Category: Product Assortment

  • 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    Now, imagine that your pricing data is outdated and unrefined when you go to price your new sneakers.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!

  • 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!

  • 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.

  • 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!

  • Why Localized, Store-Specific Pricing and Availability Insights is Critical for Consumer Brands

    Why Localized, Store-Specific Pricing and Availability Insights is Critical for Consumer Brands

    Brands are becoming increasingly proficient in monitoring and refining their presence on online marketplaces, utilizing Digital Shelf Analytics to gather and analyze data on their online performance. These tools offer invaluable insights into enhancing visibility, adjusting pricing strategies, and improving content quality on eCommerce sites.

    Yet, as the retail landscape shifts towards a more integrated omnichannel approach, it’s crucial for brands, particularly those in CPG, to apply similar strategies to their offline channels. For brands that count physical stores among their primary sales channels, gaining localized insights is key to boosting in-store sales performance.

    Collecting shelf data from offline channels presents more challenges than online. Traditional methods, such as physical store visits, often fall short in reliability, timeliness, scale, and level of coverage.

    However, the world of eCommerce provides a solution. As part of the effort to facilitate options like buy-online-pickup-in-store (BOPIS) for shoppers, major retailers make store-specific product details available online. Consumers often go online and select their nearest store to make purchases digitally before choosing a fulfillment option like picking up at the store or direct delivery. Aggregating this store-level information offers brands critical insights into pricing and inventory across a vast network of stores, enabling them to make informed decisions that improve pricing strategies and supply chain efficiency, thus minimizing stockouts in crucial markets.

    Further, as consumers increasingly seek flexibility in how they receive their purchases—be it through in-store pickup, delivery, or shipping—brands need to adeptly monitor pricing and availability for these different fulfilment options. Such granular insight empowers brands to adapt swiftly and maintain a competitive edge in today’s dynamic retail environment.

    Why does monitoring pricing and availability data across stores matter to brands?

    • Hyperlocal Competitive Strategy: This allows brands to adjust their pricing strategies based on regional competition. By understanding the local market, brands can decide whether to position themselves as cost leaders or premium offerings. In particular, this is indispensable for Net Revenue Management (NRM) teams.
    • Targeted Marketing Initiatives: Understanding regional price and availability enables brands to customize their marketing efforts for specific markets. By aligning their strategies with local demand trends and inventory levels, brands can more effectively engage their target audiences.
    • Efficient Inventory Management: By keeping a close eye on store-level data, brands can better manage their stock, ensuring high-demand products are readily available while minimizing the risk of overstocking or running out of stock.
    • Minimum Advertised Price (MAP) Monitoring: While brands cannot directly control retail pricing, staying updated on pricing trends helps them adjust their MAP to reflect the competitive landscape, consumer expectations, cost considerations, and regional differences. A strategic approach to MAP management supports brand competitiveness and profitability in a fluctuating market.

    DataWeave’s Digital Shelf Analytics solutions equip brands with the necessary data and insights to do all of the above.

    DataWeave’s Digital Shelf Analytics is location-aware

    DataWeave’s Digital Shelf Analytics platform stands out with its sophisticated location-aware capabilities, enabling the aggregation and analysis of localized pricing, promotions, and availability data. Our platform defines locations using a range of identifiers, including latitudes and longitudes, ZIP codes, or specific stores, and can aggregate this data for particular states or regions.

    The strength of the platform lies in its robust data collection and processing framework, which operates seamlessly across thousands of stores and regions. This system is designed to operate at configurable intervals—daily, weekly, or monthly—allowing brands to keep a vigilant eye on product availability, pricing strategies, and delivery timelines based on the selected fulfillment option.

    Unlike many other providers, who may provide limited insights from a sample of stores, our solution delivers exhaustive analytics from every storefront. This comprehensive approach grants brands a strategic edge, facilitating efficient inventory tracking, precise pricing adjustments, and rapid responses to fluctuating market dynamics. It cultivates brand consistency and loyalty by enabling brands to adapt proactively to the changing landscape.

    Aggregated store-level digital shelf insights via DataWeave

    In the summarized view shown above, a brand can track how its various products are positioned across stores and retailers like Walmart, Amazon, Meijer, and others in the US.

    Using DataWeave, brands can easily see important metrics like availability levels, prices, and other metrics across these stores gaining immediate visibility without having to physically audit them. the brand can track the same metrics for products across competitor brands and inform its own pricing, stock, and assortment decisions.

    Store-level availability insights

    We provide a comprehensive view of product availability, highlighting the distribution of out-of-stock (OOS) scenarios across various retailers and pinpointing the availability status throughout a brand’s network of stores. This capability enables swift identification of widespread availability issues, offering a bird’s-eye view of where shortages are most pronounced. By simply hovering over a specific location, detailed information about stock status and pricing for individual stores becomes accessible.

    Such insights are crucial for brands to adapt their strategies, mitigate risks, and ensure they meet consumer needs despite the ever-changing retail ecosystem.

    Store-level pricing insights

    Retailers often adopt different pricing strategies to deal with margin pressure, local competition, and surplus stock. Grasping these pricing dynamics at a hyperlocal level enables brands to tailor their strategies effectively to maintain a competitive edge.

    Our platform offers an in-depth look at how prices vary among retailers, across different stores, and throughout various regions. This analysis reveals the nuanced pricing tactics employed by retailers on a regional scale.

    For example, brands might see that some retailers, like Kroger and Walmart in the chart below, maintain consistent pricing across their outlets, demonstrating a uniform pricing strategy. In contrast, others, such as Meijer and Shoprite, might adjust their prices to match local market conditions, indicating a more localized approach to pricing.

    With DataWeave, brands can dive deeper into the pricing landscape of a specific retailer, examining a price map that provides detailed information on pricing at the store level upon hovering over a given location.

    By presenting a historical analysis of average selling prices across different retailers, we equip brands with the insights needed to understand past pricing strategies and anticipate future trends, helping them to strategize more effectively in an ever-evolving market.

    Digital Shelf Analytics that work for both eCommerce and brick-and-mortar store data

    While established brands have made strides in gathering online pricing and availability data through Digital Shelf Analytics solutions, integrating comprehensive insights from both brick-and-mortar and eCommerce channels often remains a challenge.

    DataWeave stands out for its capacity to collect data across diverse digital platforms, including desktop sites, mobile sites, and mobile applications. This capability ensures that omnichannel brands can have a holistic view of their pricing, promotional, and inventory strategies across all locations and digital landscapes.

    Leveraging localized Digital Shelf Analytics to understand the intricacies of pricing and availability at the store level allows brands to fine-tune their approaches, swiftly adapt to local market shifts, and uphold a unified brand presence across the digital and offline spheres. This strategic agility places them in a favorable competitive position, enhancing customer satisfaction and trust, which are crucial for sustained success.

    Know more about DataWeave’s Digital Shelf Analytics here.

    Schedule a call with a specialist to see how it can work for your brand.

  • Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Home & Furniture

    Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Home & Furniture

    Insider Intelligence‘s forecast of a 4.5% growth in US Holiday Sales this year has been validated by the sustained robust spending observed during Black Friday and Cyber Monday. Despite multiple challenges impacting consumer spending, such as escalating prices of everyday products and elevated interest rates, shoppers continued to spend significantly, aligning with these earlier predictions.

    However, in response to these projections, retailers strategically adjusted their approach. Our analysis indicates substantial discounts prevalent in the Consumer Electronics and Home & Furniture segments during Cyber Week. Prominent retailers specializing in Home & Furniture, such as Wayfair, Overstock, and Home Depot, notably led the charge in offering attractive discounts.

    At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of home & furniture products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.

    We’ve also recently published our analysis of the Consumer Electronics and Apparel categories this Black Friday and Cyber Monday.

    Our Methodology

    For this analysis, we tracked the discounts offered by leading US retailers in the Home & Furniture category during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.

    • Sample size: 44,716 SKUs
    • Retailers tracked: Amazon, Walmart, Target, Best Buy, Overstock, Wayfair, Home Depot
    • Subcategories reported on: Dishwasher, Washer/Dryer, Mattresses, Beds, Dining Tables, Entertainment Units, Rugs, Luggage, Bookcases, Cabinets, Sofas, Coffee Tables
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Discounts Across Retailers

    Wayfair led the pack with the highest average discount of 27.5%, covering an impressive 88% of its Home & Furniture inventory. This bold strategy positions Wayfair as a go-to destination for consumers seeking substantial savings on high-quality Home & Furniture items during Black Friday and Cyber Monday.

    Home Depot offered an average discount of 17.5%, covering a substantial 69% of the products analyzed, choosing to cash in on the Cyber Week madness. Overstock followed next with an average discount of 16.6%.

    Interestingly, Home & Furniture happens to be one of the few categories in which Amazon did not offer the highest discount among the analyzed retailers, choosing a moderate average discount of 13.8%.

    Best Buy also maintained a competitive stance in the category, providing an average discount of 12.8% across 58% of their assortment. Target adopted a conservative markdown strategy, offering a relatively low average discount of 6.5%.

    In summary, the Home & Furniture category exhibited a diverse range of discounting strategies among retailers, reflecting a balance between competitiveness and profit margins. Consumers could have chosen from a spectrum of discounts based on their preferences and budget considerations during Black Friday and Cyber Monday.

    Average Discounts: Subcategories

    Among subcategories, Amazon offered a moderate 8.3% average discount on 32.9% of its products in this Dishwasher category, while Best Buy took a more aggressive stance with a 14.7% average discount covering 55.9% of its products.

    Home Depot emerged as a standout player in the Washer/Dryer category, providing a substantial 21.3% discount on 78.4% of its analyzed inventory. Best Buy closely followed with a 15.1% average discount targeting 67.6% of its products.

    Wayfair grabbed attention with a generous 36.9% average discount on Mattresses, covering almost all (99%) of its analyzed products. In addition, Wafair led the discount war in Beds, Dining Tables, Cabinets, Sofas, Coffee Tables, and Entertainment Units. Overstock took an aggressive pricing stance on Rugs, offering a substantial 52.3% average discount, covering 100% of its Rugs inventory.

    Average Discounts: Brands

    Among brands, Signature Design by Ashley maintained a consistent presence with substantial discounts on both Best Buy (25.24%) and Overstock (16.19%). This could be indicative of the brand’s commitment to appealing to a diverse customer base through varied retail channels. Costway emerges as a standout brand offering exceptionally high discounts at both Target (61.6%) and Walmart (51.7%).

    Home Decorators Collection, Home Depot’s in-house brand, offered a significant 30.9% discount at Home Depot. High-margin private label brands like these afford retailers the opportunity to offer markdowns while retaining significant margins.

    Strategic positioning on specific platforms, as seen with Alwyn Home on Wayfair and Noble House at Home Depot, suggests brands tailor their approach to the strengths and customer demographics of each retailer. The data suggests a nuanced interplay between brand positioning, discount strategies, and the perceived value offered.

    Share of Search For Home & Furniture Brands

    The Share of Search data for the Home & Furniture category unveils intriguing insights into brand visibility and performance during the Black Friday and Cyber Monday events. In this competitive landscape, where consumer decisions are influenced not only by discounts but also by brand visibility, the dynamics of Share of Search become pivotal.

    Samsung strategically increased its Share of Search during the sale, showcasing a 1.2% improvement. This suggests a deliberate effort to reinforce brand visibility and capture the attention of potential buyers actively searching for Home & Furniture products, in this case, Washer/Dryers and Dishwashers.

