher post-pandemic, ensuring that consumers’ total demand and spending in this vertical will remain unchanged. 

    Comparison of New, Used & Electric cars
    Comparison of New, Used & Electric cars
    Highest & Lowest Inflation in Beauty category. Source: nielseniq.com

    Automotive

    The rise of both new and used cars has been steeply increasing partly because of the shortage of semiconductors and the backlog from the closure of factories during COVID-19. According to the Bureau of Labor Statistics, there has been a 24.4% inflation in the used car purchase prices and an 8.8% increase in the new car purchase price. Rising oil prices across the globe and the historical oil crisis fuelled by the Ukraine-Russia war have strained many people’s budgets. However, the automobile market is seeing an uptake in demand for electrical vehicles (EVs). EVs represented 14% of car sales between January and June 2021. 

    Beauty

    COVID-19 brought new challenges to the cosmetics industry, chief among this being face-covering required by law. In light of social distancing and lockdowns across the globe, consumers were buying less makeup. The rising cost of labor, energy, and raw materials used in beauty products have resulted in a “once-in-two-decade” backdrop for price hikes. The cost of palm oil, a common material in beauty products, has soared 82% in two years due to Indonesian labor shortages. Nevertheless, consumers will spend more time outside the house. Beauty price per unit changes shot up 17% in-store and online in 2021.

    5 Things that will help retailers during inflationary times

    1. Observe Competition

    Retailers should follow their competitors closely—when they start to raise/lower prices, consider following suit. Using competitive data to gauge price changes will help in managing price parity. However, excessive discounts and lower prices to gain an advantage over your competitor could backfire in various ways. For example, low pricing may convey that your products aren’t as good as your competitors’, impacting your long-term brand image. Moreover, lowering prices to sell more doesn’t necessarily mean higher profits, especially during high inflation. To leverage this strategy effectively, retailers must first identify SKUs that have the highest impact on their pricing.

    2. Build a structured and targeted pricing strategy

    An effective pricing strategy that leverages differences in product, channels, and customers will help retailers to maintain long-term value for their business and customers. However, customers might react differently to a steep price increase. Broad price increases will demonstrate insensitivity and erode customer trust. Instead, retailers can thoughtfully tailor their inflationary price increases for each customer and product segment with a competitive pricing strategy. With a competitive and historical pricing strategy, brands can examine their customers’ end-to-end profitability and willingness to pay relative to a comparable peer set. 

    Price Competitiveness for the right items
    Price Competitiveness for the right items

    3. Rethink commercial positioning

    The pandemic and rise of inflation during 2020–2021 have profoundly impacted how consumers live and what they value. Understanding how your consumer’s needs have shifted and used a promotion strategy to manage today’s inflationary pressures is crucial. As new behaviors emerge post-pandemic, retailers must prepare for the potential top-line impact of demand shifts. Rethink commercial positioning and review marketing and packaging strategies, including the potential use of nonuniform and, in some cases, nonprice mechanisms.

    4. Ensure price competitiveness on the right items

    The Key-Value Item (KVIs) list should be reviewed again, considering changing shopper needs and habits during the pandemic, plus the supply and demand shock that the industry is currently experiencing. Price-sensitive and vulnerable shoppers are finding this inflationary period particularly tough, so brands might require an even deeper investment in KVI pricing. Reinvest base prices on essential products to drive volume for your best price-sensitive (PS) customers. Compete only where you need to be without overspending. Online channels should continue to reflect in-store prices and diverge during this time. Pricing Optimisation software enables best practices to simultaneously manage a high number of price increase requests.

    5. Revisit promotions to conserve costs and preserve stock availability

    Increasing the number of promoted products is a reflexive response to inflation, but it’s not the right response for building sustainable sales or longer-term loyalty. Inflationary times offer an excellent opportunity to reset promotional strategies to save money and margin. Retailers can increase sales and seize opportunities with a promotional pricing strategy. Increased promotional activity has a knock-on effect vs pricing position in high-low strategies and erodes overall value perception, creating a vicious circle of more promotions equals poorer value.

    Conclusion

    Today’s economic climate and associated pricing pressures are challenging for retailers and customers. Some companies have responded by announcing an increase in prices across product categories. Companies can manage pricing margins responsibly and profitably during inflation. Determining how and where new opportunities exist can help companies control inflation, drive growth, and remain profitable.

    Need help to arrive at the right pricing & discounting strategies to counter inflation? Sign up for a demo with our team to know how we can help!  

  • What is Customer Price Perception and why it is important

    What is Customer Price Perception and why it is important

    Finding the right price often requires a trade-off between margin and price perception. Brands may want to defeat competitors’ prices on all their products, but that can often lead to losses because sales directly link to price perception. Instead of trying to stay competitive across the board on all products, brands must identify key value categories (KVCs) and key-value items (KPIs) whose prices buyers tend to remember and price those products competitively. In this scenario, they can make up for lowered prices on key products by fixing higher prices on other products. 

    Consumers’ perception of price fairness largely determines their experience with a brand. Brands selling online can often have a disconnect between their prices and what customers expect their prices to be. However, that does not mean spiraling downwards by getting trapped in discounting cycles and heavy promotions that can harm your bottom line. Instead, brands require real-time monitoring across thousands of stock-keeping units (SKUs) to identify key categories and items they need to price with care. In this blog, you’ll learn about price perception and the factors that influence it. 

    What is Price Perception?

    Price perception is the perceived worth of a product or service in the consumer’s mind. It is one of the leading variables in the consumer’s buying process. Buyers are unaware of the true cost of production for the products they buy. Instead, they make buying decisions based on an internal feeling about how much certain products are worth and which brand offers them the best value. To offer competitive prices and yet obtain a higher price for products, brands often pursue marketing strategies to improve the price perception of their brand and products.

    Price Perception
    Price Perception

    However, brands should not fall into the trap of assuming that price perception is a competitor’s price index. It’s not about offering the lowest price on certain SKUs. Not every brand strives to offer the lowest prices. Some brands take a slightly different approach to ensure the right value for their products. For example, take a look at Trader Joe’s, a grocery chain that has never claimed low costs. They’ve always taken a holistic approach to their pricing and customers to build a loyal following. And it worked well for them. Trader Joe’s can boast one of a high-value perception score, despite not having rock-bottom prices. 

    Marketplaces such as Walmart and Amazon may not have the best prices on every item. Still, customer perception is that they will have the lowest prices and will often shift the share of sales towards such platforms over businesses that offer the same or even lower prices. 

    Some things to consider:

    • What do your customers think of your brand?
    • What are the key factors that are driving your customers’ price perceptions?
    • Is your product mix properly aligned with your brand perception?
    • Are you communicating the most important and relevant information to your customers?
    • Is your message being received and understood?
    • Who do your customers see as your competitors, and why?

    Also Read: 11 Reasons why your eCommerce Business is fail 

    What is Price Positioning?

    Price positioning is pricing products or services within a certain price range. It indicates where certain services or products lie in relation to competitors’ pricing and in the mind of different customers. A brand’s price positioning has a huge impact on whether the products are seen as priced low or not. The following is a great way to understand the price-value matrix:

    Price Positioning
    Price Positioning

    Your brand’s position in this matrix will depend on your pricing objectives, competition, and customer loyalty. Price positioning helps the marketing and operating teams understand customers’ perceptions of your brand and convince customers to buy your products. Brands need a holistic approach toward setting prices for their products in order to drive conversions through intelligent pricing and competitive insights. 

    Factors that influence Price Perception

    Price-Quality Relationship

    Price is often an indicator of product quality. The general rule is that the higher-priced products are perceived to have better quality, implying that brands should consider a rational quality-price relationship in their pricing or promo strategy. For example, it might not be best practice to have similar prices for both good and low-quality products because customers will perceive low-quality products as overpriced and might not purchase from you.

    Price-Consciousness

    Customers aren’t price conscious about every product. Instead, they are only price conscious about certain products under the best price guarantee or BGP. For instance, if buyers find your BGP products more expensive than your competitors, the cheaper products in your assortment will still be perceived as expensive. 

    Value-Consciousness

    During markdown periods, ensure that you are not undermining the efforts to shape and maintain price perception by offering extreme or complex discounts. In an attempt to clear stocks, promotions simply confuse the shopping experience for customers and further deteriorate trust in your brand. Your promotional offers should keep price perception during the holiday season or clearance sales by offering a simplified promotional program. Start by understanding which price mechanics and SKUs work best for your target customer segment. You should also reduce over-communication on hero deals else buyers will assume that you incorrectly price products during new seasonal launches. 

    Prestige Sensitivity

    Gerald Zaltman, a Harvard professor, argues that 95% of all purchasing decisions are subconscious. Luxury brands are a great example of how psychology directly links to price perception. Customers buy premium or luxury products to demonstrate their social status. In this scenario, buyers don’t hesitate to buy expensive products from certain brands even if they are explicitly overpriced. Thus, brands selling premium products will have to ensure pricing is coherent with buyers’ expectations. 

    Every customer wants to know they’re getting the best value. They use the highest and lowest prices in a range to understand how expensive a product or brand is. So, by removing high price point lines with low volume, customers will see more minor price points around the store. Brands must merchandise entry price points to help customers identify the lowest prices and improve the perception of their product ranges. 

    Product Range
    Product Range

    How to adjust Price Perception

    Here are three ways for brands to improve price parity:

    • Marketing to influence Price Perception

    An efficient pricing management strategy will focus on competitiveness and establishing the right price perception among your customers. You can influence customers’ price perception by improving the look and feel of your online stores since simpler designs are often reflections of lower prices. Another great way to influence price perception is to offer loyalty and reward programs that also improve brand loyalty and reinforces the vision of an economy store irrespective of the prices of your products.

