The DataWeave Blog

Author: Osheen Jain

  • 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.

  • 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.

  • Quick Commerce in 2022: An Era of Hyperlocal Delivery

    Quick Commerce in 2022: An Era of Hyperlocal Delivery

    Busy lifestyles, urbanization, aging populations, and smaller households led to the preference for convenience and efficiency in eCommerce deliveries. However, the Covid-19 pandemic caused a massive shift in customer demand and buying decisions. The modern consumer journey moved from takeaway food to online shopping to quick or same-day deliveries. With evolving digital touchpoints, customers now favor fast deliveries and convenience. 

    According to a 2020 survey by KPMG in the UK, 43% of consumers chose next-day delivery, a 4% increase from last year. Interestingly, 17% of consumers abandoned a brand if they faced a longer delivery. Standard delivery time has shortened from 3 to 4 days and two-day shipping to next-day or same-day delivery. This increasing trend of quick delivery has led to the boom of quick commerce or Q-Commerce. Quick commerce or on-demand delivery refers to retailers that deliver goods in under an hour or as quickly as 10 minutes. The rise of Q-commerce is caused by changing consumer behavior and rising expectations since the pandemic. 

    In this blog, you’ll learn about quick commerce or Q-Commerce and its benefits. You’ll also read about factors to consider for quick commerce and tips to implement this business model. 

    1. What is Quick Commerce?

    on-demand delivery
    On-Demand Delivery

    Quick commerce or on-demand delivery is a set of sales and logistics processes that empowers eCommerce businesses, restaurants, grocery chains, and manufacturers to deliver products in less than 24-hours. A study shows that 41% of consumers are willing to pay for same-day delivery while 24% of customers will pay more to deliver their items within a one- or two-hour window.  

    Changing lifestyles and customer behavior directly impacted the rise of Q-Commerce. The takeaway food industry had used quick commerce for many years. But with Q-Commerce businesses consistently cutting delivery time, quick commerce for instant grocery delivery has become a new trend. For instance, India-based online grocery delivery firm Grofers rebranded to BlinkIt amid rising competition, promising 10-minute instant delivery. 

    2. How quick is Quick Commerce?

    The post-pandemic lifestyle & the rise in the number of small and single-person households has led to an increase in demand for products in small quantities that need to be delivered sooner than later. Sometimes in as little as 10 minutes! This trend is oriented towards specific products such as packed or fresh foods, Groceries, Food delivery, Gifts, Flowers, Medicines to name a few.

    quick delivery service
    Quick Commerce Categories

    Local shops that can reach more customers with less friction have swapped traditional brick-and-mortar warehouses to cater to an urban population. These online Q-Commerce stores can deliver goods from favorite stores and offer a vast choice of products that are available 24/7. However, it requires real-time inventory management, data-driven pricing management, innovative logistics technology, a fantastic rider community, and a proper assortment. 

    3. Factors to consider for Quick Commerce

    q commerce
    Competitive Assortment & Pricing

    a. Assortment

    With growing competition, getting product assortment right isn’t easy for quick commerce businesses, yet it’s critical to their success. To optimize assortment for quick commerce stores, they need to understand how demand differs between demographics and various stores. Since quick delivery involves packed and fresh products, it is even more essential to carry a unique assortment for each store. 

    Data analytics will help Q-Commerce businesses understand which products are repeatedly purchased in every store. It also helps identify high-demand gaps in your competitors’ platforms. Assortment analytics can help distinguish shifts in customer behavior across short- and long-term demands. The key to increasing sales is shaping inventory to match the overlap between market opportunity and consumer interest. With assortment analytics, they can determine the optimal mix of products for their daily inventory. 

    b. Pricing

    Pricing information is readily available on quick commerce businesses, allowing customers to compare prices before making purchase decisions. Before deciding on a product, shoppers actively track the best deals on platforms across various Q-Commerce delivery platforms. According to a survey, 31% of consumers rated price comparisons as the essential aspect of their shopping experience. Understanding price perception can help quick commerce companies to optimize their pricing strategy while remaining competitive. 

    A competitive pricing strategy does not imply that Q-Commerce businesses have to cut prices. Instead, it’s about adjusting prices relative to your competitors but not significantly impacting the bottom line. Competitive pricing provides real-time pricing updates, allowing quick commerce platforms to drive sales by nailing their pricing strategy. 

    c. Delivery Time

    delivery time
    Grocery Delivery Race In India

    Delivery time has become the game-changer in quick commerce, with platforms fighting over shorter delivery times. Unpredictable factors such as specific delivery windows, last-minute customer requests, and traffic congestion can wreak havoc in your planning. Optimizing your delivery time can improve operational efficiency through faster delivery, quick route planning, and driver monitoring. 