    Bosch too experienced a notable surge in Share of Search by 1.1%. LG, meanwhile, maintained a consistent Share of Search, with a marginal decrease of 0.1%. American Tourister experienced a modest increase in Share of Search by 0.4%.

    Like in the other categories analyzed, the dynamics of Share of Search in the Home & Furniture category reflect brand strategies aimed at not only offering discounts but also ensuring heightened visibility during the critical Black Friday and Cyber Monday shopping events. Positive shifts indicate effective marketing efforts, while stable performers demonstrate a resilient brand presence in a competitive online marketplace.


    To explore how our insights can help retailers and brands boost their pricing strategies during sale events, reach out to us today!

    For more in-depth analyses and trends across various shopping categories, stay tuned to our blog.

  • Black Friday Cyber Monday 2023 Insights: A Report on Pricing and Discounts in Apparel

    Black Friday Cyber Monday 2023 Insights: A Report on Pricing and Discounts in Apparel

    As the highly anticipated shopping season approached, industry analysts, including Deloitte, had forewarned consumer spending caution owing to persistent inflationary pressures tightening budgets. Despite these concerns, the holiday spirit was buoyed by sensational deals that delighted bargain-hunting shoppers.

    According to the National Retail Federation (NRF), over 200 million consumers participated in both in-store and online shopping activities over the Thanksgiving weekend. This marked an almost 2% uptick from the previous year, surpassing the NRF’s initial estimates of 182 million and showcasing a robust start to the holiday shopping season.

    So what was all the hype about this Black Friday and Cyber Monday? How did top retailers react to reports of possibly decreased consumer spending? At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of products across prominent retailers and categories to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.

    In this article, we focus on the pricing and discounting strategies of Amazon, Walmart, and Target in the Apparel category.

    (Read Also: Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Consumer Electronics)

    Stay tuned to our blog for insights on other shopping categories like Home & Furniture, and Health & Beauty!

    Our Methodology

    For this analysis, we tracked the average discounts of apparel products among leading US retailers during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across during the sale.

    • Sample size: 17,981 SKUs
    • Retailers tracked: Amazon, Walmart, Target
    • Subcategories reported on: Women’s Tops, Men’s Swimwear, Men’s Innerwear, Women’s Innerwear, Women’s Athleisure, Women’s Dresses, Men’s Athleisure, Men’s Shirts, Women’s Shoes, Men’s Shoes, Women’s Swimwear
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Average Discounts Across Retailers

    Amazon offered the most attractive deals, showcasing an average discount of 19.5%, applying to a substantial 61% of their apparel inventory.

    Trailing closely behind was Target, offering an average discount of 14.8% across 52% of the products analyzed. Walmart, however, took a more conservative approach, providing an average discount of 8.5%, applicable to 29% of its products.

    The contrast in discounting strategies highlights the diverse tactics employed by retailers to entice Black Friday and Cyber Monday shoppers within the Apparel category. Amazon remains the forerunner, balancing competitive discounts with a significant coverage of discounted items.

    Target follows suit with a competitive stance, while Walmart opts for a more reserved markdown approach, given that the retailer tends to carry a large number of products in the affordable price ranges.

    Average Discounts: Subcategories

    Examining the Black Friday and Cyber Monday discount landscape within the Apparel category reveals intriguing patterns among major retailers. Amazon led the charge, boasting an impressive 24.9% average discount on Women’s Tops, covering a substantial 76.5% of its products. In the same subcategory, Target competed fiercely with a 25.1% average discount, covering 87.5% of its products. Walmart, taking a measured approach, presented a 14.6% average discount across 45.1% of its Women’s Tops inventory.

    Notably, Men’s Swimwear at Target has no discounts. Meanwhile, Amazon remained aggressive across various subcategories, particularly in Women’s Shoes and Women’s Tops, aiming to capture a significant market share through both competitive pricing and a broad coverage of discounted items.

    Average Discounts: Brands

    Across brands, Tommy Hilfiger and Jockey took the lead on Amazon with an enticing average discount of 28.3% and 24.6% respectively, appealing to savvy shoppers. Calvin Klein followed closely with a 17.3% discount, offering a balance of style and affordability.

    In Walmart, Crocs stood out with a 39.9% average discount, followed by Reebok (15.7%) and Hanes (14.9%) Xhilaration, Target’s in-house brand, stole the spotlight on the retailer platform with an impressive 50% average discount. Reebok (32.3%) and Levi’s (22.9%) maintained competitive discounts, appealing to diverse tastes.

    Our analysis sheds light on the dynamic landscape of apparel discounts, showcasing how brands adopt varying pricing strategies to position themselves competitively for Black Friday and Cyber Monday shoppers.

    Share of Search For Apparel Brands Across Subcategories

    The dynamics of Black Friday and Cyber Monday extend beyond price reductions, with brands strategically vying for increased visibility through Share of Search metrics. This metric signifies a brand’s prominence among the top 20 ranked products in a given subcategory, offering valuable insights into their online marketplace visibility.

    Among the standout performers in the Apparel category, Jockey experienced a significant surge in Share of Search, leaping from 1.70% before the event to an impressive 13.30% during the Black Friday and Cyber Monday sales. Speedo, in the Women’s Swimwear subcategory, demonstrated a substantial increase from 4.40% to 13.30%, solidifying its presence and gaining an 8.90% boost in Share of Search.

    Tommy Hilfiger and Adidas also exhibited notable gains in Share of Search, increasing by 5.30% and 5.60%, respectively. However, some brands experienced a slight dip, with Speedo in the Men’s Swimwear subcategory seeing a 2.50% dip in their search visibility, and Reebok in Men’s Shoes witnessing a 3.3% decrease.

    These fluctuations highlight the dynamic nature of brand strategies during Black Friday and Cyber Monday in the Apparel category, where gaining visibility also proves to be crucial alongside offering competitive discounts.

    For a deeper dive into the world of competitive pricing intelligence and to explore how our solutions can benefit apparel retailers and brands, reach out to us today!

    Stay tuned to our blog for forthcoming analyses on pricing and discounting trends across a spectrum of shopping categories, as we continue to unravel the intricacies of consumer behavior and market dynamics.

  • Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Consumer Electronics

    Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Consumer Electronics

    As Black Friday and Cyber Monday unfolded across the globe, there was a noticeable subdued atmosphere compared to previous years. TD Cowen brokerage adjusted its forecast for US holiday spending, revising it down from an initial 4-5% growth to a more conservative estimate of 2-3%.

    Compounded by persistent inflation and elevated interest rates, many consumers find themselves financially strained, leading to the projection of the slowest growth in US holiday spending in five years.

    In this context, it would be relevant to investigate whether this restrained reaction from consumers had an influence on the extent of attractive deals and discounts provided by top retailers and brands during the sale event.

    At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of consumer electronics products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.

    Keep an eye on our blog for insights on other shopping categories like Apparel, Home & Furniture, and Health & Beauty!

    Our Methodology

    For this analysis, we tracked the average discounts among leading US electronics retailers during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.

    • Sample size: 23,505 SKUs
    • Retailers tracked: Amazon, Walmart, Target, Best Buy
    • Subcategories reported on: Headphones, Laptops, Smartphones, Tablets, Speakers, TVs, Earbuds, Wireless Headphones, Drones, Smartwatches
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Average Discounts Across Retailers

    The observed Black Friday and Cyber Monday discount strategies reveal a distinct competitive landscape among major retailers. Amazon emerged as the frontrunner, offering the highest average discounts at 23.30%, spanning a significant 74% of their consumer electronics inventory. Best Buy closely followed, with an average discount of 19.40% across 76% of their products.

    On the other hand, Target and Walmart adopted a more conservative stance, providing lower average discounts at 14.8% and 12%, respectively, with Target discounting 51% of its products and Walmart discounting 41%. This variation in discounting strategies highlights the diverse approaches retailers take to attract and retain Black Friday and Cyber Monday shoppers, balancing competitiveness with profit margins.

    Average Discounts: Subcategories

    In the Headphones subcategory, Amazon stands out with a substantial 31.40% average discount, targeting 84.69% of SKUs, showcasing an aggressive discounting strategy. Best Buy follows closely, demonstrating competitive pricing with a 21.80% average discount on 67.03% of products.

    Meanwhile, in TVs, Best Buy offered a significant 17.9% average discount across 89% of its products, signaling a targeted effort to capture a broad market share in this subcategory.

    In the Laptop subcategory, Target was highly conservative, with only a 4.1% average discount covering 14.3% of its products, while Walmart positioned itself with a moderate 9.5% average discount, targeting 39.8% of its inventory.

    Among Smartphones, Amazon (14.7%) was third to Best Buy and Target, which offered average discounts of 20.5% and 18.1%, respectively. Walmart, with an average discount of only 9.9% in the subcategory opted for a relatively muted approach.

    Average Discounts: Brands

    The discount strategies across top electronics brands during Black Friday unveil distinct approaches. Samsung emerges as a focal point across Amazon, Best Buy, Walmart, and Target. The brand was most attractively priced on Best Buy, with an average discount of 25.3%, followed by Target (18.3%) and Amazon (17.9%).

    Apple’s discounts were quite consistent across Amazon (17.6%), Best Buy (16.1%), and Target (17.8%), with the exception of Walmart (8.1%). JBL, interestingly, opted to discount very heavily on Best Buy, at an average of 38.8%, resulting in several attractive deals for shoppers on the website. Sony, too, offered impressive discounts at over 23% on Amazon and Best Buy, followed by 16% on Walmart. On Amazon, Amazon Renewed (13.9%) was among the most aggressively discounted products, highlighting an effort to further appeal to cost-conscious consumers.

    Overall, our analysis throws light on the nuanced strategies employed by leading brands on Amazon, Best Buy, Walmart, and Target, reflecting a delicate interplay between brand positioning, pricing competitiveness, and customer appeal.

    Share of Search For Consumer Electronics Brands Across Subcategories

    The Share of Search data reflects intriguing shifts in brand strategies during the Black Friday and Cyber Monday events. During sale events, brands looking to entice shoppers don’t rely only on price but also on search visibility to help drive awareness and conversion. Share of Search is defined as the share of a brand’s products among the top 20 ranked products in a subcategory, thereby providing insight into a brand’s visibility on online marketplaces.

    Some of the brands that improved their Share of Search the most include LG, Skullcandy, Asus, JBL, and Samsung. On the other hand, prominent brands like Sony and Apple actually lost ground on this metric by 0.4% and 2% respectively.

    At DataWeave, our commitment to empowering retailers and brands with actionable competitive and digital shelf insights remains unwavering. Our AI-powered platform provides a comprehensive view of market dynamics for our customers, enabling informed decision-making. As a partner in your journey, we offer tailored solutions to enhance your competitive edge, drive sales, and elevate your brand presence. To find out more about our solution, reach out to us today!

    To learn more about pricing and discounting trends during Black Friday and Cyber Monday across various other shopping categories, stay tuned to our blog!

  • 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.

  • 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! 

  • Prime Day Germany 2022 – highlights from the 2 day annual shopping festival!

    Prime Day Germany 2022 – highlights from the 2 day annual shopping festival!

    In 2022, Amazon sold 300 million products during Prime Day – selling roughly 100,000 items per minute. Since Amazon started Prime Day in 2015 to celebrate its 20th birthday, the shopping festival has grown into a holiday and rivals Black Friday and Cyber Monday in the U.S. and Singles’ Day in China. 