    • Competitive Analysis

    Brands can understand price differences after a competitive analysis. Customers often search for similar products across brands to find the best deals, and you will be able to understand customer opinion through competitor analysis.

    • Price Management Automation

    A price monitoring platform can help brands to stay on top of promotions and discounts offered by their competitors. A price intelligence software will help brands associate products by similarity criteria and compare the pricing of their products with those of competitors. It offers a detailed view of the market and ensures that brands take care of their bottom line.

    Conclusion

    When a consumer comes across a similar low-priced product or service from a different brand, they may see it as a good deal or might perceive it not worthy of their time or money. What consumers think about your brand’s price is just as important as the actual price of that product. A buyer may sense a company as “upscale” and assume that they have high prices, or they may see a brand as a discount retailer whose prices are too high for its reputation. At times, consumers might also see cheaper alternatives as inferior. It’s not easy for a brand to understand its customers’ perception of price vs. value it offers. Brands need a long-term, dynamic pricing strategy that matches the demands and trends of a global, competitive market. And in order to drive sustainable growth, they need to make smarter pricing and promotion decisions with insights into competitive pricing. 

    Learn how DataWeave can help make sense of your and your competitor’s pricing & promotional strategies and help your brand build the right Price Perception. Sign up for a demo with our team to know more.

  • 11 Reasons why your eCommerce Business is failing

    11 Reasons why your eCommerce Business is failing

    No matter where your eCommerce business sells, there are some fundamentals that brands have to get right to achieve sales targets. Brands need to find the right product/market fit, nail their lead acquisition strategy, and design a qualified sales funnel to turn prospects into leads and eventually returning customers. They will also have to analyze their customer’s buying journey and get insights into competitors’ strategies to understand what works for their business.

    If your eCommerce business is struggling, read this blog to learn about steps you can take to increase sales and keep your business afloat. 

    1. Lack of social proof

    Customers often check for reviews or testimonials before making a purchase. Our decisions are consciously or unconsciously influenced by the opinions, choices, and actions of people around us. Social proof helps brands build customer trust, adds credibility to their business, improves brand presence, and validates customers’ buying decisions. 92% of consumers are more likely to trust user-generated content (UGC) and non-paid recommendations than any other type of advertising. Additionally, brands should also find ways to combat negative reviews since bad reviews can sometimes be extremely damaging. 

    Understanding these reviews or the impact of your brand’s social proof is critical. At DataWeave, we help brands analyze online reviews to understand customer sentiment and adapt to feedback to enhance their experience with your brand. 

    2. Slow site speed

    Site speed of the home page and checkout page on your D2C website can be a roadblock. Slow sections on your site like My Accounts, checkout, and cart are often overlooked when it comes to tracking site speed. Brands should run their checkout process at least once a month to ensure it’s fast, smooth, and bug-free. You can optimize images, strip unused scripts, implement HTTP/2, etc., to improve site speed and performance. 

    3. Poor customer service

    69% of US consumers say customer service is very important when it comes to their loyalty to a brand. Guaranteeing a return customer is important to maintaining customer loyalty. While the focus is on the first purchase for new customers, your brand’s customer service will determine if first-time customers become repeat buyers. Loyal customers are known to spend 67% more on a brand product than new customers, even if they make up only 20% of your audience. 

    Types of customer service
    Types of customer service

    4. Failure to send traffic to popular products

    Be it your own D2C website, or when selling on a marketplace, you should be able to drive traffic to your best-selling products. One of the best ways for sending traffic to popular products on your website is to run paid ad campaigns and reach new audiences with influencer marketing on social media. Brands can also attract customers with organic media such as writing blogs and producing podcasts. 

    If you’re looking at driving traffic to key products on Amazon & other such marketplaces, sponsored ads are the way to go! Sponsored ads help your best-selling products more discoverable & helps shoppers find your brand with ease. 

    5. Inadequate pricing

    Finding the right pricing strategy for your eCommerce business is crucial for optimizing sales and increasing revenue. The first step is to perform a competitor and historical data analysis to get a general idea of the market and then develop a pricing strategy that is the right fit for your products. Brands also have to ensure that they have dynamic pricing that can adjust according to supply and demand. 

    Our Digital Shelf solution at DataWeave helps brands track pricing for products across different pack sizes & variants across multiple online retailers and marketplaces helping them stay competitive in the market. 

    Optimize the right pricing strategy
    Optimize the right pricing strategy

    6. Not targeting the right audience

    One of the biggest mistakes that eCommerce businesses can make is targeting the wrong audience. It’s crucial for brands to define that target audience and then tailor products and marketing toward them. To increase sales as an eCommerce business, brands have to understand their audience, their interests, and how to appeal to their interest. Start by creating ideal buyer personas that represent your ideal customers. Also, segmenting audiences and targeting various groups based on buyer personas for ad campaigns will lead to better sales and revenue. 

    Targeting the right audience
    Targeting the right audience

    7. Poor product descriptions

    One of the major and common mistakes by eCommerce brands is using irrelevant product descriptions that are not optimized for the product. Customers don’t add products to their cart if they have difficulty finding sufficient information relevant to the product. Brands should write attention-grabbing descriptions optimized for SEO that are informative for the users. Here are some tips to optimize content to drive more eCommerce sales.

    At DataWeave, our AI-Powered solution helps brands optimize content and visuals across product pages to improve discoverability. 

    8. Not having multiple revenue streams

    Due to COVID-19, many businesses have had to modify or temporarily shut down their daily operations. However, finding new revenue streams can be a great way for eCommerce businesses to make up for the lost income and keep the company afloat. The best solution is to diversify your product offerings by offering commonly purchased products in bundles. 

    9. Low-quality visuals

    Businesses fail to hit their sales targets because of low-quality visuals in product descriptions. High-quality and custom images can improve conversion rates from both marketplaces and image-based channels like social media. Social media users are attracted to exciting, high-quality content that conveys a desirable lifestyle. Brands should use high-resolution, attractive pictures of their products. Brands can also utilize UGC and influencers to help build up their content libraries.

    Low-quality visuals
    Low-quality visuals

    10. Wrong Assortment. Poor Availability

    When your target audience lands on your eCommerce store and cannot find what they’re looking for, it leads to a poor shopping experience, but more importantly a lost sale for your brand! While you cannot have endless inventory, it’s essential to optimize your assortment & product availability to decrease the chances of your customer walking away. Assortment & availability optimization begins with analyzing current and historical inventory trends. If done manually, assortment can be a time-consuming task. A healthy assortment can increase retail sales by creating a positive shopping experience for your customers and encouraging them to return to your store again.

    11. Bad eCommerce UX

    Offering a sub-standard user experience is a common reason why eCommerce businesses find it difficult to increase sales. According to a study, the conversions can fall by up to 7% for every one-second delay in page load time. Businesses can use a countdown clock on their landing page and exit pop-ups to improve conversations. Your landing page and product descriptions should provide information that helps your users make a better and more informed decision. 

    Conclusion

    If your eCommerce’s business sales are tanking, improving site speed, customer service, social proof, and product descriptions are some of the levers you can pull to remedy the situation. Brands should also work on improving online reviews & ratings, availability, assortment, visuals, and website UX to improve customer experience. These steps not only increase loyalty but also improve customer retention. 

    Need help tracking online pricing for your eCommerce business? Or decoding customer sentiment from reviews they’ve left for your products? Or do you need insights into your product assortment and availability? Sign up for a demo with our team to know how DataWeave can help!  

  • How VCs and Brand Rollups are using Data for faster Acquisitions

    How VCs and Brand Rollups are using Data for faster Acquisitions

    When it comes to brands – the biggest story of 2021 was the astronomical growth of Brand Roll-ups. For the uninitiated, Brand Roll-ups are companies that acquire multiple digital consumer brands and then scale these brands 100x by leveraging their own operational expertise across eCommerce platforms, Supply Chain, Warehousing, Marketing, and so on.

    Thrasio is the poster boy for the Brand roll-ups and is valued at over 10 Bn USD.

    Brand rollups have raised over $12 billion in 2021 and the trend only seems to be accelerating in 2022. Not only Brand Roll ups, but VCs too have been pouring money into digital brands. In India, 77+ brands have raised more than 2B USD in 2021. In the US this number is estimated to be north of $10 billion.

    Cumulative capital raised by Amazon Aggregators
    Cumulative capital raised by Amazon Aggregators

    Scaling fast doesn’t come easy. It comes with its own set of challenges. So even with ample experience in running and scaling brands, Brand roll-ups are posed with unique challenges.

    Challenge of Scouting the right brand

    There are 1000s of online consumer brands and new ones are launching every day. Every Brand roll-up wants to be the first one to scout a brand – but this is not easy.

    The challenge here is to identify & pick the right brands without having access to any sales or financial data. Every Brand Rollup has a wishlist with regards to the number of SKUs, price points, reviews, and ratings as well – but don’t have tools in place to scout brands with these criteria in mind. And across multiple platforms and categories, the problem gets more complicated.

    This is an ongoing problem since a brand that was not selling well yesterday may start hitting higher sales numbers a week down the line – and that is why Brand scouting has to be a continuous process.

    One way these aggregators have solved this challenge is by offering mouth-watering referral fees for referring a brand. But this is not a sustainable long-term solution.

    Data Comes to the Rescue

    What Brand Roll-ups need is a continuous and automated data first Brand Scouting solution to enable them to scout the right brands.

    • What are all the brands in a category of interest?
    • Which of these brands is within the filters of Number of SKUs, Price Range, etc.?
    • Which brands have shown an exceptional rise in search rankings?
    • Which brands have shown the most increase in the number of ratings and reviews?
    • Which brands have the highest gain in the customer ratings?
    • What are the estimated sales and market share of the brands?