    Big eCommerce platforms like Amazon offer same-day or next-day delivery to prime members with no extra fee on minimum order criteria. The only demand of customers who do not worry about discounts or lower wholesale prices is quick delivery. The demand for quick delivery services has led to many global retailers offering same-day delivery to meet those expectations.

    d. Demand Forecasting

    Since quick commerce is a viable solution for certain products, businesses must determine what customers want and when they want it. Q-Commerce businesses can use historical data to predict future sales patterns with demand forecasting. It ensures that Q-Commerce businesses can limit wastages and their inventory can cater to a targeted market. Demand forecasting also helps to replenish stock based on real-time data. Furthermore, companies can identify bottlenecks and points of wastage in the supply chain with a demand-driven system in place.

    4. Benefits of Quick Commerce

    same day delivery
    Q-Commerce Benefits

    a. Competitive USP

    Q-Commerce businesses get new value propositions because customers that need immediate delivery are willing to try new brands and order from new stores. It also allows online Q-Commerce businesses to compete with global marketplaces and brick-and-mortar stores. 

    We at DataWeave have helped quick-service restaurants (QSRs) that are going the Q-Commerce route & selling via food aggregator apps to increase their revenue significantly. Our AI-Powered Food Analytic solutions have helped QSRs diagnose improvement areas, monitor key metrics, and drive 10-15% growth. Our data has helped them understand availability during peak times, monitor product visibility by region, track competitors, and choose suitable banners for promotion. Read more about that here.

    b. Increase margins

    A study from Deloitte suggests that 50% of online shoppers spend extra money to get convenient delivery of the products they need during the pandemic. These customers also paid extra for on-demand fulfillment and bought online pick-up in-store options. 

    Since the assortment of products in quick commerce is relatively small, Q-Commerce businesses can drive sales for their most profitable product lines. There is a potential for greater margins because wealthier demographics often require convenience. For instance, time-stranded professionals value convenience over discounts. 

    c. Customer experience is paramount

    With quick commerce, retailers can meet customer expectations and exceed them, fostering brand loyalty. Quick commerce addresses customer pain points such as running out of food before a small party or getting a birthday present for your friends. It can simply help people who cannot make it to the shop or stock up essentials.

    5. How to implement Quick Commerce

    quick delivery
    Implementation of Quick Commerce

    a. The need for local hubs

    To pack and deliver products in under an hour, businesses must be located close to the customers. Therefore, quick commerce relies on local warehouses that can serve customers in immediate proximity. Since the duration of two-wheelers is less likely to be impacted by heavy traffic or parking spaces, delivery services employ riders to deliver products.

    b. Ensure you have the right analytics in place

    Another essential part of running a quick commerce business is to have a web or phone application that can facilitate online ordering and offer accurate stock information to customers. Q-Commerce businesses also need a real-time inventory management tool that will provide insights into stock levels and allow for quick reordering and redistribution of products. This will also prevent deadstock and stockouts. 

    DataWeave’s Food Delivery Analytics product suite helps companies to increase order volumes, understand inventory, and optimize prices. It also provides access to discounts, offers, delivery charges, inventory, and final cart value across all your competitors. 

    c. It’s all about stock availability & assortment

    Q-Commerce in the Grocery Delivery space is excellent for specific product niches like packed or fresh foods and vegetables, drinks, gifts, cosmetics, and other CPG products that customers use every day.

    The stock assortment is as important in the Food Delivery space with restaurant chains like McDonald’s or Burger King that generate as much as 75% of their sales from online orders. These businesses have to make sure they’re carrying the most in-demand product assortment there is. 

    Conclusion

    same day delivery
    Same Day Delivery

    The rise of quick commerce represents the next big change in eCommerce, accompanied by a shift in consumer behavior towards online grocery shopping and food ordering. When positioned with proper assortment and pricing, instant delivery services can allow Q-Commerce businesses to capture the influx of consumers looking for speedy delivery. By tapping into big data from quick commerce markets, Q-Commerce businesses can gain insights into consumer demands. 

    If you’re a Q-Commerce business in the Food Delivery or Grocery Delivery space, reach out to our experts at DataWeave to learn how our solutions can help you understand the best Pricing Strategy, Delivery Time SLAs, Assortment Mix you need in order to successfully sell on Q-Commerce platforms. 

  • 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.

  • 6 Promotional Strategies for the Holiday Season

    6 Promotional Strategies for the Holiday Season

    For eCommerce companies, holidays are the busiest season of the year. Whether creating brand awareness with your marketing campaigns or freshening up your landing pages or finding new ways to segment & understand your customers, the list of tasks seems endless. It’s the time of the year when most people look forward to shopping for friends and family. 