    According to RetailDetail, the leading B2B retail community in Benelux, Amazon is planning a 2nd Prime Day shopping festival in the autumn, just a few months after its annual Prime Day event. The retailer has asked its sales partners to prepare for a promotional event in the autumn where they have until the beginning of September to propose attractive discounts, with at least 20% discounts. This year’s second Prime Day may occur in October, with or without the same name. 

    But before that, let’s examine what happened in Germany this year on Prime Day 2022.

    Methodology

    • We tracked Amazon.de both before & on 12 & 13th July 2022, on Prime Day.
    • Categories Tracked – Electronics, Wine & Spirits, Grocery, Furniture, Fashion, and 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.

    What kind of Discounts did Amazon.de offer?

    Amazon Prime Day will be significant, especially for customers hoping to get discounts amid soaring inflation. Both Amazon as well as other sources reported that electrical and electronic items were the most popular purchases, followed by general retail products. Electrical and electronics saw the value of transactions soar 90% on the first day. Mobile phones and accessories were the most popular, with transaction values almost doubling to 96% on day one.

    Discounts across Categories on Amazon.de
    Discounts across Categories on Amazon.de
    • Based on trends from past events, Amazon likely knew electronic items were going to be best sellers. Keeping this in mind, they made sure to offer high discounts in the electronics category. They offered a 6.5% additional discount on electronics on Prime Day. And once the sale ended, they continued to discount electronics by 1.3%.
    • The Fashion category also had a fair bit of discounts and came in at a close second at 5.9%
    • Looks like Amazon discounted everyday use items minimally. Groceries had an additional discount of just 1.8% on Prime Day, and wine and spirits had 2% extra discount.  
    Discounts on Electronics Category on Amazon.de
    Discounts on Electronics Category on Amazon.de
    • Within Electronics, in the four categories we tracked, we saw the highest additional discounts were offered on Bluetooth earphones (10.6%) and Smartwatches (9%)
    Discounts on Fashion Category on Amazon.de
    Discounts on Fashion Category on Amazon.de
    • Jeans and Sunglasses had the highest discounts at 8.6% & 7.6% respectively.
    • Sneakers & Watches too had additional discounts of 6.6% on Prime Day.
    • Post the Prime Day event, Amazon retained an average of 1.5% discount across all products in the fashion category instead of pricing them at the original price. 
    • However, in the case of women’s T-Shirts, they increased the price by 1.7% from the pre-event price.

    Discounts across Price Tiers

    Retailers must consider several factors when making strategic discounting decisions, including customer buying behavior, the type of discount offered & the volume of discount offered. The best discounting approach will vary depending on the product and other factors like the original selling price of the product.

    Now let’s compare the discounting strategy Amazon used in the Electronics v/s Fashion category on Prime Day.

    Discounts across Price Ranges
    Discounts across Price Ranges
    • Interestingly, in both the Electronics and Fashion categories, Amazon increased prices for the lowest-end products between the €0-10 range by 3.6% and 13.2%, respectively, during the sale instead of discounting them! Maybe this was a strategy to drive consumers to higher-value products with greater discounts? 
    • Another similarity in strategy was that most of the mid-priced items had maximum discounts. In electronics & fashion both, the maximum discounts were given to products between the € 30-100 range. 
    • Here’s a difference that stood out – for Electronics in the higher price range between €100 – 500, the volume of discounts dropped a bit which meant Amazon gave moderate discounts on high-end electronics. But the trend flipped for Fashion as luxury fashion items were made to look more attractive with higher discounts.

    Monitoring stock availability during key sales days is critical

    Brands need to have the right stock availability, especially during sale events, because more customers shop online during sales. What’s worse, non-availability of products may drive customers to competitors that are stocking the same product.  Out-of-stock situations lead to missed opportunities & lost sales! Let’s take a look at our data and see how Amazon planned product availability across categories on Prime Day. 

    Availability Analysis across Categories on Prime Day
    Availability Analysis across Categories on Prime Day
    • Amazon was betting big on 2 categories – Electronics & Home. This meant they needed to keep a keen eye on availability in these categories, especially since they forecasted the highest sales to be generated here.
      … it was no surprise that the Furniture category had almost 100% availability during Prime Day! Electronics too had a high availability at 94% during the event.
    • Generally, our data showed that availability across multiple categories we tracked seemed robust and above 80% in more cases. Only Beauty & Grocery had 79% availability.

    Conclusion

    Prime Day sales reached an estimated 12 billion U.S. dollars worldwide, 9.8% higher than last year, making it the most successful shopping event in Amazon’s history. If you’re a brand selling on Amazon or a retailer trying to compete with Amazon, reach out to us at DataWeave to know how we can help!

  • Prime Day UK 2022 – highlights from the 2 day annual shopping festival!

    Prime Day UK 2022 – highlights from the 2 day annual shopping festival!

    Prime Day launched in 2015 as a celebration of the 20th anniversary of Amazon’s founding & has quickly become the biggest shopping event of the year for Amazon. Prime Day is a great opportunity for customers to snag fantastic deals on products they might not otherwise consider buying. Last year, Amazon Prime Day was a tremendous success, with Prime members spending billions of dollars on discounted items. In 2022 alone, global sales during the event reached a new record high of $12 Bn.

    18 countries participated in Prime Day this year, including the US. We did a deep dive into what happened in the UK – the discounts Amazon offered and categories with the highest discounts as well as checked to see if other retailers tweaked their pricing strategy to compete with Amazon on Prime Day.

    Methodology

    • In addition to Amazon UK, we tracked some key retailers on 12 & 13th July 2022, on Prime Day.
      Retailers tracked – eBay UK, OnBuy, Selfridges, ASOS.com, Net-A-Porter 
    • Categories tracked – Electronics, Wine & Spirits, Grocery, Furniture, 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.

    Did other retailers compete with Amazon on Prime Day?

    Traditionally, as Amazon’s Prime Day sale approaches, other retailers adjust their prices by offering summer deals or getting creative with offers. However, we did not see aggressive strategies from other retailers this year. In the US, Walmart always has a sale during Amazon’s Prime Day. The Wall Street Journal reported that Walmart announced there wouldn’t be an annual promotional event on Prime Day 2022 this year.

    Another report published by Forrester stated that major retailers scaled back their promotions, and overall offers from other retailers were less than impressive. We took a look at the data we gathered in the UK to see if this trend aligned. 

    Discounts offered on Prime Day on Amazon v/s other retailers
    Discounts offered on Prime Day on Amazon v/s other retailers
    • Our data showed that most retailers we tracked offered negligible discounts (in the range of 0.1 – 1.5%) and did not really try and compete or match the discounts Amazon was offering. 
    • However, ASOS was the one retailer that competed heavily with Amazon in the Fashion & Beauty category. While Amazon offered an average additional discount of 7.7% in the Fashion category, ASOS offered 13.2%. And in the beauty category, Amazon offered 6.7%, while ASOS offered 15.2%.
    • When we looked at post-prime day discounts, we saw that as soon as Prime Day ended, ASOS went back to the original price and stopped offering a discount which clearly shows they were keeping an active eye on out their competitors pricing. In fact, ASOS was offering up to 80% off almost everything on the site until Prime Day.

    Which were the popular categories that offered the most discounts?

    During Prime Day, shoppers saw tons of deals on essential gadgets. Tech deals were a massive hit and saw big discounts on everything from TVs, laptops, smartwatches, phones, and tablets. We look at the data we collected to see if we saw a similar trend. 

    Discounts on Amazon UK across categories
    Discounts on Amazon UK across categories
    • Amazon offered discounts across categories and reported that some of the best-selling categories were Consumer Electronics & Home. 
    • Our data too showed that the highest additional discounts were offered in electronics – Bluetooth Earphones at 18.4%, followed by Smartwatches at 14.9% and Laptops as well as Cameras, both at 12%.
    • Low discounts were offered on Alcohol, with Beer at 0.9% and Wine at 1.3%, respectively.
    • Relatively attractive discounts were seen in the Fashion & Beauty category – Sunglasses (9.1%), Shampoo (9.7%), & Watches (9.4%)
    Discounts on Amazon UK in the Electronics category
    Discounts on Amazon UK in the Electronics category

    Electronics being the hot favorite – we wanted to deep dive into the data and get more insights on Amazon’s pricing & discounting strategy here. Discounts can entice customers to buy more, encourage customer loyalty, or clear out old inventory. However, businesses must be careful since too much discounting can eat into profits. They also have to be mindful of which products should be discounted and by how much. 

    • Our data showed that the highest discounts (between 13 – 18%) were given on electronics priced between the £ 20-100 price range.
    • Electronics priced higher, between the £ 100 – 500 pound price range, were discounted less than 10%
    • However, high-value premium electronics over £ 500 were discounted slightly above 10%

    How did Amazon manage stock availability during Prime Day?

    Keeping track of inventory is especially important during big sales like Prime Day when thousands of customers are actively looking for deals.  There’s nothing worse than them finding the item they wanted is out of stock (OOS). OOS leads to lost sales, a situation that must be avoided at all costs. Read about how a small short term stock out on Amazon led to long term negative impacts for one of our customers. And let’s also look at the data and see what product availability looked like on Prime Day.

    • Overall, Amazon maintained robust availability across categories, and re-stocking was constant both before, during & after the event. 
    • Furniture, Fashion & Electronics had the highest availability. No surprise there since Amazon estimated that Home/ Furniture would be one of the best-selling categories.
    • Grocery saw average availability – perhaps cause some of these products are perishables, so it’s best to be mindful about overstocking.

    Which Brands Won on Prime Day?

    If there is one thing to remember about improving your product visibility on Amazon, it’s that it all boils down to the usage of the right keywords. Using relevant keywords makes your product appear higher up in search when customers are running searches on Amazon for those products. And the higher up a product appears in search, the higher the chances of a sale! 

    Let’s take a look at some popular categories and which brands had the highest Share of Search (SoS) during Prime Day.

    • Corona, San Miguel, and Becks were the top 3 brands optimized for the keyword Beer. However, what’s really important to note is both Corona & Becks had 20% SoS that was completely organic. San Miguel had a 20% SoS too, but it was sponsored ads that gave them this artificial boost. 
    • While a whole bunch of other brands had a 10% SoS most of them achieved this via Sponsored Ads. Youngever was the only brand that achieved this completely organically. They must have made sure they optimized key KPIs like content, ratings & reviews & product availability to achieve this result.
    • There were deep discounts on a wide range of Lenovo laptops. For example, the Lenovo IdeaPad duet Chromebook and Lenovo IdeaPad Flex 3 Chromebook were available at £100 off. Our data, too saw Lenovo & Asus fight for the top spot.
    • Asus sponsored 28% of products before Prime Day, hoping to capitalize on the pre-sale frenzy. During the event, they sponsored only 13% of products, bringing down their total SoS from 31% before the event to 13% during the event. 
    • Lenovo followed the opposite strategy; they sponsored just 6% of products before the event and during the event sponsored a whopping 25% which made them “almost” dominate the Laptop category during Prime Day.
    • Then there was Microsoft, with the highest SoS at 38%, of which all of it was organic!
    • The Smartphone SoS battle was clearly between Samsung & Xiaomi. Samsung was a consistent #1 at all 3 time periods (Before, During & After Prime Day) with the highest total SoS. Xiaomi came in at a close second. 
    • Samsung had an exciting strategy – they went heavy on sponsorships before and after the event. Their sponsored SoS was 31% & 39% respectively. And SoS of 13% during the event. 
    • Xiaomi’s strategy was just the opposite. Their sponsored SoS was 16% before the event. And 17% after the event, which was moderate compared to their Sponsored SoS during the event at 25%, which was much higher than Samsung’s 13%
    • Critical to note, Xiaomi’s organic search visibility before, during, and after the event was 0%. It definitely should be a concern area for any brand.
    Share of search
    Share of search
    • Both before & after the event, Cadbury had the highest visibility for the keyword Chocolate. During the event, they were not in the top 5 brands.
    • During Prime Day, Nestle won the top spot and had a 29% SoS. However, before the event, they were at #3 and after at #2. Artificially boosting visibility might’ve had something to do with this.