    DataWeave’s Brand Scouting solution solves exactly this.

    DataWeave’s Brand Scouting Solution

    DataWeave’s Brand Scouting Solution is a comprehensive solution to help Brand Rollups and VCs scout for the ideal brand that fits their acquisition profile. We leverage public data collected from multiple eCommerce platforms to get them the desired information on brands they’re looking for.

    For all the focused categories (Typically 30-40) – we collect data of all the SKUs (Typically 15,000-20,000) and aggregate that at a Brand level:

    • Ranking – Usually Brand Rollups are not interested in the Brands which are on the first page. But, they are interested in the brands which might be b/w 500 to 10,000 ranks but are showing an exceptional gain in ranking week on week.
    Brand Discoverability & Ranking on Amazon
    Brand Discoverability & Ranking on Amazon
    • Ratings – It’s important to look at brands that are showing high improvement in ratings or have consistently shown high ratings. The proportion of 5 stars vs. 1 star is an important metric here.
    • Number of Reviews and Ratings – We enable you to find brands that have both high ratings as well as a high number of reviews. This is a very good metric to find the brands in a category that are getting exceptional customer love.
    Brand Popularity Tracker
    Brand Popularity Tracker
    • Filters – We enable filtering in terms of – No. of SKUs, Price Range, Rating and Reviews and even can eliminate established brands so that you only see the brands which qualify your criteria. We also enable you to separately analyze brands that are buying sponsored ads in a category, so you have a clear distinction between organic and sponsored growth of these brands.
    • Trends – What is important is not just the static performance on the day of analysis – but a trend analysis over a period of time to find the brands which are growing exceptionally.
    Brand Score Trend, Average Rating trend & No of Reviews Trend
    Brand Score Trend, Average Rating trend & No of Reviews Trend

    … but, wait there’s more.

    We compliment Brand Scouting with three more solutions to provide the right context and further analysis needed to provide comprehensive insights into the category and platforms where you are scouting for brands:

    Category Analytics: When you are looking at a category and the brands in that category, it is often important to understand how dynamic that category is. We can help analyze:

    • If the category is crowded with more brands per product.
    • Does it have space for new brands?
    • What is the number of new brands entering that category?
    • What is the number of new SKUs entering that category?
    Category & Subcategory Evaluation
    Category & Subcategory Evaluation

    We can also help with benchmarking the category – to help understand how the brand that you are scouting is doing when compared to its category peers.

    Rank Group versus Price, Rating & No of Reviews
    Rank Group versus Price, Rating & No of Reviews

    Sales & Share: We can also provide a good directional estimate of the sales and market share of all the SKUs in the category wherein you are scouting for brands. These are estimates powered by our proprietary machine learning algorithms and can help you solidify your hypothesis around a blog or a category.

    Revenue by Price Points
    Revenue by Price Points

    Sentiment Analysis of Reviews: Customer reviews tell more about the qualitative aspects of the SKU and the brand itself. Our algorithms can help understand what features of a brand or a product do customers really care about. We can answer questions such as:

    • Which features are mentioned most commonly?
    • Which features are mentioned positively or negatively?
    • What adjective is used to describe that particular feature?
    Customer Sentiment Analysis
    Customer Sentiment Analysis

    The suite of Brand Scouting and complementary solutions is evolving rapidly as the space is evolving rapidly. We are supporting several VCs and Brand Roll-ups globally to scout for brands.

    The best aspect about DataWeave is our ability to scout brands across 2,000+ eCommerce platforms globally across geographies. We are super stoked to be playing an enabler in the Brand Rollup revolution.

    Beyond Brand Scouting – Digital Shelf Analytics

    The challenge for Brand roll-ups is not over by just scouting and acquiring a brand. The journey is just about starting – the next challenge that the Brand Rollup faces now is to scale up these brands.

    The challenge the Brand Rollup face is unique and very different from a single brand operator or even traditional CPG conglomerates.

    DataWeave’s flexible product philosophy enables Brand Roll-ups to diagnose and measure the performance of multiple brands across multiple platforms in one dashboard.

  • How Restaurants can use QSR Intelligence to Drive Sales

    How Restaurants can use QSR Intelligence to Drive Sales

    Quick service restaurants (QSR) are not only about delivering great food. They also have to overcome challenges like delivery, logistics, and affordable pricing, especially since covid-19 has staggered the entire industry. QSR intelligence helps restaurants get real-time insight into their performance across food delivery apps. With QSR intelligence, restaurants can identify the highest paying buyers across customer segments, demographics, and locations. Data-driven insights will help QSRs improve performance, decrease delivery time, optimize ad budget, and increase food quality – all with the goal to scale revenue and increase orders through food apps.

    The global fast food and quick service restaurant market are expected to grow at a CAGR of 5.1% from 2020 to 2027. The QSR industry is rapidly growing to encompass the changing needs of customers. 60% of U.S. consumers order delivery or takeout once a week and online ordering is growing 300% faster than in-house dining. With QSR intelligence, restaurants can get insights into metrics that will drive their profitability by helping them to fine-tune menus, enhance customer interaction, improve advertisements, and adjust inventory.

    Benefits of QSR Intelligence

    Continuous in-depth analysis of restaurant statistical data will help companies spot trends and devise strategies to improve sales via food apps. Here are a few benefits of QSR intelligence:

    a.    Improve estimates & minimize wait times

    QSR intelligence can help with accurate sales forecasting. With big data, restaurants can track their popular dishes or combos for various meal times to minimize wait times and increase delivery speed. It can also inform restaurants about upcoming trends, especially during holidays and festivals. Keeping an eye for trends will play a significant role in maximizing efficiency during food preparation and ensuring accurate food delivery ETAs.

    b.    Location-based promotions

    QSR intelligence allows restaurants to target customers based on their proximity to the restaurant. The food must be delivered at a particular time to the customers to enjoy the dish at the right temperature. QSRs can apply demographic intelligence to determine cancellation rates, delivery charges, and the proportion of demand and supply. These metrics will help QSRs to improve location-based promotions.

    c.    Increase ROI on deliveries

    To increase return on investment through food deliveries, QSRs can track metrics like location-based promotions, various payment options, ratings, etc. Tracking these metrics will help QSRs offer accurate ETAs, improve operational efficiency, and personalize services, which will increase revenue. Restaurants will also be able to understand where they can adjust their profit margins to increase revenue while maintaining a cumulative level of success.

    How to use QSR Intelligence

    a.    Assortment and availability

    The more restaurants can understand what and how their customers eat, the better they will be prepared to service those demands throughout the day. For example, QSRs can calibrate the menu, ingredients availability, and kitchen preparation time depending on their customers’ orders for lunch and dinner. This also helps optimize daily workflow, such as reorganizing staff to lower labor costs, optimizing the supply chain for ingredient delivery, and revamping the menu to offer better dishes. Another way to ensure your availability is to analyze your busiest hours and adjust the staff and delivery workforce accordingly. For example, if your customers tend to order more during breakfast, it’s worth considering opening your restaurant a bit earlier.

    QSR availability across 4 Food Delivery apps
    Availability across 4 QSR Food Delivery apps
    Availability trend during peak hours - Lunch & Dinner
    Availability trend during peak hours – Lunch & Dinner

    b.    Delivery time

    One of the most driving factors for the success of QSR is delivery time. Restaurants have to ensure the food is delivered as quickly as possible so customers can consume it at the right temperature. Data-driven insights can help restaurants track repeat addresses, find shortcuts or time-saving routes, and avoid unfamiliar or low delivery locations.

    QSRs have to analyze the entire delivery process from time taken to order on the app, how quickly kitchens can prepare orders, hand over to delivery partners, and get them to the customers. An essential part of QSRs is throughput, the speed at which they can process and deliver orders. During peak hours like lunch and dinner, faster service and quick ETAs ensure that customers do not choose other restaurants. If you have different menus for breakfast and other meals, ensure that your foodservice app can remove such menus when they are not available.

    Delivery Time Analysis
    Delivery Time Analysis
    Delivery Fee Analysis
    Delivery Fee Analysis

    c.    Pricing and Promotions

    QSRs have to understand customers’ price sensitivity while determining delivery costs and ensuring profitability for the business and delivery partners. Customers might look for free deliveries but not adding delivery charges might lead to loss. A deep dive into common transaction data across the locations will allow restaurants to understand the price sensitivity of all customer segments, helping them make intelligent pricing decisions.

    QSR intelligence can also help restaurants determine which delivery locations are most profitable. This helps to adjust the delivery radius, fee, and promotions. Restaurants can offer promo codes, coupons, referral codes, etc., to attract customers and encourage repeat purchases.

    d.    Discoverability

    Restaurants have to ensure that their dishes are on the first-page listing. With QSR intelligence on category analysis, keyword optimization, and competition analysis, restaurants can help their customers discover dishes. This also includes optimizing listings for pricing and rating and delivery fees and availability during peak times such as breakfast, lunch, and dinner.

    e.    Advertisement Optimizer

    QSRs can use data to optimize the advertisement budget and adequately improve return on investment. They can track the visibility of advertisement banners across locations and optimize them for different times of the day. Data analysis can also help restaurants understand which customer segments are more likely to convert to long-term loyalists. This data will help QSRs design personalized campaigns and align advertisement budgets while converting them to long-term customers, further improving the bottom line.

    Ad spends by identifying carousels with the highest visibility
    Ad spends by identifying carousels with the highest visibility
    Track QSRs performance across Carousels across multiple zip codes
    Track QSRs performance across Carousels across multiple zip codes

    f.     Growth & Expansion

    Upselling and cross-selling are two popular tactics that improve growth for quick-service restaurants. However, that requires a rich understanding of customers’ price sensitivity, preferences, and behavior. QSR intelligence can provide information about which upsell and cross-selling offers a customer segment is likely to value and which optimal channels for distributing the offer.