    The holiday shopping season begins with Black Friday and Cyber Monday and leads to the December holidays, including Christmas and New Year. Consequently, proper planning and marketing are essential for a successful holiday season. 

    In fact, holiday sales during November and December are forecasted to be between $843.4B – $859B, up 10.5% over 2020, according to the National Retail Federation (NRF). For online stores specifically, sales are predicted to increase between 11% – 15% to a total of between $218.3B and $226.2B driven by online purchases.

    This guide will share eight promotional strategies retailers can use during the holiday season. We will also discuss how data analytics can help retailers improve their promotional strategies. 

    Using data analytics to guide promotional strategies

    Promotional Strategies
    Promotional Strategies

    Data is the foundation of every successful marketing campaign. Data analysis helps companies understand which graphics worked well and campaigns that generated the most revenue. Gathering data and running analysis helps companies improve their next marketing campaign. Retailers can also get deeper insights into campaigns/channels with the highest conversion rate or average order value (AOV).

    With data analytics, retailers can prioritize campaigns and channels that resonate the most with their customers this holiday season. But, it would be best to try more than one promotional strategy to ensure you double down on what works without placing all of your eggs in one Christmas-themed basket. 

    Here are four ways that data analytics can help guide promotional strategies:

    a. Customized alerts for listing pages

    Data analysis helps retailers determine if certain products are out of stock on their rival’s website and adjust their own pricing accordingly. It allows retailers to grab market share for trending items. For example, if you get an out-of-stock alert for a particular product at competitors’ stores, you can invest more in advertising that product on your online store. In addition, customized alerts keep retailers informed about their inventory status, allowing them to plan promotions and ads. They can see which products are becoming commoditized due to intense competition and which ones offer better revenue opportunities. 

    Learn how DataWeave can help retailers track their competitor’s stock and inventory status.

    b. Maximize conversions by tracking product trends

    Assortment Analytics
    Assortment Analytics

    Customers are always looking for products that are currently trending. With assortment analytics, eCommerce companies can get insights into hot trends, allowing them to stock in-demand categories and products. Integrating assortment analytics with AI-powered image analytics can also provide insights into attributes that are popular among customers. By filling gaps in their current assortments, retailers can improve conversion rates and increase revenue. 

    Here’s a case study on how DataWeave helped Douglas, a luxury beauty retailer in Germany boost sales by building an in-demand product assortment 

    c. Monitor competitor promotions

    Promotional Insights
    Promotional Insights

    With increased competition and consumer demand for deals, it has become important for retailers to monitor their competitor’s promotions. Monitoring promotions helps retailers to optimize their ad spend accordingly. AI-powered image analysis tools can capture important information from competitors’ ad banners and deliver insights into metrics that are working to deliver sales. 

    Here’s how DataWeave can help retailers make their marketing magnetic with competitive promotional insights! 

    d. Optimize margins with a data-driven pricing strategy 

    Pricing Intelligence
    Pricing Intelligence

    It has become challenging to price products in recent years since digital tools enable price transparency across channels. Although this trend is excellent for consumers, it makes competition fierce for retailers. A data-driven pricing strategy incorporates a variety of factors, including industry needs, competitor analysis, consumer demand, production costs, and profit margins. 

    With data-driven competitive pricing, retailers can keep pace with the changing eCommerce environment with real-time pricing updates. It also helps them optimize margins and quickly respond to changes in prices on rival stores. 

    Promotional Strategies for the Holiday Season

    a. Virtual Webrooms

    When customers want to see a product in-person, they go to a store showroom. It helps them make a purchase decision. However, with the Internet, eCommerce companies can bring this tactic online. The only difference between showrooming and webrooming is that the former takes in-person, whereas the latter happens digitally. Webrooming grew in popularity during the COVID-19 pandemic. Instead of spending weekends browsing stores, consumers took to the Internet for most of their product research.

    A webroom allows customers to explore products from every angle, providing them with the complete in-person showroom experience online. Webrooming is a powerful holiday marketing strategy, especially regarding expensive purchases. Customers prefer to understand how the product will look. However, building a webroom is extensive and requires retailers to hire developers and professional photographers.