    Conclusion

    Prime Day sales reached an estimated 12 billion U.S. dollars worldwide, 9.8% higher than last year, making it the most successful shopping event in Amazon’s history. If you’re a brand selling on Amazon or a retailer trying to compete with Amazon, reach out to us at DataWeave to know how we can help!

  • 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.

  • 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

  • Baby Formula Shortage Continues Alongside National Price Increases – June 2022

    Baby Formula Shortage Continues Alongside National Price Increases – June 2022

    As the baby formula shortage continues, retailers and brands are working quickly to meet evolving consumer demand, considering supply chain driven headwinds, a baby formula recall, and inflationary-driven impacts. The DataWeave analytics team has actively tracked marketplace changes, alongside reports from the FDA, for the baby formula category at a state-level, and has shared the latest snapshot of product availability through June 7th, 2022, below.

    Average Baby Formula Product Availability by State - June 2022
    Average Baby Formula Product Availability by State – June 2022

    While the U.S. has reached an average of 84% baby formula availability the first week of June 2022, given recent news headlines related to the baby formula shortage, and tracking out of stock encounters by state, we see a continued decline in availability throughout the Midwest versus product availability levels seen in May 2022.

    Wisconsin, Michigan, Illinois, Indiana, Ohio, and Kentucky all show average availability for baby formula to be less than 50%, with Wisconsin being impacted the most at less than 18% average availability. While Texas shows an average availability improvement of 3.5% from the first two weeks of May 2022 to the first week of June 2022 as noted in the below chart, availability is also very low overall at less than 60%.

    Average Change in Baby Formula Product Availability by State: May-June 2022
    Average Change in Baby Formula Product Availability by State – May 2022 to June 2022

    Outside of the Midwest and Texas, the other states for consumers to be cautious in are California, Virginia, and South Carolina as their month-over-month average change in availability also declined 4%, 12.6% and 8.2% respectively. Below is a snapshot of where the baby formula availability average started as of May 1st through the 15th, 2022.

    Average Baby Formula Product Availability by State - May 2022
    Average Baby Formula Product Availability by State – May 2022

    Baby Formula Product Availability Changes – March 2021 through May 2022

    At an aggregated level overall, the availability for baby formula was relatively stable across all retailers considered within our analysis from March 2021 through September 2021, but has been on a steady decline ever since, starting at 81.7% availability in September and ending at 53.8% availability in May 2022 as noted in the below chart.

    Monthly Average Availability for Baby Formula Across Major Retailer Websites
    Monthly Average Availability for Baby Formula Across Major Retailer Websites

    Looking at baby formula availability at a retail level, we saw yet again not all availability challenges were alike, by month or retailer. Costco.com lead the other retailers within our analysis for greatest average availability from March 2021 through May 2022, but had one of the lowest availability percentages at 62.7% in May 2021, and dropped to the lowest availability of the group in May 2022 at 37.5%.

    Average Availability for Baby Formula Across Major Retailer Websites
    Average Availability for Baby Formula Across Major Retailer Websites

    Baby Formula Prices Increase as Availability Changes

    While unnecessary price gouging is prohibited, price increases are still happening at a slow and steady rate across all the accounts included within our Pricing Intelligence analysis given external market factors outside of baby formula recall related stockout scenarios.

    Kroger.com experienced the greatest average price increases overall, with the peak being in May 2022 at a 19% increase, 8% higher than other retailers on average, versus prices seen in March 2021 for the same baby formula products. The most significant price hike occurred on Kroger.com from December 2021 to January 2022. Other retailers like H-E-B, Target and Wegman’s have had minimal price changes from March 2021 through May 2022. 

    Average Price Inflation for Baby Formula, Indexed to March 2021
    Average Price Inflation for Baby Formula, Indexed to March 2021

    Address the Baby Formula Shortage With eCommerce Intelligence

    As the market continues to evolve and baby formula supply works its way to catching back up to demand, our team will continue providing critical pricing, merchandising, and competitive insights at scale, to enable retailers and brands to develop data-driven growth strategies that directly influence their eCommerce performance, accelerate revenue growth and drive profitability.

    Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis, or for more information on our Commerce Intelligence and Digital Shelf solutions, and let us know what other category insights you’d be interested in seeing this year.

  • 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!  

  • Share of Keyword Search Cinco de Mayo 2022

    Share of Keyword Search Cinco de Mayo 2022

    As inflation continues to hike costs for consumers and supply chains challenge them to maintain loyalty, there is still an active audience willing to pay the ultimate price for the convenience of food and alcohol delivery. That being said, we analyzed 8 popular Retail and Delivery Intermediary websites and 11 popular ‘Cinco de Mayo’ keywords to see which Brands are predicted to win the battle of Digital Shelf Share of Search this holiday.

    2022 Cinco de Mayo Share of Search Insights - Top Brands for 'Cinco de Mayo'
    2022 Cinco de Mayo Share of Search Insights – Top Brands for ‘Cinco de Mayo’

    Opportunities for Food & Bev on Cinco de Mayo

    While most of our analysis focused on popular Cinco de Mayo food and beverage products, none of these brands populated on either Target (pictured on left below) or Walmart (pictured on right below) page 1 search results for the term ‘Cinco de Mayo’. Keyword search results for this term are dominated primarily by décor brands as indicated below.

    Brands Achieving Top Share of Search for Food and Beverage Categories on Cinco de Mayo 2022
    Brands Achieving Top Share of Search for Food and Beverage Categories on Cinco de Mayo 2022

    Share of Keyword Search Results – Alcohol Category

    Three of the most popular alcohol types sought out during Cinco de Mayo are ‘Mexican Beer’, ‘Mezcal’, and ‘Tequila’. Below are the brands dominating Share of Keyword Search results on each of the major retail websites we researched.

    AmazonFresh, Meijer, Kroger, and Sam's Club Share of Search - Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022
    AmazonFresh, Meijer, Kroger, and Sam’s Club Share of Search – Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022

    We also reviewed the same keyword performance across popular delivery intermediaries to see how Share of Keyword Search altered for ‘Mexican Beer’, ‘Mezcal’, and ‘Tequila’. The results are below for TotalWine, Instacart, Drizly and GoPuff:

    TotalWine, Instacart, Drizly, and GoPuff of Search - Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022
    TotalWine, Instacart, Drizly, and GoPuff of Search – Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022

    The keyword ‘Agave’ is also a popular search term within the alcohol category during the time leading up to Cinco de Mayo. We reviewed keyword search performance at various zip codes to see how price points that populated on page 1 search results varied given the change in median income. Below are the results:

    Share of Search for Alcohol by Price Point and Zip Code on AmazonFresh
    Share of Search for Alcohol by Price Point and Zip Code on AmazonFresh

    Share of Keyword Search Results – Grocery Categories

    We also reviewed some of the most popular grocery items purchased during Cinco de Mayo by Keyword Share of Search results to see which brands are primed to win the Digital Shelf this year. Below are the results for Target.com and Walmart.com.

    Walmart and Target Share of Search - Food and Beverage Keywords on Cinco de Mayo 2022
    Walmart and Target Share of Search – Food and Beverage Keywords on Cinco de Mayo 2022

    Below are the results for the same popular grocery items and alcohol keywords related to Cinco de Mayo and the page 1 results seen for Brand Share of Search on Safeway.com.

    Safeway Share of Search - Food and Beverage Keywords on Cinco de Mayo 2022
    Safeway Share of Search – Food and Beverage Keywords on Cinco de Mayo 2022

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions and help drive profitable growth in an intensifying competitive environment. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis, and let us know what other holiday insights you’d be interested in seeing this year. Happy Cinco de Mayo!

  • 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!  

  • eCommerce Performance Analytics for CPG Private Label

    eCommerce Performance Analytics for CPG Private Label

    The combination of economic uncertainty, inflation, and perceived affordability has increased consumer’s willingness to buy and try more private label products, challenging National brands to differentiate their eCommerce strategies, especially those related to price positioning, in other ways.

    Our previously released report, Inflation Accelerates Private Label Share and Penetration, confirmed 8 out of 10 brands with the highest SKU count carried across all grocery retailer websites to be private label, signaling the strength of their digital Share of Voice. Given the growing shift in consumer preference toward private label brands, we are providing access to the latest trends seen from September 2021 through March 2022. Below you will find a summary of what the data revealed about the growing presence of private label brands on the Digital Shelf.

    Private Label Account and Category Penetration

    We analyzed private label penetration at an account level to understand which private label brands have the greatest presence on retailer digital shelves, and to see which retailers may be leaving product assortment opportunities on the table.

    Private Label Penetration Across Retail Grocer Websites

    As a retailer, it is important to understand how your private label penetration stacks up against the industry average at a category level, especially given the performance tracked for retailers included within our analysis and the vast number of SKUs they offer online (over 20,000).

    Private Label Penetration by Category Across Retail Grocer Websites

    The Private Label and National Brand Price Gap Widens

    Private label brands tried out of necessity mid-pandemic increased in popularity as grocery prices continued to rise, providing an opportunity for retailers to increase brand affinity and loyalty for their online shoppers. Retailers alike were able to keep affordability at the forefront of their strategies and maintain a price gap of 23% or more, despite inflationary pressures to increase prices.

    Private Label / National Brand Price Gap by Retailer

    Looking at the results at a category level, we can see that Meat is the only category found within our analysis where private label brands are priced higher than National brands at an average of 8% greater. The Alcohol & Beverages category tends to always see the greatest price gap between private label and National brands given the price variances by unit (ranging from under $10 to over $100), in this case averaging a 148% price gap.

    Private Label & National Brand Price Gap by Category

    Private Label Total Basket Value Comparison Across Retailers

    While SKU-level pricing is extremely important to product strategy, for a retailer, it is equally as important to be as mindful of the total basket value even more so now as consumers further their private label loyalty across various categories. A few SKU-level missteps in pricing decisions can exacerbate cart abandonment and negatively impact shopper loyalty in a world where prices can be compared instantly in the palm of your hand.

    Based on our analysis, Walmart and H-E-B private label products offered the lowest priced total basket of goods at $42.90 and $45.06 respectively, whereas AmazonFresh and Safeway offered the highest total at $73.19 and $69.52 respectively.

    Private Label Item Level Price Comparison by Retailer

    Inflation-driven Price Changes are on the Rise with Room to Grow

    Based on the 20,000+ SKUs analyzed, we saw a continual price increase every month since September 2021 when comparing future monthly prices to those we tracked in September. The greatest price increase happened in March 2022 at 12.5% on average, however, there are still 48% of SKUs that have yet to see a price increase even as inflationary pressures rise.

    When viewing the split between National and private label brand price increases in March 2022 versus September 2021, we saw National brands increased prices on average by 13% where private label brand prices only increased an average of 7%.

    Private Label & National Brand Price Change
    Private Label & National Brand Price Change (%)

    Price decreases are still occurring across all categories, despite inflation, but to varying degrees ranging from 5% for Deli items to 17% for Dairy & Eggs. Within the Dairy & Eggs and Pantry categories, private label brands reduced prices for an additional 10% of total SKUs compared to National brands.