    Conclusion

    Quick service restaurants can track critical data points and use them to increase revenue and improve customer experience. Learning how to price, promote, and deliver food to customers during a pandemic can be challenging. QSR intelligence will help brands attract the right clientele, adjust inventory, reduce overall marketing costs, and increase order rates. This will also help increase customer loyalty across segments which can, in turn, increase the number of returning customers and profitability.

  • What Historical Pricing Data can tell you & how to use it

    What Historical Pricing Data can tell you & how to use it

    For many brands, pricing strategy boils down to guesswork — shooting in the dark and hoping consumers are willing and happy to pay. However, the ‘throw it at the wall, and see what sticks’ pricing strategy leads to big pricing mistakes. Pinning down an optimal price for products requires a clear picture of ideal customers, understanding each customer segment’s behavior, a solid grasp of your product’s value, and an analysis of competitors. Pricing analytics can help brands track a wide range of pricing metrics with cutting-edge analytical tools and use insights to get ahead of their competition. This analysis uses historical data to understand how previous pricing and promotion activities affect brand, sales, and customer price perception. It often involves identifying opportunities and weaknesses in competitors’ pricing strategies and exploiting them to improve sales and revenue. 

    Pricing analytics helps brands understand how product pricing and promotions affect profitability and the steps they can take to optimize their pricing structures. Brands can leverage their pricing and consumer data to design appropriate pricing models for achieving their sales goals.

    Here is a brief overview of pricing analytics, its benefits, and ways to improve sales with historical pricing analytics.

    What is historical pricing data analytics?

    historical pricing data analytics
    Historical Pricing Data Analytics

    Pricing analytics uses historical pricing and demand data to understand how pricing activities have affected profitability and overall brand. It also helps to optimize a brands’ pricing strategy for maximum revenue. Manual tracking of pricing for brands with numerous product lines, multiple selling points, different customer tiers, and complex product bundles is a huge challenge. Brands from every sector and industry vertical, manufacturing and distribution to retail and eCommerce, can benefit from pricing analytics.

    There are three types of pricing analysis:

    Descriptive

    Descriptive pricing analytics involves analyzing historical data to evaluate how customers have perceived and reacted to pricing fluctuations in the past. It analyzes metrics such as month-on-month sales growth, average revenue per customer, year-on-year pricing changes, or changes to the number of registrations to a particular service over a specific period. 

    Predictive

    Although brands can’t accurately predict how pricing changes will reflect sales, they can use predictive pricing analytics to get insights into the best possible chance of doing so. Predictive pricing analyzes historical data with statistical algorithms and machine learning to predict the price and trends of products in the future. It also helps brands to optimize their prices with future goals.

    Prescriptive

    Prescriptive pricing analytics is the opposite of descriptive analytics. Unlike descriptive analytics that helps brands explore their historical data to understand customer response after an event, prescriptive analytics help brands design better and more informed strategies. With prescriptive analytics, brands can shape their growth strategies to achieve more sustainable results over the long term.

    Benefits of historical pricing data analytics

    Benefits of historical pricing data analytics
    Benefits of Historical Pricing Data Analytics

    Acquire insights into customers price perception

    While analyzing the metrics to understand pricing optimization, brands can also gather valuable insights into their customer’s price perception. Pricing analytics helps brands understand which customer segments are the most (and least) profitable and how each segment responds to specific pricing strategies. With historical pricing data analytics, brands can also intelligently link pricing and promotions by first determining customer price sensitivity then gauging the effectiveness of promotions. 

    Fully Optimized Pricing

    Historical pricing analytics means eliminating guesswork from deciding the optimal pricing for a given product. By analyzing historical pricing data, brands can discover how their past pricing and promotional decisions impact profitability. Based on this historical data, they can also test various pricing strategies like value-based and dynamic pricing. It also allows brands to learn which customer segments are most likely to respond positively to price change. These insights from pricing analytics will drive more effective (and profitable) pricing decisions.

    Recognize pricing tiers that work the best

    Many brands have gaps in their pricing strategy — underpriced or overpriced tiers, pricing leaks, markup errors, or neglected upsell opportunities. Tiered pricing models are prevalent in subscription-based brands where brands offer tiers to meet the needs of diverse customer segments. With historical pricing analytics, brands can improve their pricing tiers and get insight into the right number of tiers and optimal prices for each. Pricing analytics will comb a brand’s historical data to find tier pricing mistakes to improve sales and revenue.

    Planning Pricing Strategies and Promotions

    Promotional pricing decisions are critical for any brand, as pricing perception is directly linked to consumer demand and profits. Brands have to carefully plan promotions that include variables such as list prices, special offers, advertisements, and discounts while ensuring profit margins. With predictive analytics, brands can determine optimal discount levels, keep a close eye on the competition, and announce promotional offers when customers are likely to purchase. Historical pricing analysis also helps predict revenue and determine optimal locations and platforms for promotional ads.

    Discover profitable channels

    Not all sales channels bring equal revenue to your brand. Historical pricing analysis can help you determine the most effective quality, volume, and revenue channels. Brands must understand which marketing and sales channels bring quality leads that convert to paying customers. It also helps to determine which eCommerce channels are most profitable so you can optimize your budget and identify channels you should be investing in as a part of future customer acquisition strategies. 

    Metrics to track

    Metrics to track
    Metrics to Track

    Here are a few pricing analytics metrics that can help brands to understand customer behavior towards pricing:

    Willingness to Pay (WTP)

    WTP, also known as price sensitivity, is the maximum price your potential customers are willing to pay for your service or product. It is an essential part of pricing strategy since you have no other way of understanding whether your product can yield an augmented product value. Numerous factors are responsible for a customer’s willingness to pay, and it’s not static. Brands must track willingness to pay for all customer segments to ensure that the product is priced competitively and drives maximum profit while staying in line with current market conditions. 

    Feature Value Analysis

    Feature value analysis, also known as relative reference analysis, measures the most important features to customers in relation to other features of a product or service. Analyzing critical features to customer segments will help brands price products based on basic or premium components. It can also help to better bundle your services or products so you can drive the most revenue. 

    Average Revenue per User (ARPU)

    The average revenue per user is the revenue generated from the sum of active users divided by the total number of users in a monthly time frame. Delving deeper into ARPU can help brands compare numbers with rivals and check how all products or customer segments perform. 

    Lifetime Value (LTV)

    Lifetime Value offers a complete picture of a user’s journey and the average revenue that the user will generate throughout their engagement as a customer with your brand. It helps brands determine various economic decisions such as marketing budgets, profitability, forecasting, and resource allocation. 

    Customer Acquisition Cost (CAC)

    A successful and profitable brand needs to balance its customer acquisition cost or CAC. It is about spending the right amount of resources and time to drive new customers without jeopardizing their lifetime value and revenue. Correct calculation of CAC helps brands to quantify their sales funnel and determine the efficiency and profitability of their strategies.

    Conclusion

    Historical pricing analytics is a powerful tool, and it can make a huge difference to a brand’s potential by increasing sales and unlocking incredible profitability in a relatively short time. Historical analysis of pricing and promotions data will help brands get better marketing returns than relying on traditional pricing approaches. 

    Leveraging pricing analytics will prevent brands from blindly reacting to competitor price changes and support solutions for scaling up price transformation efforts. By using historical pricing data, brands can more effectively segment their customers for marketing and promotion strategies. Properly utilizing predictive analytics and past sales data can help cut costs and keep profit margins high by adjusting production and prices according to market trends.
    Need help tracking your competitor prices? Or want historic pricing insights for your own brand? Or need to track the efficacy of your online promotions?
    Sign up for a demo     with our team to know how DataWeave can help!

  • Best Practices to avoid MAP Violations

    Best Practices to avoid MAP Violations

    Competition is a fundamental and healthy part of commerce that protects customers by keeping prices low and the quality of services (and choice of goods) high.

     Healthy competition drives prices down, but it can harm brands and their reputation without a pricing policy. The manufacturer or brand designs MAP or Minimum Advertised Pricing policies to stipulate retailers’ lowest price point to advertise the product. It is an agreement between distributors and manufacturers about the minimum price that retailers and resellers can advertise the product for sale. 

    Most legitimate brands have a MAP policy, especially brands that rely heavily on brand identity. It becomes critical that they maintain price parity across retailers. When a retailer violates MAP policies, brands can penalize them under the agreed-upon terms or terminate contracts. 

    In this blog, you will learn about MAP policy, its benefits, and tips on tackling MAP violations. 

    1. What is a MAP policy?

    MAP policy
    MAP Violations

    MAP stands for Minimum Advertised Price, and brands create MAP policies to ensure that retailers don’t advertise their products below the specified price. However, it only controls advertised prices, ensuring the retailers don’t display a lower price in online listings or advertisements. Since it doesn’t cover the checkout price, retailers can sell products at a lower price through promotional offers like discounts and cashback during checkout. 

    MAP policies ensure a price war between eCommerce platforms does not devalue products and that an even playing field is set among retailers that allow everyone to drive margins. Brands have a legal right to withdraw products if a retailer advertises products below the minimum advertised price. Brands can also restrict future sales or refuse to replenish products after the current stock has sold out if an eCommerce platform, reseller, or distributor violates MAP policies. 

    In the U.S., MAP policies fall under federal antitrust law since they restrict advertisement pricing rather than the last sales price. However, in the UK and the EU, violation of minimum advertised pricing is an infringement of current competition laws.