    Webrooms allow retailers to share their collections, schedule virtual appointments, share 3D product images, set up virtual fitting rooms for clothing products, and accept purchase orders. For example, in 2015, Tommy Hilfiger launched its first digital showroom in Amsterdam to improve sustainability and minimize its carbon footprint. Through remote wholesale selling and digital product creation, a digital showroom helped Tommy Hilfiger transform the buying journey and retail value chain.

    b. Loyalty-rewarding sales and perks

    Loyalty Rewarding
    Customer Loyalty

    Consider building customer loyalty during your holiday promotions. First, encourage your holiday shoppers to become loyal customers by offering bonus rewards, contests, or giveaways when they sign up for your loyalty program. You can encourage them to purchase right away by providing instant discount coupons or points for a reward to redeem on their next purchase. For maximum impact, you should run this promotion throughout the holiday season. 

    Second, you should attract your current loyalty program members with discount codes. Offer free shipping or provide a one-day-only discount code to ensure your customers choose you during their last-minute purchases. With these rewards, you’ll attract customers who are window shopping and simultaneously bring your loyal customers back throughout the holiday season.

    c. Charitable Tie-Ins

    AmazonSmile
    AmazonSmile

    Research shows that customers are four times more likely to purchase from brands with a strong sense of purpose. With the festive season being the time of giving, working with a charity and giving back to your community is a great way to reach out to customers. 

    After a tough one and half years because of the pandemic, people want to give back and help those in need this holiday season. You can partner with a non-profit and run campaigns that allow customers to give back. It’s also great for sharing your brand mission with your customers. For instance, Amazon allows customers to shop from AmazonSmile, which donates 0.5% of their eligible Charity List purchases to a selected charity, at no extra cost to the customers. 

    Consider partnering with an organization within your industry. For example, you can pair up with a non-profit that collects and gives clothes to the needy if you sell clothes. You can involve customers by asking them to exchange old dresses for coupons or cash discounts. 

    d. Omni-channel customer experience

    Omni-channel marketing provides customers with a seamless, consistent, and cohesive experience over multiple marketing channels. Omnichannel marketing aims to provide a meaningful and cohesive experience that inspires your customers to make a purchase. Unlike multichannel marketing, this strategy puts the customer at the center of marketing campaigns and elevates the cross-channel customer experience. 

    Omnichannel shoppers spend 10% more money and purchase 15% more items than the original shoppers. eCommerce companies can use historical data to analyze successful channels and create a more transparent marketing strategy for the holiday season. Omnichannel analytics will provide a holistic picture of customer data that will help retailers to better meet the customer’s requirements and predict inventory. 

    e. Buy now, pay later (BNPL)

    Buy Now Pay Later
    Buy Now Pay Later (BNPL)

    Technically, buy now, pay later isn’t a promotional idea since your customers will still be paying the full price. However, BNPL allows them to delay their payments and not pay in full right away at checkout. Buy now, pay later needs to be on every eCommerce company’s holiday promotions plans. Various retailers, including Walmart, offer affordable monthly payments at the pace of 3 to 24 months with Affirm. Target also has a similar scheme with Sezzle and Affirm. Whereas Sephora and Macy’s offer 4 interest-free payments with Klarna.

    BNPL is especially popular with millennials and Gen Z shoppers and will factor into their 2021 holiday shopping plans. Research showed 62% growth in the use of buy now, pay later service in consumers aged 18 to 24. Giving customers a means to manage their budgets during holidays while still taking home their purchases will attract more customers. While the customer doesn’t pay the full price right away for their purchase, businesses still get the total worth of the item. In the eCommerce industry—nearly 50% of BNPL users say they use it while shopping online, and among them, 45% use the service frequently.

    f. Buy One, Get One

    The last promotional idea is a classic buy one, get one offer. Everyone likes a good BOGO promotional offer. In fact, 66% of shoppers from a survey preferred BOGO over other promotions. It’s a win-win promotional strategy for retailers and customers. With this offer, people shop and stock up on gifts for their friends and family, while retailers make a more significant profit than 50% off sales. People prefer to get 100% off on a product over 50% on two items. 

    BOGO sales are best to move inventory by giving shoppers a deal they can’t pass up. If you have stocked up extra items during Black Friday, you can move those last-minute gifts as end-of-year BOGO sales, making room for new merchandise in January.

    Conclusion

    In this post, you saw that there’s more to holiday marketing than a few social media posts. eCommerce companies can use these holiday promotional ideas to offer Loyalty-rewarding sales and perks, buy now pay later service, and an omnichannel customer experience. Regardless of which strategies you’re using, remember that historical data analytics and early planning will play a significant role in increasing your sales and revenue. 

    Proper planning backed by insights into key metrics will help your team develop a one-of-a-kind holiday marketing strategy to drive your holiday sales upward. From sharing gratitude to offering personalized experiences, retailers have various options for promoting business this holiday season.

    Learn how DataWeave can help make sense of your and your competitor’s pricing, promotional, and assortment data this holiday season. 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.

  • 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.