    The greatest category of opportunity for price increases within private label were found within Beauty & Personal Care with 67% of private label products yet to see a price change since September 2021.

    Price Change (%) by Category and Brand Type

    Private Label Price Change Correlation to Product Availability

    The category with the greatest magnitude of price increase seen within private label brands occurred within Baby at 16.3% followed by Home at 14.3% on average. Private label products within Home and Baby categories were also showing the lowest availability rates, 75.9% and 79.5% respectively, indicating a high demand for these items even as prices increased.

    The private label categories with the smallest price increase on average were Dairy & Eggs at 2.4% and Other Foods and Pantry at 3.4% and 3.6%, respectively.

    Private Label Price Change Magnitude & Availability
    Private Label Price Change Magnitude & Availability

    While in many accounts both private label and National brands struggled with stock availability in March 2022, National brand availability is much lower (around 10% on average) than private label availability.

    H-E-B had the lowest overall product availability at 76% across both private label and National brands on average. Only Walmart had lower availability for Private Label at 75% compared to 93% for National brands, but they also had the greatest price gap between private label and National brands.

    Private Label & National Brand Product Stock Availability

    The Future of eCommerce Growth for Private Label

    Our greatest learning from this analysis is that it’s time for retailers to start thinking and planning more like the National brands they carry when it comes to positioning their private label brands for success. Successful retailers are taking this time to reset their private-label strategies and transfer short-term switching behavior into long-term customer loyalty.

    Retailers playing catch up have the opportunity to address some of the gaps highlighted throughout this analysis, for example, relative to pricing and assortment changes. Below are some of the highlighted opportunities:

    • Though inflation is driving price hikes, more than 50% of products analyzed have yet to see a price increase indicating an opportunity to protect margin
    • Narrowing the price gap between a store’s brand and National brands should not be the only focus as competitive private label brands are becoming a greater threat at a category and basket level
    • Modifying and expanding assortments as demand increases for private label can improve customer retention and loyalty, especially for cross-shopping consumers

    According to The Food Industry Association (FMI), only 20% of food retailers currently promote private brands on their homepages, and only 48% include detailed product descriptions indicating even more opportunities left on the table for retailers to optimize private label digital performance.

    Many leading retailers are leveraging real-time digital marketplace insights and eCommerce analytics solutions like ours to further their online brand presence and optimize sales performance. This report highlights only a small sample of the types of near real-time insights we provide our clients to effectively build competing strategies, make smarter pricing and merchandising decisions, and accomplish eCommerce growth goals. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

    For access to a previously recorded webinar presented in partnership with the Private Label Manufacturers Association and conducted by DataWeave’s President and COO, Krish Thyagarajan, click here.

  • 9 Things to Build a Thriving Fashion eCommerce Brand

    9 Things to Build a Thriving Fashion eCommerce Brand

    According to the Statista Fashion eCommerce report 2021, the compound annual growth rate (CAGR) for online fashion is predicted to be 10.3% between 2018-2023. The widespread need for trendy fashion presents a challenge for fashion brands to succeed in a highly crowded and competitive space. With eCommerce shopping becoming more prevalent, fashion brands aren’t just competing for brick-and-mortar sales. Instead, they’re also competing for those late-night or impulse purchases from online customers.

    Looking to 2022 and beyond, this blog will highlight 9 things to build a thriving fashion eCommerce brand:

    1. Allow shopping on multiple channels

    Breakdown of Shopping journeys in Apparel
    Breakdown of Shopping journeys in Apparel

    Typically buyers from diverse age groups prefer different sales channels. Some prefer large retailers, and some choose web stores. If you know where your customers like to purchase your products, you can leverage the power of search engines and marketplaces to improve your sales. Multi-channel retailing helps fashion eCommerce brands to sell and promote products on a platform and device of the audience’s choice. 

    A brand should offer support and access to its products across all platforms, channels, and devices. It helps fashion brands to reach customers where they prefer to shop. If your customers prefer to shop on a computer or an app, your brand can offer a seamless customer experience. 

    2. Don’t sell on the Homepage

    Your online fashion store homepage is more about increasing credibility and trust among potential buyers. Your ideal home page shouldn’t display products or their prices. Instead, it would be best to integrate promotional and marketing strategies on the landing page to encourage visitors to explore your product categories and the rest of the website. You should have an intuitive interface that makes navigating the pages easier. You can also use the homepage to promote seasonal offers and new launches. Fashion brands can also display customer reviews, awards, brand achievements, and web security trust seals to increase the conversion rate.

    Don't sell on homepage
    Don’t sell on the homepage

    3. Product Descriptions with Unique Stories

    Product descriptions often get overlooked or underutilized even though they are important for eCommerce businesses. Your products won’t sell with spammy and same product descriptions. The modern product description is all about communicating a product’s worth and value with a story that captivates your buyer’s attention. Identify areas where your content & images don’t align with your product or represent it in the best light. Make sure to deliver an enhanced consistent brand experience across all online channels to improve your conversions.

    4. Focus on Review and Ratings

    Rating & Review of a fashion brand
    Rating & Review of a fashion brand

    Customer reviews have a huge influence on a buyer’s purchase decision, especially in the fashion industry. Encourage your consumers to leave reviews on your brand website. Reviews help fashion brands to build trust for their products and convert customers. Legitimate customer reviews help your shoppers to get crucial insights into what previous buyers liked or disliked about a particular product. 

    However, you should stay away from paid-for or false reviews usually encouraged by unscrupulous sellers as they are easy to spot and hurt your rankings. You must remember that receiving reviews also includes dealing with negative comments. They should be used to improve your upcoming product offerings. 

    5. Sell Looks

    Product can be combined with in the detail page
    The product can be combined with in the detail page

    Successful fashion brands don’t simply sell individual products. Instead, they sell complete looks that inspire shoppers to purchase the entire stylish look. As an online fashion brand, you’re not selling clothes; you’re selling an elegant collection of wearable art. When visitors reach your online store, you should appeal to their fantasies and sentiments through aesthetic look books that are both pleasing and congruent with your brand. Most successful online fashion shops are inspirational and visual. Look books help brands pair their previous season items or dead stock with new stock and increase sales. Brands can also share these look books on social media or in their monthly newsletters to increase reach. 

    6. Provide Promotions and Offers

    Fashion brands can take advantage of plenty of sales throughout the year, from New Year celebrations to Black Friday, Cyber Monday, and Christmas. Brands can leverage these high sales periods to sell looks and gift items to boost sales. Just make sure you’re measuring the effectiveness of your online promotions. Holiday and festive sales also offer an excellent opportunity to plan strategic discounts to get rid of old stock. Since trends in the fashion industry have been changing rapidly, you can use discounts to get rid of dead-stock or out-of-trend items each season. 

    7. Be active on social media

    Social media is a way to promote your brand, increase trust among your audience, and entertain your audience with exciting content. You can also engage the audience by providing gift coupons or giveaways. Brands can promote products while keeping their audience engaged with engaging content and promotional offers. 

    Social media is a great way to get influencer support, either organically or through a paid partnership. Brands have to focus on every element of social media marketing strategy, right from choosing a platform, creating Instagram/Facebook shops, jumping on trends/events, and tracking customer sentiment

    8. High-quality product photography

    Capture every detail of your product
    Capture every detail of your product

    Nothing is worse than ordering a piece of clothing online and not getting what you saw on the website. Not being able to accurately convey fashion products will hurt your bottom line. Fashion brands must use top-notch product photography that includes high-quality visuals, such as multiple angle views, 360-degree images of each product, accurate depictions of all color options, and the option to zoom in on product attributes.  

    High-quality product photography
    High-quality product photography

    A recent game-changer in the fashion industry has been including different sets of models to accurately feature clothes of various shapes, heights, and weights. Instead of displaying a dress in only one size, fashion brands can have multiple models wearing various sizes for the same article of clothing.  

    9. Stay up to date with new trends

    Fashion eCommerce brands have to be particularly careful of continuously updating their product offering with the latest fashion trends for each season. They can boost sales with an in-demand product assortment. Continuously updated fashion inventory signifies that the brand is up-to-date with the latest fashion trends in the market and has unique products to offer. You can always get creative with new styling, better looks, and personalized product recommendations. 

    Conclusion

    Fashion eCommerce is rapidly growing and transforming at a staggering rate as technologies continue to advance. Traditional fashion brands can now expand their reach from brick-and-mortar shops to digital and eCommerce platforms to reach shoppers across the globe. The new digital selling opportunities also come with considerable challenges – from staying up to date with ever-evolving trends to managing dead stock. 
    Are you a fashion brand that needs help monitoring your product content? Or measuring the effectiveness of your online promotions? Or decoding customer sentiment from reviews they’ve left for your products? Sign up for a demo with our team to know how DataWeave can help!

  • 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.

  • Valentine’s Day eCommerce Insights

    Valentine’s Day eCommerce Insights

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions and help drive profitable growth in an intensifying competitive environment. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.         

  • 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. 

  • 2021 Cost-Push Inflationary Trends Ran Rampant, Impacting Holiday Discounts

    2021 Cost-Push Inflationary Trends Ran Rampant, Impacting Holiday Discounts

    Business has been anything but usual this holiday season, especially in the digital retail world. The holiday hustle and bustle historically seen in stores was once again occurring online, but not as anticipated given the current strength of consumer demand and the reemergence of COVID-19 limiting in-store traffic. While ‘Cyber Weekend’, Thanksgiving through Cyber Monday, continues to further its importance to retailers and brands, this year’s performance fell short of expectation due to product shortages and earlier promotions that pulled forward holiday demand.

    Holiday promotions were seen beginning as early as October in order to compete with 2020 Prime Day sales, but discounting, pricing and availability took an opposite direction from usual. This shift influenced our team to get a jump start on our 2021 digital holiday analysis to assess how drastic the changes were versus 2020 activity, and to understand how much of this change has been influenced by inflationary pressures and product scarcity.

    Scarcity Becomes a Reality

    Our initial analysis started by reviewing year-over-year product availability and pricing changes from January through September 2021, leading up to the holiday season, as detailed in our 2021 Cyber Weekend Preliminary Insights blog. We reviewed popular holiday categories like apparel, electronics, and toys, to have a broad sense of notable trends seen consistently throughout various, applicable marketplaces. What we found was a consistent decline in product availability over the last six months compared to last year, alongside an increase in prices.

    Although retailers significantly improved stock availability in November and early December 2021, even digital commerce giants like Amazon and Target were challenged to maintain consistent product availability on their website as seen below. While small in magnitude, there is also a declining trend occurring again closer toward the end of our analysis period, post Cyber Weekend, across all websites included in our analysis.

    Inventory Availability 2021 Holidays
    Source: Commerce Intelligence – Product Availability insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

    Greater Discounts, Higher Prices?

    With inflation at a thirty-nine year high, retailers and manufacturers have realized they can command higher prices without impacting demand as consumers have shown their willingness to pay the price, especially when threatened by product scarcity. Our assessment is that while some products and categories have responded drastically, manufacturers’ suggested retail prices (MSRPs) have increased nearly seven percent on average from January to December 2021. MSRP adjustments are not taken lightly either, as this is an indication increased prices will be part of a longer-term shift in product strategy.

    2021 MoM Retail Inflation Tracker
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com each month in 2021 comparing price increases from January 2021 base

    Our 2021 pre-Cyber Weekend analysis reviewed MSRP changes for select categories (Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion) on Amazon and Target.com, and found around forty-eight percent of products on Amazon and thirty-five percent of products on Target.com have increased their MSRPs year-over-year, but kept pre-holiday discount percentages the same.