    2. Why Does Having a MAP Policy Matter?

    Having a MAP policy protects both brands and retailers while ensuring consumers get the best-priced items. Following are the benefits of having a MAP policy:

    a. Prevent margin erosion

    Although online retailers are willing to take a margin cut to attract traffic, selling products below MAP can significantly hurt a brand’s bottom line. Setting a minimum advertised price benefits both parties. It allows shoppers to purchase products at the best-valued price & also creates a balanced economy and prevents hyper-competition of products between retailers. However, manufacturers must set a realistic pricing policy that matches current market demand, ensuring eCommerce platforms implement MAP while taking care of the margins. 

    b. Retain brand identity

    pricing policy
    Brand Protection

    Price is one of the essential indicators consumers use to determine the authenticity and value of a product. Constant price fluctuations can negatively impact a brand’s reputation. Brands need to safeguard their pricing to create a consistent price perception. Price changes often make the buying decision complex since consumers no longer have a clear reference of prices. It also shifts purchasers’ attention from the brand and product features to its price. With price fluctuations, brands that were used to be differentiated for their features can be seen as commodities.

    Low prices & MAP violations on an online platform can even be a sign of counterfeit products or unauthorized sellers. However, customers might hold the brand responsible if they purchase counterfeit products from a retailer at lower prices. A negative product experience with a retailer will also reflect the brand’s reputation. An effective MAP policy that enforces consistent pricing will ensure that customers hunting for the best deals will stick with the most legitimate retailers.

    Read how DataWeave helped Classic Accessories, a leading manufacturer of high-quality accessories detect counterfeits and identify unauthorized sellers.

    c. Ensure price parity across retailers

    Comparing prices has become an essential and common milestone in every consumer’s purchasing journey. It’s imperative that a brand ensures price parity across platforms and stores because substantial pricing variations on different platforms can make customers suspicious of a brand. Consistent pricing across eCommerce platforms ensures brands maintain their identity. MAP policies also allow retailers to maintain profit margins while avoiding price wars.

    d. Combat revenue loss from illegitimate sales

    While most authorized sellers or distributors comply with pricing policies, unauthorized sellers or grey market sellers have no obligation to follow a brand’s MAP pricing infrastructure. Brands can reduce risk with an authorized seller badge on retailer websites. This will help customers to verify authorized retailers and resellers of your products & help safeguard your brand equity online. 

    3. Tips on Implementing MAP policy and Tackling violations

    Enforcing and tackling MAPs comes down to two things: monitoring the market for infringements and then acting on those violations. Here are a few tips for tackling MAP violations:

    price parity
    Implementation of MAP Policy & Tracking Violations

    a. Communicate actively with retailers

    To maintain a positive relationship with retailers and avoid confusion, brands should create proper communication strategies and channels to accompany the launch of the MAP policy. The policy should be easy to understand, but legal advisors are necessary to understand the jargon of the document. Brands can use checklists, videos, and well-briefed brand reps to communicate their policy clearly with retailers.

    b. Reward retailers for compliance

    Retailers who follow MAP guidelines can lose out to platforms that do not follow these pricing guidelines. Non-MAP following platforms undercut the price of products to drive sales and secure higher traffic. In such instances, brands can incentivize MAP following retailers to encourage them to comply with MAP guidelines while not affecting the competitive edge. It can be in the form of laxity of rules during promotion seasons like New Year, Christmas, and Black Friday sales. The laxity of rules for promotional seasons should be used as an exception to the general rule, and outlined in the guidelines.  

    c. Implement an AI-driven MAP monitoring

    When product distribution is spread across the globe through a network of resellers and retailers, keeping a close watch on all platforms for multiple products can become difficult. With the expansion of online marketplaces, manually tracking the pricing of numerous products on multiple platforms is time-consuming and unsustainable. An automated AI-driven monitoring platform can track the pricing of all products sold across hundreds of online platforms and identify violations around the clock. Such platforms can alert brands of violations, price inconsistencies, or suspicious activities in real-time. 

    d. Send cease and desist to MAP violators and unauthorized dealers

    Brands must enforce a MAP policy to ensure price parity among retailers and resellers. Brands must systematically monitor prices across retailers, social media, marketplaces, and price comparison websites. Whenever brands encounter a MAP violation, they should take action by sending a cease and desist letter to unauthorized sellers. For legitimate sellers, brands can notify them and outline the steps that will be taken if they don’t comply. Brands must be consistent in enforcing MAP policy violations, signaling retailers and unauthorized sellers that there will be repercussions for MAP violations. 

    Market Demand
    MAP Policy

    4. Conclusion

    The trend towards online shopping helps businesses to cut overheads, allowing their products to be sold at a significantly reduced price. Although price appears to be the most effective consumer attraction strategy, significantly lowering product prices can devalue products and hurt brand reputation in the long term. However, including and enforcing MAP policies helps brands to manage their reputation and allows retailers to manage their margins. 

    Want to see first-hand how DataWeave can help brands track MAP Violations, Counterfeit products, and identify unauthorized sellers? Sign up for a demo with our Digital Shelf experts to know more.

  • Importance of Image Recognition in the Retail Industry

    Importance of Image Recognition in the Retail Industry

    When it comes to classifying and analyzing images, humans can easily recognize distinct features of objects and associate them with individual definitions. However, visual recognition is a highly complex task for machines because it involves identifying multiple objects and finding object relationships. Image recognition has been a long-standing research problem in the computer vision field. But, the recent development in AI has improved the process of object detection, image identification, and image classification. The image recognition market is assumed to rise globally to a market size of $42.2 billion by 2022. Various industries are adopting image recognition technology to improve augmented reality applications, optimize medical imagery, boost driverless car technology, predict consumer behavior, and much more. 

    Although image recognition is a relatively new aspect of analysis, it is also making its way into eCommerce. Image recognition is helping retailers to expand consumer reach, offer insights into trends, and improve customers’ online shopping experience for the eCommerce industry. The Global Image Recognition in Retail Market is estimated to be USD 1.8 Bn in 2021 and is expected to reach USD 4.5 Bn by 2026, growing at a CAGR of 20%.

    Image Recognition
    Global Image Recognition in Retail Market

    In this blog, you’ll learn about image recognition technology and its importance in the retail industry. 

    What is Image Recognition?

    Image recognition, a subcategory of computer vision, is a technology that can identify objects, entities, or attributes in digital images or videos. However, computer vision is a broader term, including methods for gathering, processing, and analyzing data from the real world. Image recognition can be performed at varying degrees of accuracy, depending on the type of information required.

    Image recognition can perform the following tasks:

    Object Detection, Semantic Segmentation & Instance Segmentation
    Object Detection, Semantic Segmentation & Instance Segmentation
    • Classification: It identifies the “class,” i.e., the category to which an image belongs. A picture can have only one class.
    • Tagging: It’s a classification task but involves a higher degree of accuracy. Tagging can recognize several concepts or objects within an image, and there can be more than one tag assigned to a particular image.
    • Detection and localization: This step helps locate object(s) in an image. Once the system locates the object in question, localization helps to place a bounding box around it. 
    • Segmentation: This is also a detection task but involves a higher degree of precision. Segmentation locates element(s) to the nearest pixel in an image. 
    • Instance segmentation: It helps differentiate multiple objects belonging to the same class. 

    Image Recognition in eCommerce and how it works

    Nowadays, increasing competition and customer expectations are forcing online retailers to constantly monitor market dynamics wrt their pricing, promotion & product assortment in order to stay competitive. To get these insights, retailers need to match and compare their products against their competitors to see where the gaps are. That’s where product matching comes in. 

    Product matching refers to finding the same or similar products against a target universe of products from across the web, across multiple competing retailers. Product matching uses AI-based image recognition to determine product attributes, find patterns, and detect text, product price, shipping information, and so on. 

    Here’s how DataWeave’s AI-powered analytics platform uses image recognition & aggregates insights & data for retailers from across the web to provide a comprehensive view of the online competitive environment.

    Image recognition use-cases in the retail industry

    a. Attribute tagging

    Attribute Tagging
    Attribute Tagging

    Getting shoppers to your eCommerce platform is one thing and getting them to complete a purchase is a steeper hill to climb. If your platform can’t provide search results that match with customers’ requirements, they’ll get lost, grow frustrated, and drop off. Attribute tagging with image recognition allows eCommerce stores to automatically generate attributes for all products so customers can quickly find products they are looking for. 

    Tags allow users to filter products based on the categories they want to explore. Product tags include everything the customer might specifically search for — color, type, size, brand, use, design, fabric, discount, etc. For example, a dress could have tags like red, evening, midi, summer, long-sleeve, silk, summer sale, etc. When a user looks for midi dresses or long-sleeve dresses, products with these tags will show up. 

    b. Search by image

    Visual Search
    Visual Search

    Visual Search allows users to look for similar products using a reference image from their camera roll or downloaded from the internet. The visual search feature also enables eCommerce businesses to implement image-based search into their software applications. It maximizes the searchable potential of their visual data. 

    Meanwhile, Gartner predicts a 30% increase in digital commerce revenue by 2021 for companies who start supporting visual and voice search on their websites and apps. The benefits of visual search include more personalized, easy product recommendations and enhanced product discovery.

    c. Fashion trend analysis

    similarity matching
    Similarity Matching

    Tapping into trending product categories is a goldmine for any eCommerce business. Having insights into trending categories and products means less competition on search engines, fewer ads, and intelligent pricing. All of which can boost any retailer’s margins. Image recognition technology provides information about colors, styling techniques, fabric textures, prints, and more to spark consumer demand. It works by scanning social media images to pinpoint trending attributes and predict fashion trends. For instance, while scanning images, technology understands that it’s seeing a photo of a color-blocked sweatshirt because it recognizes the product has a hooded neck, full sleeves, blocks of different colors, and even the type of fabric. This technology can analyze millions of images, helping retailers analyze the volume of color-blocked sweatshirts. 