    Looking more specifically as to what year-over-year changes occurred on Black Friday in 2021, we observed MSRPs increasing across the board for all categories at various magnitudes. This indicates why 2021 discounts appeared to be greater than or equivalent to 2020 for many categories, when in reality consumers paid a higher price than they would have in 2020 for the same items.

    2021 Black Friday MSRP Increases
    Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKU count from November 20-26th 2021

    On Amazon.com, categories like health & beauty have already increase MSRPs by a much greater percentage and magnitude versus Target.com leading up to and during Black Friday 2021, while other categories like furniture have increased MSRPs evenly on average across both retail websites. The below chart cites a few specific examples of year-over-year SKU-level MSRP, promotional price, and discount changes within found within the electronics, furniture, fashion, and health & beauty categories.

    Black Friday 2021 vs. 2020 SKU-level Price Changes
    Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKUs on Black Friday November 26th, 2020.

    Fewer, but Deeper Discounts

    From October through early November 2021, fewer products were discounted compared to this same period in 2020, and the few that were saw much deeper discounts apart from the home improvement category. The most extreme example we saw in discounts offered was within furniture where only three percent of SKUs were on discount in 2021 compared to twenty-six percent in 2020. Interestingly, the magnitude of discount was also higher pre-Cyber Weekend 2021 versus 2020, but this trend was not exclusive to furniture and was also seen within electronics, health & beauty, and home improvement.

    Pre-Black Friday 2021 and 2020 SKUs on Discount and Magnitude
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com Pre-Black Friday average selling price during November 20-26th 2021 versus average selling price from November 13-19th 2021 compared to Pre-Black Friday average selling price during November 19-25th 2020 versus average selling price from November 12-18th, 2020.

    Within the furniture category, the subcategories offering the greatest number of SKUs with price decreases on Black Friday 2021 were rugs by a wide margin, followed by cabinets, bed and bath, and entertainment units, but the magnitude of discounts offered were all under twenty percent.

    2021 Black Friday Furniture Category Price Decreases
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    Accounting for this phenomenon could have been retailers’ attempts to clear inventory for SKUs which hadn’t sold even during the period of severe supply chain shortages. With more products selling at higher prices this year, retailers were also able to use fewer SKUs with greater discounts to attract buyer in hopes of filling their digital baskets with more full-priced goods, helping to protect margins heading in to Cyber Weekend. Scarcity threats also encouraged consumers to buy early, even when not on promotion, to ensure they would have gifts in time for the holidays.

    The same trends seen pre-Cyber Weekend 2021 were also seen on Black Friday with a year-over-year decrease in the percentage of SKUs offered on discount versus 2020, and steeper price reductions for the discounted products which can also be attributed to the increase in MSRPs.

    Black Friday 2021 and 2020 SKUs on Discount and Magnitude
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    2021 Black Friday Price Increases?

    We all know Black Friday is all about price reductions, discounts and deals and so it’s rare to see actual price increases, yet for Black Friday 2021, trends ran counter to this. We observed price increases across all categories for around thirteen to nineteen percent of SKUs, with an average price increase of around fifteen percent in 2021 versus an average of only two percent in 2020.

    SKUs with Price Increases Black Friday 2021 and 2020
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    At an account level, we noticed a few interesting differences happening on Black Friday 2021 versus 2020 regarding category price changes. On Target.com, almost ninety percent of the bed and bath SKUs analyzed had a price change on Black Friday in 2021 versus 2020 with eighty-two percent presenting a higher price year-over-year versus only around seven percent showing a decrease, where on Amazon nearly forty-four percent of bed and bath SKUs showed an increase in price and around thirty-eight percent showed a decrease. Except for the health and beauty category on Target.com, more than half of the SKUs in each category saw a price increase on Black Friday versus a price decrease.

    2021 YoY Price Changes on Black Friday
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

    The magnitude of year-over-year price changes seen on Black Friday 2021 was significant across all categories, but the magnitude of price increases found on Amazon.com within the health and beauty category outpaced the rest by far. We reviewed three hundred and sixty-five SKUs on Amazon.com within the health & beauty category and saw almost eighty-three percent of them had a price change with around thirty-one percent decreasing prices and around fifty-two percent increasing prices. This means that within the health & beauty category on Amazon.com, more than fifty percent of the SKUs tracked were sold at a one hundred and seventy-six percent higher price on average during Black Friday 2021 versus 2020.

    Magnitude of Black Friday 2021 Price Increases
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

    The subcategories offering the greatest number of SKUs with price increases on Black Friday 2021 were cameras, followed by men’s fragrances, laptops, and desktops & accessories, but the magnitude of discounts offered were all under ten percent.

    2021 Subcategories with Price Increases during Black Friday
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    The Aftermath Post-2021 Cyber Weekend

    Extending this analysis beyond the holiday weekend, we analyzed price change activity from December third through the ninth across the top US retailers (chart below) and found that price decreases have been very minimal, comparatively speaking. Though there was a spike in number of price decreases from December 8th to the 9th, the percentage of SKUs with price decreases was still very low (less than three percent). We anticipate this trend will continue into 2022.

    SKUs with Price Decrease Post Cyber Weekend 2021
    Source: Commerce Intelligence – Pricing insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

    A Sign of Things to Come

    A confluence of inflationary trends, product shortages and consumer liquidity have driven many marketplace changes to occur simultaneously. Government programs in the form of stimulus checks, have put extra money in consumers’ hands, and so they’ve been more willing to spend. That, coupled with the shock in the supply chain, has motivated people to buy far ahead of the 2021 holiday season. Hence, retailers have needed to rely much less on across-the-board discounts. Promotions have been more strategic – we’ve seen deeper discounts over fewer products, likely used to draw consumers in to buy certain items, and once they’re there, customers are buying everything else at a non-discount level. When these factors once again normalize, we could see a return to the “race to the bottom” that has occurred since the financial crisis of 2008-2009, but for once, retailers may be able to maintain some pricing power as the 2021 holiday shopping season played out.

    Even though performance was not as anticipated and holiday sales did not grow as rapidly as they did in 2020, Cyber Monday was still the greatest online shopping day in 2021. Through it all, retailers managed to keep their digital shelves stocked and orders filled in time for the holidays for the most part, running the risk of housing aged inventory if goods didn’t arrive in time. Despite predictions for steep promotions in January 2022, with supply chains still challenged and inflationary pressures still full steam ahead, we don’t anticipate much in the way of enhanced discounts to continue beyond the holidays.

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions like how and when to address inflationary pressures, while also supporting many other day-to-day operations and help drive profitable growth in an intensifying competitive environment. Continue to follow us in the coming weeks for a detailed 2021 year-end review across more retailers and categories. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.         

  • 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.

  • 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.

  • 2021 Cyber Weekend Preliminary Insights

    2021 Cyber Weekend Preliminary Insights

    The exponential growth of eCommerce has forever changed holiday shopping as we know it. What was once led by the launch of Cyber Monday in 2005, has since expanded to ‘Cyber Five’ in 2018, now spans beyond an eight-week period, and is collectively the busiest digital shopping period of the year. Most retail websites have launched a ‘Thanksgiving Comes Early’ sales event for a mosaic of products, causing one to wonder how this ‘early start’ to holiday shopping will impact the traditional promotional cadence consumers have grown to expect to see launch closer to the holidays. Given today’s environmental challenges, threats of scarcity are also encouraging consumers to buy early, which could also impact traffic on the shopping days that have traditionally seen the highest sales volume from digital shoppers.

    In the current environment, the onus will be on consumers to keep a watch for their categories of interest and buy them as and when they appear on sale in their favorite store, because there is no guarantee of sustained availability. Of course, they might return and buy at a different store if a better deal comes up, but there’s a time cost for the dollars saved. More broadly, there has been enough noise made about deals and discounts to keep consumer interest and curiosity going.

    The early promotional start and heightened demand has influenced our team to get a jump start on our 2021 Black Friday analysis to look deeper at trends seen pre-Black Friday 2021 versus 2020. With this assessment, we can track how promotional prices and product availability rates may have changed throughout the event leading in to 2021 Cyber Five, and compare it to last year’s activity to understand how 2021 holiday sales may be impacted.

    We reviewed popular holiday categories like apparel, electronics, and toys (for kids and pets), to have a broad sense of notable trends seen consistently throughout various, applicable marketplaces. What we found is a consistent decline in product availability over the last six months and as compared to last year, alongside an increase in prices.

    We first analyzed availability changes for popular categories on Amazon, noted in the chart below, to understand how inventory may have changed throughout the year, and also compared to 2020. With the exception of batteries and solar power goods and books and maps, there appears to be consistency in greater product availability in 2021 versus 2020, but a slow decline in availability throughout 2021, leading into the holiday season.

    Source: DataWeave Commerce Intelligence – Product Availability in-stock percentage from July 2020 through September 2021 for a sample size of 1000+ products on Amazon.com

    When it came to our pricing analysis, we reviewed select categories on Amazon and Target.com, and found around fifty percent of products on both websites to have seen a price increase year-over-year, while only thirty-seven percent and sixteen percent of products saw a price decrease on Amazon and Target.com, respectively. We also see an increase in the manufacturer’s retail price (MRP) in 2021 versus 2020 for a very high proportion of products (forty-eight percent of products on Amazon and thirty-five percent of products on Target.com), but the discount percentages have remained the same.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: MRP and promotional pricing for 1000+ products on Amazon and Target.com were analyzed from November 13th – 15th, 2021 versus Pre-Black Friday November 24th & 25th 2020

    *Please reach out to our Retail Analytics experts for access to sub-category details available within the above analysis conducted on Amazon and Target.com.

    This indicates 2021 discounts may appear to be greater than or equivalent to 2020, but in reality, consumers will end up paying higher prices than they would have for the same items in 2020. The remainder of this article highlights our key findings found within each key category reviewed – Electronics, Apparel and Toys.

    Electronics Category Analysis

    The television category showcases a great example of how pricing fluctuations impact holiday promotional cadences. Based on our analysis, we found the average television price to have increased around seven percent from April to October 2021, as seen below and as noted within our analysis conducted with NerdWallet.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: The change in average price captured for televisions sold on Amazon from May 2021 through October 2021.

    In fact, on Amazon and Target.com, we see around eighty-four percent of the SKUs listed show both an MRP and promotional price increase in 2021 versus 2020 during pre-Black Friday times. One specific example found on Amazon is noted below for Samsung TV model QN65LS03TAFXZA, a 65 inch QLED TV that was priced at $1697 during this analysis at a fifteen percent discount from MRP, but was priced last year at $1497 without a discount from MRP. In essence, even though the TV offers a greater discount this year, it is actually more expensive than it was in 2020 at this same time of year.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: MRP and promotional pricing analysis on Amazon.com comparing prices from November 13th – 15th, 2021 versus Pre-Black Friday November 24th & 25th 2020

    Unlike TVs, the price of laptops has experienced a decrease over time based on our analysis conducted during the same timeframe, indicating these are a great buy for consumers this holiday season versus promotional offers seen in 2020.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: The month-over-month change in average price captured for televisions sold on Amazon from April 2021 through September 2021.

    Overall, our prediction is that within the electronics category, promotions during Cyber Five may be equivalent to last year’s offers, however, supply will be limited and the total spend versus last year will be greater to the consumer outside of Doorbuster deals offered on select models.

    Apparel Category Analysis

    The Luxury market is seeing a Roaring 20s-like feeling this season given the Covid-induced changes in work and lifestyle and higher disposable income. Therefore, our prediction is that prices for these goods are likely to remain flat, or offer very little discounts this season both due to supply constraints as well as higher demand. For example, our analysis on shoe pricing changes shows relative stability from April to October 2021.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: The change in average price captured for shoes sold on Amazon from May 2021 through October 2021.

    Given heightened demand and the Global shipping crisis, we anticipate luxury apparel categories to face out-of-stock challenges this holiday season, and therefore we also anticipate seeing less promotional activity for these items as well during Cyber Five 2021. To dive deeper into the severity of the impact, we looked at availability for clothing, accessories, and footwear categories from August 2020 until present to verify our thesis.

    Focusing only on clothing, accessories, and footwear, these categories followed the same downward trending pattern regarding product availability decreases this year with a decline from June (seventy-six percent versus eighty-six percent in May 2021) to September 2021 (the lowest rate seen at sixty-eight percent availability), followed by a partial recovery in October and November (achieving seventy-seven percent availability).

    Source: DataWeave’s Commerce Intelligence – Product Availability: 10k SKUs tracked across 11 retailers US websites (Farfetch, Brownsfashion, NetAPorter, EndClothing, 24s, Selfridges, Ssense, Harrods, Luisaviaroma, MyTheresa, MrPorter) tracked daily stock status in apparel categories; Availability is calculated as percent of instances when product is in stock against all instances tracked.

    Not all recoveries were the same however, and given this, we predict accessories to have the lowest availability rate and greatest risk of facing out of stocks heading into Cyber Five. From May through November 2021, accessories availability continued to decline significantly from month to month, beginning at eighty-three percent in May and ending at seventy-four percent in November. Given this continued decline and with Black Friday right around the corner, we don’t anticipate inventory levels to increase enough to meet the increased holiday demand.

    Source: DataWeave’s Commerce Intelligence – Product Availability: 10k SKUs tracked across 11 retailers US websites (Farfetch, Brownsfashion, NetAPorter, EndClothing, 24s, Selfridges, Ssense, Harrods, Luisaviaroma, MyTheresa, MrPorter) tracked daily stock status in apparel categories; Availability is calculated as percent of instances when product is in stock against all instances tracked.

    Toys & Games Category Analysis

    As noted by DigitalCommerce360, we also anticipate toys to be one of the greatest impacted categories this holiday season given the continued decline in overall availability for these items on Amazon.com, as one great example. Within our category analysis, we saw a steady decline in availability from March 2021 through June (eighty percent to sixty-one percent), followed by a period of stability from June through August (approximately sixty percent), followed by another decline from September through October, finally reaching the lowest availability of fifty-six percent (down twenty-four percent from March 2021).

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Toys & Games SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    The biggest sub-category within the toys department on Amazon, Sports and Outdoor Play, followed the same trend as Toys and Games overall through June 2021, also reaching its lowest availability of fifty-six percent. Instead of continuing along that pattern, Sports and Outdoor Play started on a recovery path, ending at a relatively high availability level of sixty-seven percent in October, which is only five percent lower than its highest availability (seventy-two percent in March 2021). Games and Accessories, the second largest sub-category in Toys and Games, had a continuous decline starting with eighty-nine percent in March 2021, reaching its lowest availability of fifty-four percent in October.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Toys & Games SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    The sub-category Tricycles, Scooters and Wagons interestingly had its highest availability from July to September 2021 (around eighty percent), unlike other sub-categories which as a whole, had their lowest availability during the same timeframe. From September through October, there was a significant decline (fourteen percent), reaching its lowest availability of sixty-seven percent. The sub-category Babies & Toddlers started on a continuous decline from its highest availability of eighty percent in April to its lowest availability of fifty-six percent in October.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Toys & Games SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    *Please reach out to our Retail Analytics experts for access to sub-category details available within the above analysis on the Toys and Games category on Amazon.com.

    Pet Toys Category Analysis

    When it comes to in demand holiday toys, you can’t forget about the needs for gifts for our furry friends and family. We also tracked sub-categories such as dog, cat, and bird toys, following the same methodology as tracked within Toys and Games to track pet toy availability changes.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Pet Toys SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    Dog toys, the biggest sub-category out of the three pet toys analyzed, had high availability – ninety percent in March 2021, but started to decline reaching a low of sixty-five percent in October. There was a period of stability from April to August (averaging seventy-seven percent), followed by a significant decline of over thirteen percent in from September to October. Cat toys, the second largest sub-category, also had its highest availability in March (eighty-nine percent) followed by a steady decline to sixty-six percent in June, a recovery from July to August (achieving seventy-three percent), followed by another decline during September and October, reaching its lowest availability of sixty-three percent (down twenty-six percent from eighty-one percent in March). Interestingly, dog toys which has a product count eight times greater than cat toys, had higher availability than cat toys during each of the months considered during the analysis.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Pet Toys SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    In Conclusion

    If we consider discounts and availability to be a good indicator of sales for the 2021 holiday season, with the Global shipping crisis looming over this year’s event, we expect retailers to have trouble keeping their inventory well stocked, which might affect growth rates. That being said, while discounts may be muted and popular items may come on very limited sales given constraints, we believe digital sales on Black Friday will see the highest year-over-year growth to date, given a number of supporting factors: scarcity threats increasing demand and the reason to buy, and consumers waiting to see if holiday offers surpass those see in the early start promotions, followed by the sudden rush to buy on Black Friday so as not to risk a given product being out of stock beyond this time period.

    We also anticipate seeing a continued decline in product availability day-to-day as we progress throughout Cyber Five 2021. Given the analysis conducted on 2020 trends, (we tracked nearly a one percent decline in availability on Black Friday 2020 vs. Thanksgiving Day, followed by a two percent decline on Cyber Monday), our data indicates products went out-of-stock at a faster rate then also.

    Ultimately only the digital-savvy retailers and brands will thrive during these opportune times, while others will continue to be in catch-up mode. Access to real-time marketplace insights can enable a first-to-market strategy, while having access to historical patterns can also help react faster to commonly seen future market factors, such as another pandemic or Global shipping crisis. These types of insights also support day-to-day operations, enabling retailers and brands to accelerate eCommerce growth, determine systems to distinguish their online strategies, discover efficiencies and drive profitable growth in an intensifying competitive environment.

    Continue to follow us in the coming weeks to see the insights we track through Cyber Five 2021, and be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

  • 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.

  • How Essential Goods Have Shaped Retail Strategies

    How Essential Goods Have Shaped Retail Strategies

    The rapid evolution in essential goods is rattling retail. That’s because the COVID-19 pandemic has dramatically changed shopping habits and retail necessities, leading to unpredictable shifts in demand.

    Most notably, U.S. e-commerce has surged by an astonishing 45% year-over-year, as the pandemic accelerated online shopping by five years.[1] Since more consumers now work and learn from home, many pandemic-inspired habits will likely shape retail for years to come.[2]

    Now that the risk of the second wave lies ahead, it’s the ideal time for retailers to review pandemic bestsellers and patterns to adapt to shifts in shopping behavior.


    Pandemic’s bestsellers shape retail strategies

    2020’s unexpected consumption patterns give retailers a glimpse of how they can adapt and thrive. The best-selling essential goods during the pandemic have included:

    • Toilet paper: +734% year-over-year (YoY) growth in March[3]
    • Disposable gloves: +670% in March[4]
    • Fitness equipment: + 535% YoY in online sales for February to March[5]
    • Hand sanitizer: +470% YoY for the week ending March 7[6]
    • Yeast: +410% YoY for the four weeks ending April 11[7]
    • Puzzles: +370% YoY in the last two weeks of March
    • Pyjamas: + 143% in online sales between March and April[8]

    As such, retailers can ensure their assortments contain these types of popular cross-category items, which reflect overall themes of consumers’ needs for self-sufficiency, wellness and comfort.

    E-grocery is also soaring, as experts predict a 40% rise in U.S. online grocery sales in 2020 due to the pandemic.[9] Top categories bought by online grocery shoppers include:

    • Packaged non-fresh food (69%)
    • Toiletries, personal care and diapers (63%)
    • Household cleaning and paper products (61%)[10]

    In response to these trends, retailers can prioritize shelf-stable center store products and non-food consumer goods throughout the pandemic.

    How retailers boost agility, clarity and sales amid COVID-19 chaos

    Consumer panic led to pricing volatility for hard-to-find items like hand sanitizer, disinfectant wipes and masks.[11] To keep up with competitors’ online price fluctuations, more retailers use competitive analytics to adapt their own prices accordingly. Notably, McKinsey & Company cites data insights and price sensitivity as the top two disruptive trends the pandemic has turbocharged.[12]

    In March, shortages of toilet paper and flour led consumers to react with panic and hoarding that created urgent supply chain issues. To avoid out-of-stock items, more retailers now turn to data insights to identify potential disruptions. Up-to-date insights help retailers spot emerging market trends and adapt their assortment to stock in-demand items.

    Now that more consumers shop online, retailers are investing in digital promotions to boost sales. Data analytics help retailers quickly evaluate the effectiveness of their promotions, which can inspire consumers to fill their baskets. Nimbly adapting to competitors’ promotions is essential, as McKinsey cites rising competition for deals among the pandemic’s most disruptive retail trends.[13]

    Avoid empty shelves: The pandemic has motivated more retailers to rely on data insights to make fast, effective pricing and assortment decisions.

    As consumption habits evolve, high-level dashboards help retailers quickly spot inventory shortages to prevent out-of-stocks.

    To make their retail strategies pandemic-proof, leading retailers are collaborating with DataWeave to access accurate, actionable insights that boost online agility and sales. Applying DataWeave’s trusted data gives retailers clarity amid today’s chaotic market and shifting demand for essential goods, so they can make effective decisions fast. Insights also help retailers enhance the customer experience by supporting in-stock product assortments, competitive pricing and effective promotions that boost sales, trust and loyalty. To see how DataWeave helps retailers stay agile and competitive, visit dataweave.com.


    [1] Perez, Sarah. COVID-19 pandemic accelerated shift to e-commerce by 5 years, new report says. TechCrunch. August 24, 2020.

    [2] Gottlieb, David. 5 Strategic Imperatives for Retail’s New Normal. Total Retail. August 18, 2020.

    [3] Weiczner, Jen. The case of the missing toilet paper: How the coronavirus exposed U.S. supply chain flaws. Fortune. May 18, 2020.

    [4] Clement, J. COVID-19 impact on fastest growing e-commerce categories in the U.S. 2020. Statista. June 19, 2020.

    [5] Gibson, Kate. Coronavirus inspires fitness buying binge that tops New Year’s. CBS News. April 1, 2020.

    [6] Chasark, Krisann. Coronavirus impact: Hair dye becoming next high-demand item amid COVID-19 pandemic. ABC News. April 11, 2020.

    [7] Guynn, Jessica and Kelly Tyko. Dry yeast flew off shelves during coronavirus pantry stocking. Here’s when you can buy it again. USA Today. April 23, 2020

    [8] Thomas, Lauren. Comfort is en vogue during coronavirus: PJ sales surge 143%, pants sales fall 13%. CNBC. May 12, 2020.

    [9] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.

    [10] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.

    [11] Levenson, Michael. Price Gouging Complaints Surge Amid Coronavirus Pandemic. The New York Times. March 27, 2020.