    We do this seamlessly at DataWeave. Our similarity matching solution helps retailers gather insights into attributes for products similar to the ones they’re carrying on their site. Similarity matching helps retailers gain visibility into their entire competitive landscape to keep their e-commerce strategy responsive to price & product assortment shifts among consumers and rivals

    d. Augmented reality

    According to Statista, the AR market is valued at $9.5 billion, with around 810 million active mobile users. Since shoppers want the full sensory product experience before shopping online, augmented reality (AR) can help them understand what they’re buying and how the product will work for them. There are AR applications for trying makeup, clothing, accessories, and even eyeglasses. IKEA was one of the pioneers in using AR for eCommerce retail. In 2017, IKEA launched the Place app, allowing shoppers to see how thousands of items will look in their homes, with 98% accuracy. 

    Image recognition helps AR applications anchor virtual content with the real world. For instance, Sephora has a Virtual Artist that allows users to try different makeup looks and even take pictures of an outfit they’re planning to wear to match the shade. Users can even check out full-face looks and learn how to do their makeup with virtual tutorials. 

    e. Counterfeit Detection

    Counterfeit Detection
    Counterfeit Detection

    Another application of image recognition that has proven to be very successful is counterfeit product detection. It has become increasingly difficult for brands and retailers to find and eliminate fake items on eCommerce sites. U.S. Customs seized over 13,500 counterfeit goods worth $30 Million in November 2021, indicating how brands and online marketplaces have struggled in the past to find an effective solution. 

    Essentially, image recognition technology allows eCommerce sites to detect products with fake logos and designs attempting to sell as legitimate brands by capturing discrepancies in images and content. The system flags and delists the products and sellers when a fake is detected.

    Here’s how DataWeave helped Classic Accessories, a leading manufacturer of high-quality covers, furnishings, and accessories automate their counterfeit detection process using our super Image Recognition capabilities. 

    f. User-generated content analysis

    Visual content plays a vital role in eCommerce sites, especially when it comes to product photos and videos. Today, branded visual content isn’t as effective as it’s one-dimensional. As a matter of fact, 93% of marketers agree that customers trust user-generated content more than content produced by brands. However, user-generated content that features product images or videos is way more exciting, realistic, and creative. It gives customers an appealing view of products being used in real life. 

    The most common form of UGC, i.e., reviews and ratings, have been the key for eCommerce brands as they are quantitative and qualitative metrics about a product/service quality, worth, value, reliability, etc. With image recognition, retailers can access insights into strengths and gaps in all product offerings by understanding what consumers are saying about them. 

    Here’s how DataWeave can help retailers and brands analyze consumer reviews & help them adapt to customer needs.

    Conclusion

    Because of its massive influence, image recognition technology is becoming widely adopted by eCommerce companies. It benefits both retailers and customers. Image recognition based on deep learning can provide retailers with helpful capacities like customer analytics, counterfeit detection, personalized searches, and more. Retailers can also use the data gathered from image recognition eCommerce technology to design effective marketing campaigns and improve their ROI.

    With super sharp image recognition capabilities, DataWeave offers 90% accuracy in matching eCommerce products, allowing us to provide comprehensive and precise insights into pricing and assortments. Sign up for a demo with our team to know more.

  • Top 7 AI tools for your eCommerce business

    Top 7 AI tools for your eCommerce business

    The 2020 global health crisis sped up the adoption of omnichannel shopping and fulfillment. Consumers spent $791.70 billion online with U.S. merchants in 2020, a 32.4% rise compared to 2019. To keep up with this digital shift, offline businesses have substantially moved investments to online infrastructures for everything from e-commerce platforms, product recommendations, inventory management, and communications. AI tools for eCommerce have played a major role in helping businesses in the digital shift. 

    However, the benefits of setting up e-commerce stores are potentially outweighed by the increased costs. As markets transition to online retailers, they must learn to efficiently collect, secure, and analyze data coming in from multiple sources. Strategically approaching the data problem with artificial intelligence (AI) can help better serve customers, gain a competitive advantage, and drive loyalty.

    In this blog, you will learn about seven data and AI tools for eCommerce businesses:

    Seven data and AI tools for eCommerce businesses
    Seven Data and AI tools for eCommerce businesses

    1. Data Warehouse

    Data is the one advantage that eCommerce merchants and marketers have over brick and mortar retailers. When buyers are from the internet, eCommerce retailers can collect data and measure almost every aspect of their interactions. However, that advantage is worthless unless there is a system to make sense of the data they collect. Companies assume that they have a sound system in place. But, what they have is a network of silos. In such a system, data sticks to different platforms like Google Analytics, Shopify, or Klaviyo and can’t move to deliver valuable insights. Funneling all your data into a single location for your eCommerce stores is the right way to go. Data warehouses centralize and merge a plethora of data from various sources, helping organizations to derive valuable business insights and improve decision-making. 

    Data Warehouses support real-time analytics and ML operations quickly & are designed to enable and support business intelligence (BI) activities like performing queries and analysis on a colossal amount of data. Data could range from customer-related data, product or pricing data, or even competitor data. 

    However, the time needed to gather, clean, and upload the data to the warehouse is a time-consuming process. Here’s where DataWeave’s AI-Powered Data Aggregation & Analysis Platform can help! Get critical insights on your competitor’s pricing, assortment, and historical sale trends with a real-time dashboard. Build a winning eCommerce strategy with market intelligence without the need to store your data. 

    2. Data Lake

    Data Lake

    A data lake is a centralized repository that can store structured and unstructured data at any scale. Companies don’t have to provide a schema to the data before storing it, but they still can run different analytics and ML-related operations. However, it takes more time to refine the raw data and then analyze or create ML models for predictions. 

    An Aberdeen survey saw businesses implementing a Data Lake outperforming similar companies by 9% in organic revenue growth. The organizations that implemented Data Lake could perform various analytics over additional data from social media, click-streams, websites, etc. A Data Lake allows for the democratization of data and the versatility of storing multi-structured data from diverse sources, improving insights and business growth. 

    eCommerce businesses can collect competitors’ data in data lakes like their popular products, categories, landing pages, and ads. Analyzing competitors’ data helps retailers price their products correctly, helps with product matching, historical trend analysis, and much more. However, data lakes can also be used to store consumer data such as who they are, what they purchase, how much they spend on average, and how they interact with a company. Successful retailers leverage both competitor and consumer data to understand their consumers better, what brands to carry, how to price each product, and what categories to expand or contract. Retailers also store identity data such as a person’s name, contact information, gender, email address, and social media profiles. Other types of data stored are website visits, purchase patterns, email opens, usage rates, and behavioral data. 

    The major challenge with a data lake architecture is that it stores raw data with no oversight of the contents. Without elements like a defined mechanism to catalog and secure data, data cannot be found, or trusted resulting in a “data swamp.” Consequently, companies need teams of data engineers to clean data for data scientists or analysts to generate insights. This not only increases the turnaround time of gaining valuable information but also increases operational costs.

    However, you can rely on platforms like DataWeave that stores competitor pricing & assortment information at a centralized location. You can leverage intelligently designed dashboards to get real-time insights into the collected data and make data-driven decisions without the need for storing, cleaning, and transforming the data.

    3. Data Ingestion & ETL

    To churn out better insights, businesses need access to all data sources. An incomplete picture of data can cause spurious analytic conclusions, misleading reports and inhibit decision-making. As a result, to correlate data from multiple sources, data must be in a centralized location—a data warehouse or a data lake. However, extracting and storing information into these systems require data engineers who can implement techniques like data ingestion and ETL.

    While data ingestion focuses on getting data into data lakes, ETL focuses on transforming data into well-defined rigid structures optimized and storing it into a data warehouse for better analytics workflows. Both processes allow for the transportation of data from various sources to a storage medium that an organization can access, use, and analyze. The destination can be a data warehouse in the case of ETL and a data lake in case of data ingestion. Sources can be almost anything from in-house apps, websites, SaaS data, databases, spreadsheets, or anywhere on the internet.

    Data ingestion & ETL are the backbones of any analytics/AI architecture since these processes provide consistent and convenient data, respectively. 

    4. Programming languages

    Programming languages

    Programming languages are tools used by programmers to write instructions for computers to follow since they “think” in binary—strings of 1s and 0s. It serves as a bridge that allows humans to translate instructions into a language that computers can understand. Some common and highly used programming languages for building AI models are Python and R.  

    While Python is the most widely used language for training and testing models, R is mostly embraced for visualizations and statistical analysis. However, to productize the ML models, you would require Java programming language so that models can be integrated with your websites to provide recommendations.

    5. Libraries/AI frameworks

    An AI framework is a structure that acts as a starting point for companies or developers to add higher-level functionality and build advanced AI software. A framework serves as a foundation, ensuring that developers aren’t starting entirely from scratch.

    Using AI frameworks like TensorFlow, Theano, PyTorch, and more saves time and reduces the risk of errors while building complex deep learning models. Libraries and AI frameworks also assist in building a more secure and clean code. They future aid developers in simpler testing and debugging.

    Various open-source frameworks in the market also come with pre-trained models for specific use cases. Organizations can leverage off-the-shelf models and tweak with existing data to enhance the accuracy of the predictions.

    6. IDE & Notebooks tools

    IDE or Integrated Development Environment is a coding tool that allows developers to write and test their code more efficiently. However, notebooks are one of the most popular AI tools for organizations to execute analysis and other machine learning tasks. It offers more flexibility over IDEs in terms of exploratory analysis.

    All the features, including auto-complete, that IDEs or notebooks offer are beneficial for development as they make coding more comfortable. IDEs/Notebooks increase developers’ productivity by combining common software activities into a single application: building executables, editing code, and debugging.

    7. Analytics tools

    Competitive Pricing

    Data Analysis transforms raw data into valuable statistics, insights, and explanations to help companies make data-driven business decisions. Data analytics tools like PowerBI and Tableau have become the cornerstone of modern business for quickly analyzing structured and semi-structured data. 

    However, these platforms aren’t optimized specifically for the eCommerce industry. Consequently, you should embrace analytical tools particularly designed for eCommerce companies to make better decisions about product assortment, pricing, and promotions. With data analytics, companies can gain insights into the most popular and discoverable brands on their own and competitors’ platforms. Paired with attribute matching, competitive intelligence gives a deeper understanding of the latest trends and why certain products are popular with your customers. Some more meaningful metrics that retailers can track are discount gap, price gap, catalog strength, and product type gaps. 

    Competitive pricing is another benefit of data analytics with which retailers can identify gaps and keep up with actionable pricing insights. Retailers get to maximize profits and respond to demand by cashing in on insights into rivals’ pricing. With the right analytics tools, they can also track changes in pricing across crucial metrics such as matched products, recent price changes, highest price positions, stock status, and much more. 

    Analytics tools can also help eCommerce companies to capture information about competitors’ promotional banners through AI-powered image analysis. It can provide insights into how and where to spend promotional expenditure. 

    Conclusion

    This listicle discusses some of the AI and data tools commonly used by the eCommerce industry. Data analytics has become a popular method for retailers to understand their customers and boost productivity. Data analytics help companies improve customer experience, improve customer loyalty, generate insights, and advise on data-driven actions. Business intelligence tools can help companies monitor key performance indicators (KPIs), perform proper data analyses, and generate accurate reports. 

    Want to learn how DataWeave can help make sense of your and your competitor’s pricing, promotional, and assortment data? Sign up for a demo with our team to know more.

  • How Brands Can Outperform Rivals With Next-Gen Digital Shelf Analytics

    How Brands Can Outperform Rivals With Next-Gen Digital Shelf Analytics

    As eCommerce grows in complexity, brands need new ways to grow sales and market share. Right now, brands face urgent market pressures like out-of-stocks, an influx of new competition and rising inflation, all of which erode profitability. As online marketplaces mature, more brands need to make daily changes to their digital marketing strategies in response to these market pressures, shifts in demand, and competitive trends.

    eMarketer forecasts 2021 U.S. eCommerce will rise nearly 18% year-over-year (vs. 6.3% for brick-and-mortar), led by apparel and accessories, furniture, food and beverage, and health and personal care. The eCommerce industry is also undergoing fundamental changes with newer entities emerging and traditional business models evolving to adapt to the changed environment. For example, sales for delivery intermediaries such as Doordash, Instacart, Shipt, and Uber have gone from $8.8 billion in 2019 to an estimated $35.3 billion by the end of 2021. Similarly, many brands have established or are building out a Direct to Consumer (D2C) model so they can fully own and control their customer’s experiences.

    In response, DataWeave has launched the next generation of our Digital Shelf Analytics suite to help brands across retail categories directly address today’s costly market risks to drive eCommerce growth and gain a competitive advantage.

    “Our new enhancements help brands improve online search rank visibility and quantify the impact of digital investments – especially in time for the busy holiday season.”  
    ~ Karthik Bettadapura, CEO and co-founder, DataWeave

    The latest product enhancements provide brands access to tailored dashboard views that track KPI achievements and trigger actionable alerts to improve online search rank visibility, protect product availability and optimize share of search 24/7. Dataweave’s Digital Shelf Analytics platform works seamlessly across all forms of eCommerce platforms and models – marketplaces, D2C websites and delivery intermediaries.

    Dashboard for Multiple Functions

    While all brands share a common objective of increasing sales and market share, their internal teams are often challenged to communicate and collaborate, given differing needs for competitive and performance data across varying job functions. As a result, teams face pressure to quickly grasp market trends and identify what’s holding their brands back.

    In response, DataWeave now offers executive-level and customized scorecard views, tailored to each user’s job function, with the ability to measure and assess marketplace changes across a growing list of online retail channels for metrics that matter most to each user. This enhancement enables data democratization and internal alignment to support goal achievement, such as boosting share of category and content effectiveness. The KPIs show aggregated trends, plus granular reasons that help to explain why and where brands can improve.

    Brands gain versatile insights serving users from executives to analysts and brand and customer managers.

    Prioritized, Actionable Insights

    As brands digitize more of their eCommerce and digital marketing processes, they accumulate an abundance of data to analyze to uncover actionable insights. This deluge of data makes it a challenge for brands to know exactly where to begin, create a strategy and determine the right KPIs to set to measure goal accomplishment.

    DataWeave’s Digital Shelf Analytics tool enables brands to effectively build a competitive online growth strategy. To boost online discoverability (Share of Search), brands can define their own product taxonomies across billions of data points aggregated across thousands of retailer websites. They can also create customized KPIs that track progress toward goal accomplishment, with the added capability of seeing recommended courses of action to take via email alerts when brands need to adjust their eCommerce plans for agility.

    “Brands need an integrated view of how to improve their discoverability
    and share of search by considering all touchpoints in the digital commerce ecosystem.”
    ~ Karthik Bettadapura, CEO and co-founder, DataWeave

    Of vital importance, amid today’s global supply chain challenges, brands gain detailed analysis on product inventory and availability, as well as specific insights and alerts that prompt them to solve out-of-stocks faster, which Deloitte reports is a growing concern of consumers (75% are worried about out-of-stocks) this holiday season.

    User and system generated alerts provide clarity to actionable steps to improving eCommerce effectiveness.
    You also have visibility to store-level product availability, and are alerted to recurring out-of-stock experiences.

    Scalable Insights – From Bird’s Eye to Granular Views

    DataWeave’s Digital Shelf Analytics allows brands to achieve data accuracy at scale, including reliable insights from a top-down and bottom-up perspective. For example, you can see a granular view of one SKUs product content alongside availability, or you can monitor a group of SKUs, say your best selling ones, at a higher level view with the ability to drill down into more detail.

    Brands can access flexible insights, ranging from strategic overviews to finer details explaining performance results.

    Many brands struggle with an inability to scale from a hyper-local eCommerce strategy to a global strategy. Most tools available on the market solve for one or the other, addressing opportunities at either a store-level basis or top-down basis – but not both.

    According to research by Boston Consulting Group and Google, advanced analytics and AI can drive more than 10% of sales growth for consumer packaged goods (CPG) companies, of which 5% comes directly from marketing. With DataWeave’s advanced analytics, AI and scalable insights, brands can set and follow global strategies while executing changes at a hyper-local level, using root-cause analysis to drill deeper into problems to find out why they are occurring.

    As more brands embrace eCommerce and many retailers localize their online assortment strategies, the need for analytical flexibility and granular visibility to insights becomes increasingly important. Google reports that search terms “near me” and “where to buy” have increased by more than 200% among mobile users in the last few years, as consumers seek to buy online locally.

    e-Retailers are now fine-tuning merchandising and promotional strategies at a hyper-local level based on differences seen in consumer’s localized search preferences, and DataWeave’s Digital Shelf Analytics solution provides brands visibility to retailer execution changes in near real-time.

    Competitive Benchmarking

    Brand leaders cannot make sound decisions without considering external factors in the competitive landscape, including rival brands’ pricing, promotion, content, availability, ratings and reviews, and retailer assortment. Dataweave’s Digital Shelf Analytics solution allows you to monitor share of search, search rankings and compare content (assessing attributes like number of images, presence of video, image resolution, etc.) across all competitors, which helps brands make more informed marketing decisions.

    Brands are also provided visibility into competitive insights at a granular level, allowing them to make actionable changes to their strategies to stay ahead of competitors’ moves. A new module called ‘Sales and Share’ now enables brands to benchmark sales performance alongside rivals’ and measure market share changes over time to evaluate and improve competitive positioning.

    Monitor competitive activity, spot emerging threats and immediately see how your performance compares to all rivals’, targeting ways to outmaneuver the competition.

    Sales & Market Share Estimates Correlated with Digital Shelf KPIs

    In a brick-and-mortar world, brands often use point of sale (POS) based measurement solutions from third party providers, such as Nielsen, to estimate market share. In the digital world, it is extremely difficult to get such estimates given the number of ways online orders are fulfilled by retailers and obtained by consumers. Dataweave’s Digital Shelf Analytics solution now provides sales and market share estimates via customer defined taxonomy, for large retailers like Amazon. Competitive sales and market share estimates can also be obtained at a SKU level so brands can easily benchmark their performance results.

    Additionally, sales and market share data can also be correlated with digital shelf KPIs. This gives an easy way for brands to check the effect of changes made to attributes, such as content and/or product availability, and how the changes impact sales and market share. Similarly, brands can see how modified search efforts, both organic and sponsored, correspond to changes in sales and market share estimates.

    Take Your Digital Shelf Growth to the Next Level

    The importance of accessing flexible, actionable insights and responding in real-time is growing exponentially as online is poised to account for an increasing proportion of brands’ total sales. With 24/7 digital shelf accessibility among consumers comes 24/7 visibility and the responsibility for brands to address sales and digital marketing opportunities in real-time to attract and serve online shoppers around the clock.

    Brands are turning to data analytics to address these new business opportunities, enhance customer satisfaction and loyalty, drive growth and gain a competitive advantage. Companies that adopt data-driven marketing strategies are six times more likely to be profitable year-over-year, and DataWeave is here to help your organization adopt these practices. To capitalize on the global online shopping boom, brands must invest in a digital shelf analytics solution now to effectively build their growth strategies and track measurable KPIs.

    DataWeave’s next-gen Digital Shelf Analytics enhancements now further a brand’s ability to monitor, analyze, and determine systems that enable faster and smarter decision-making and sales performance optimization. The results delight consumers by helping them find products they’re searching for, which boosts brand trust.

    Connect with us to learn how we can scale with your brand’s analytical needs. No project or region is too big or small, and we can start where you want and scale up to help you stay agile and competitive.

  • Top 10 Retail Analytics that You Must Know

    Top 10 Retail Analytics that You Must Know

    Customers expect personalization. Unless they have a seamless experience on your online channels, they’ll leave for a different retailer. Retail analytics can solve these problems for merchants looking to increase customer satisfaction and sales. It provides insights into inventory, sales, customers, and other essential aspects crucial for decision-making. Retail analytics also encompasses several granular fields to create a broad picture of a retail business’s health and sales, along with improvement areas.

    Big data analytics in the retail market
    Big data analytics in the retail market

    Big data analytics in the retail market is expected to reach USD 13.26 billion by the end of 2026, registering a CAGR of 21.20% during the forecast period (2021-2026). The growth of analytics in retail depicts how it can help companies run businesses more efficiently, make data-backed choices, and deliver improved customer service.

    In this blog, we’ll discuss the top 10 analytics that retailers are using to gain a competitive advantage in accurately evaluating business & market performance.

    Top 10 of Retail Analytics You Must Know
    Top 10 of Retail Analytics You Must Know

    1. Assortment

    Assortment planning allows retailers to choose the right breadth (product categories) and depth (product variation within each category) for their retail or online stores. Assortment management has grown beyond simple performance metrics like total sales or rotation numbers. Instead, retail analytics offers a comprehensive analysis of product merchandise and an estimated number of units at the push of a button. Retailers that effectively apply assortment analytics can enjoy increased gross margins and prevent significant losses from overstocks sold at discounted prices or out-of-stock inventory leading their customers to buy from competitors. 

    It also helps retailers gain insights into the trendy and discoverable brands and products on all e-commerce websites across the globe. They can boost sales by making sure they have an in-demand product assortment. They can also track pricing information and attributes common across popular products to drive their pricing and promotion strategies.

    2. Inventory Management

    An inadequately maintained inventory is every retailer’s worst nightmare. It represents a poor indicator of inadequate demand for a product and leads to a loss in sales. Data can help companies answer issues like what to store and what to discard. It’s beneficial to discard or increase offers on products that are not generating sales and keep replenished stocks of popular items. 

    Worldwide Inventory Distribution

    In 2020, the estimated value for out-of-stock items ($1.14 trillion) was double that of overstock items ($626 billion). A similar trend was especially prominent in grocery stores, where out-of-stock items were worth five times more than overstock items.

    Unavailability of high-selling products can lead to reduced sales, ultimately generating incorrect data for future forecasting and producing skewed demand and supply insights. Retailers can now use analytics to identify which products are in demand, which are moving slowly, and which ones contribute to dead stock. They can know in real-time if a high-demand product is unavailable at a specific location and take action to increase the stock. Retailers can use this historical data to predict what to stock, at what place, time, and cost to maintain and optimize revenue. It helps satisfy consumer needs, prevents loss of sales, reduces inventory cost, and streamlines the complete supply chain.

    3. Competitive Intelligence

    Market intelligence & Competitive Insights
    Market intelligence & Competitive Insights

    The ability to accurately predict trends after the global pandemic and with an unknown economic future is becoming the cornerstone for successful retailers. Smart retailers know how important it is to Pandemic-Proof their retail strategy with Market Intelligence & Competitive Insights 

    With 90% of Fortune 500 companies using competitive intelligence, it’s an essential tool to gain an advantage over industry competitors. Competitive Intelligence allows you to gather and analyze information about your competitors and understand the market–providing valuable insights that you can apply to your own business. A more strategic competitor analysis will explain brand affinities and provide insights on what to keep in stock and when to start promotions. Customer movement data will also give you access to where your customers are shopping.

    4. Fraud Detection

    Fraud Detection
    Fraud Detection

    Retailers have been in a constant struggle with fraud detection and prevention since time immemorial. Fraudulent products lead to substantial financial losses and damage the reputation of both brands and retailers. Every $1 of fraud now costs U.S. retail and eCommerce merchants $3.60, a 15% growth since the pre-Covid study in 2019, which was $3.13. Retail Analytics acts as a guardian against fraudsters by constantly monitoring, identifying, and flagging fraud products and sellers. 

    5. Campaign Management

    Some of the challenges of the retail industry are that it’s seasonal, promotion-based, highly competitive, and fast-moving. In today’s competitive marketplace, consumers compare prices and expect personalized shopping experiences. Campaign management allows marketing teams to plan, track, and analyze marketing strategies for promoting products and attracting audiences. Retail analytics can help businesses predict consumer behavior, improve decision-making across the company, and determine the ROI of their marketing efforts. 

    According to Invesp, 64% of marketing executives “strongly agree” that data-driven marketing is crucial in the economy. Retail analytics can help businesses analyze their data to learn about their customers with target precision. With predictive analysis, retailers can design campaigns that encourage consumers to interact with the brand, move down the sales funnel, and ultimately convert.

    6. Behavioral Analytics

    Retail firms often look to improve customer conversion rates, personalize marketing campaigns to increase revenue, predict and avoid customer churn, and lower customer acquisition costs. Data-driven insights on customer shopping behaviors can help companies tackle these challenges. However, several interaction points like social media, mobile, e-commerce sites, stores, and more, cause a substantial increase in the complexity and diversity of data to accumulate and analyze. 

    Insider Intelligence forecasts that m-eCommerce volume will rise at 25.5% (CAGR) until 2024, hitting $488 billion in sales, or 44% of all e-commerce transactions. 

    Data can provide valuable insights, for example, recognizing your high-value customers, their motives behind the purchase, their buying patterns, behaviors, and which are the best channels to market to them and when. Having these detailed insights increases the probability of customer acquisition and perhaps drives their loyalty towards you. 

    7. Pricing

    competitive pricing in retail
    Competitive pricing in retail

    Market trends fluctuate at an unprecedented pace, and pricing has become as competitive as it’s ever been. The only way to keep up with competitive pricing in retail is to use retail analytics that enables retailers to drive more revenue & margin by pricing products competitively. 

    A report from Inside Big Data found companies experience anywhere from 0.5% up to 17.1% in margin loss purely because of pricing errors. Pricing analytics provides companies with the tools and methods to perceive better, interpret and predict pricing that matches consumer behavior. Appropriate pricing power comes from understanding what your consumers want, which offers they respond to, how and where they shop, and how much they will pay for your products. 

    In 2021, the price optimization segment is anticipated to own the largest share of the overall retail analytics market. Retailers can identify gaps and set alerts to track changes across crucial SKUs or products with pricing analytics. Knowing your customer’s price perception will increase sales and also allow you to design promotions that’ll attract customers. Pricing analytics also accounts for factors like demographics, weather forecasting, inventory levels, real-time sales data, product movement, purchase history, and much more to arrive at an excellent price.  

    8. Sales and Demand Forecasting

    Sales and demand forecasting allow retailers to plan for levels of granularity—monthly, weekly, daily, or even hourly—and use the insights in their marketing campaigns and business decisions. The benefits of a granular forecast are apparent since retailers don’t have to bank on historical data of previous clients and customers to predict revenues. Retailers can plan their strategies and promotions that suit their customer’s demands. 

    With sales and demand forecasting, retailers can also consider the most recent, historical, and real-time data to predict potential future revenue. Sales and demand analytics can predict buying patterns and market trends based on socioeconomic and demographic conditions. 

    9. Customer Service and Experience

    With the development of eCommerce, more and more customers prefer to browse and interact with the product before purchasing online. They look for better deals and discounts across stores and platforms. 3 out of 5 consumers say retail’s investment in technology is improving their online and in-store shopping experiences. To enhance merchandising and marketing strategies, retailers can gather data on customer buying journeys to understand their in-store and online experiences. 

    Retailers can run test campaigns to know the impact on sales and use historical data to predict consumers’ needs based on their demographics, buying patterns, and interests. Retail analytics help retailers to bring more efficiency in promotions and drive impulsive purchases and cross-selling.

    10. Promotion

    Analyze competitors' promotions
    Analyze Competitors’ Promotions

    Promotions are potent sales drivers and need to be cleverly targeted towards specific customers with precise deals to generate outstanding sales. Retail analytics allows companies to study their customers and competitors to a vastly elevated level. 

    To be an industry leader, retail companies not only have to understand their customers, but they must also analyze competitors’ promotions to improve their marketing strategies. Analyzing your competitor’s promotional banners, ads, and marketing campaigns are no more associated with imitation. 

    With data analytics and AI, retailers can watch their competitors’ commercialization strategies. It can uncover vital information about their target audience, sales volume fluctuations, popular seasonal product types, product attributes of popular items, and significant industry trends.  Knowing exactly which products and brands are popular among your competitor’s campaigns can help retailers improve their promotional strategies. 

    Conclusion

    The benefits of retail analytics are spread across various verticals, from merchandising, assortment, inventory management, and marketing to reducing losses. The need for analytics has become even more apparent considering the growing eCommerce platforms, changing customer buying journeys, and the complexity of the industry. Understanding which products sell best among which customers will help retailers to deliver an optimized shopping experience.

    Want to drive profitable growth by making smarter pricing, promotions, and product merchandising decisions using real-time retail insights? DataWeave’s AI-powered Competitive Intelligence can help! Reach out to our Retail Analytics experts to know more.