    [12] Kopka, Udo, Eldon Little, Jessica Moulton, René Schmutzler, and Patrick Simon. What got us here won’t get us there: A new model for the consumer goods industry. McKinsey & Company. July 30, 2020.

    [13] Kopka, Udo, Eldon Little, Jessica Moulton, René Schmutzler, and Patrick Simon. What got us here won’t get us there: A new model for the consumer goods industry. McKinsey & Company. July 30, 2020.

  • JioMart Launches Online Grocery Store

    JioMart Launches Online Grocery Store

    JioMart, the online channel for Reliance Retail Limited, launched in December 2019 as a contender in the e-grocery segment. Currently in India, this segment is being dominated by bigbasket, Amazon, Flipkart Supermart, Grofers, etc. After less than a year and from their initial launch in Mumbai, they now have their presence in 205 cities across India.

    According to their recent press release, they claim to be clocking over 250,000 daily orders, compared to bigbasket’s 220,000 and Amazon’s 150,000. To get an understanding of this rapid penetration, we had a look at the PIN codes that JioMart serves, spanning the country.

    The map below represents the percentage of PIN codes that are being served by JioMart’s online grocery in each state:

    **Disclaimer -Map for representation purposes only

    While states like Chandigarh, Delhi and Punjab in the North are covered extensively, JioMart has a stronger distribution in the Southern states.

    The image below shows the top ten states in India where JioMart’s online grocery has the highest presence:

    They’re yet to launch in 14 more states but it’s interesting to note that in this limited time, they’ve managed to cover 14% of the PIN codes in the country and all this, in the midst of lockdowns.

    Assortment

    To get an idea of the assortment in their range, we analyzed select PIN codes across three tiers of cities in India. The parameters we looked at were categories, brands and discounts to get an understanding of how JioMart is stacking up against its competitors. The cities we examined were:

    • Tier 1 – Bangalore, Delhi, Kolkata, Mumbai
    • Tier 2 – Ahmedabad, Jaipur, Kochi, Visakhapatnam
    • Tier 3 – Mohali, Mysore, Nagpur, Siliguri

    In its range, they offer eight broad categories, of which, we focussed on the four that offer the highest selection of products: home care, personal care, snacks & branded food and staples.

    The table below represents the average selection of products offered across each tier.

    Overview of discounts offered and the private label split

    Out of the assortment we looked at in the three tiers, we noticed that an average of 18% of the products are JioMart’s private labels. What stood out further is that private labels accounted for 48% in the Staples category and 24% in Personal Care. We noticed this trend (increase in the private label) when we did an analysis of Amazon.

    When it comes to discounts, we noticed that a near-total 91% of the products listed are being sold at a discount. Out of this, the highest discounts were witnessed in the Home Care and Staples categories.

    The brands with the highest number of products listed were Good Life, Reliance, Amul, Gillette and items sold loosely. All these accounted for 14% of the assortment. Out of these, Good Life, Reliance and the loose items are JioMart’s private labels.

    Competitor analysis

    To get an idea of where JioMart stands with relation to its competitors, we focussed on food and essentials in the Tier 1 cities. The table below highlights the number of product offerings in each category:

    It’s clear that in these categories (food and essentials), JioMart has the least number of products on discount. There’s no doubt that bigbasket is miles ahead in its product range/ assortment.

    To get a better idea of the discounting patterns, we analyzed the same categories to get a count of the number of products being discounted, as well as the average discount being offered. 

    We noticed that JioMart bookended our analysis – the least average discount, across the most number of products. Grofers offered the highest average discount of 23% with Flipkart Supermart and bigbasket closely behind. Lastly, bigbasket had the least number of products on discount with a little over 53%.

    Conclusion

    JioMart launched during a tumultuous and unprecedented time; the COVID-19 pandemic and the subsequent nation-wide lockdowns. Given this trial by fire, they managed to make an impact in this highly competitive space. Their expansion plans of tying up with mom and pop stores to fortify their penetration, had to take a back seat due to the ongoing situation but is sure to resume once conditions improve. This set-back did not however deter JioMart from attracting strategic investments from Facebook, Google and 12 other investors  in a span of 3 months. 

    In a study by Goldman Sachs, it found that India’s e-commerce business is expected to grow at a compound annual growth rate of 27% by 2024, resulting in a $99 billion market share. What’s even more shocking is that 50% of this market will be captured by Reliance Industries. It, therefore, stands to reason that all we’ve seen and heard of so far, is merely the tip of the iceberg and there’s surely more to come in the near future.

  • Decoding Alibaba’s Singles Day Sales

    Decoding Alibaba’s Singles Day Sales

    An average of $11.7 million per second was the rate at which Alibaba clocked $1 billion in sales during the first 85 seconds of Singles’ Day. As Alibaba’s annual sale event continues to grow in scale, referring to it as a global retail phenomenon is an understatement. Alibaba closed the day having shipped 1.04 billion express packages based on sales of merchandize worth 213.5 billion yuan ($30.67 billion).

    This performance shredded any lingering concerns analysts may have harbored about the prospects of this year’s sale, given the international backdrop of the ongoing trade skirmish between the US and China.

    Along with attractive discounts across a range of product categories, Singles’ Day also promised an integrated experience fusing entertainment, digital and shopping, in stark contrast to other large global sale events like Black Friday, which focus predominantly on discounts.

    At DataWeave, we set out to investigate if all the hype resulted in actual price benefits to the shoppers and how the various categories and brands performed in terms of sales during the event. To do this, we leveraged our proprietary data aggregation and analysis platform to capture a range of diverse data points on Tmall Global, covering unit sales (reported by the website) and pricing associated with Tmall Global’s major categories over the Singles’ Day period.

    Our Methodology

    We captured 5 separate snapshots of data from Tmall.com during the period between October 25 and November 14, encompassing over 15,000 unique products each time, across 15 product categories.

    To calculate the average discount rate, we considered the percentage difference between the maximum retail price and the available price of each product. We also looked at the additional discount rate, for which we compared the available price during Singles’ Day to the available price from before the sale. This metric reflects the truest value to the shopper during Singles’ Day in terms of price.

    Our AI-powered technology platform is also capable of capturing prices embedded in an image. For example, the offer price of ¥4198 was extracted accurately from the accompanying image by our algorithms and attributed as the available price while ¥100 from the same image was ignored.

    This technology was employed across hundreds of products using DataWeave’s proprietary Computer Vision technology.

    Domestic Appliances and Digital/Computer Categories Powered Turnover

    The Domestic Appliances and Digital/Computer categories dominated the Singles Day Sale in terms of absolute sales turnover. This isn’t surprising, since the average order value for these categories are typically much higher compared to the other categories analyzed.

    What clearly stands out in the above infographic is that the two largest categories in terms of sales turnover had average additional discounts of only 2 per cent and 0 per cent — a rather surprising insight. In general, with the exceptions of Women’s skincare, Men’s skincare, and Women’s bags (11 per cent, 10 per cent, and 9 per cent respectively), all other categories saw low additional discounts during Singles’ Day.

    However, the absolute discounts across the board were consistently high, with only Luggage (6 per cent), Digital/Computer (9 per cent) and Women’s wear (12 per cent) staying significantly below the 20 per cent mark. In fact, eight categories enjoyed absolute discounts greater than 30 per cent.

    Among common categories between Men and Women, the Men clocked more sales in Men’s wear, shoes, and bags. Only skincare proved to be an exception, where Women’s skincare generated twice the turnover of their Men’s equivalent.

    The Infants category was another intriguing sector to emerge during the sale. Both Diapers (38 per cent) and Infant’s Formula (25 per cent) were substantially discounted, despite only receiving low additional discounts of 2 per cent and 0 per cent respectively – indicating aggressive pricing strategies in this category even during non-sale time periods.

    The biggest takeaway from our analysis is the lack of any correlation between sales turnover and additional discounts, or even the absolute discounts.

    International Brands Make Gains

    International brands continue to penetrate the Chinese market showing up amongst the Top 5 brands of 13 of the 16 categories on sale.

    In the Diaper category, Pampers delivered nearly twice the sales turnover of its next biggest competitor. As expected, Apple and Huawei battled it out for honors in the Digital/Computer category although Xiaomi enjoyed pleasing results, nearly matching Huawei’s sales to go with its sales leadership of the Domestic Appliances category. Local brands, though, swept the Domestic Appliances, Furniture and Women’s Wear categories.

    The challenge posed by Chinese brands was illustrated by Nike’s spot in the second place in the highly competitive Men’s Shoes category after Anta.

    International brands topped only five of the 16 categories and Top 3 positions in ten categories. Still, there’s a growing presence of international brands in China’s eCommerce.

    Gillette won handsomely over its competition in the Personal Care category while Skechers enjoyed a similar result in Women’s Shoes, racking up nearly twice the retail sales of its nearest competitor. Another category dominated by international brands was the Women’s Cosmetics category where international brands accounted for 4 of the Top 5 brands.

    Similarly, Samsonite’s acquisition of American Tourister gave it two top 5 brands in the Luggage category. Other global brands to make the cut during the Singles’ Day sale included L’Oréal, Canada’s Hershel, Playboy, South Korea’s Innisfree and Japan’s Uniqlo.

    It’s Not All About Price On Singles’ Day

    The dramatic rise in shopping during Singles’ Day is not driven solely by price reductions. Alibaba’s commitment to its “New Retail” strategic model has led the Chinese giant to channel its impressive resources to focus on bringing together the online elements of its business with the more traditional offline aspects of its retail distribution. This is combined with entertainment to create a larger story based around the shopper’s overall “experience” rather than just driving “attractive prices” as a short-term retail hook.

    Alibaba is betting big on erasing the line between online and offline and its futuristic vision of structuring retail around the way people actually want to shop. Based on the consistently impressive results of Singles’ Day year after year, “New Retail” has a promising future.

    If you wish to know more about how DataWeave aggregates data from online sources to provide actionable insights to retailers and consumer brands, check out our website!

  • Dataweave – Smartphones vs Tablets: Does size matter?

    Dataweave – Smartphones vs Tablets: Does size matter?

    Smartphones vs Tablets: Does size matter?

    We have seen a steady increase in the number of smartphones and tablets since the last five years. Looking at the number of smartphones, tablets and now wearables ( smart watches and fitbits ) that are being launched in the mobiles market, we can truly call this ‘The Mobile Age’.

    We, at DataWeave, deal with millions of data points related to products which vary from electronics to apparel. One of the main challenges we encounter while dealing with this data is the amount of noise and variation present for the same products across different stores.

    One particular problem we have been facing recently is detecting whether a particular product is a mobile phone (smartphone) or a tablet. If it is mentioned explicitly somewhere in the product information or metadata, we can sit back and let our backend engines do the necessary work of classification and clustering. Unfortunately, with the data we extract and aggregate from the Web, chances of finding this ontological information is quite slim.

    To address the above problem, we decided to take two approaches.

    • Try to extract this information from the product metadata
    • Try to get a list of smartphones and tablets from well known sites and use this information to augment the training of our backend engine

    Here we will talk mainly about the second approach since it is more challenging and engaging than the former. To start with, we needed some data specific to phone models, brands, sizes, dimensions, resolutions and everything else related to the device specifications. For this, we relied on a popular mobiles/tablets product information aggregation site. We crawled, extracted and aggregated this information and stored it as a JSON dump. Each device is represented as a JSON document like the sample shown below.

    { "Body": { "Dimensions": "200 x 114 x 8.7 mm", "Weight": "290 g (Wi-Fi), 299 g (LTE)" }, "Sound": { "3.5mm jack ": "Yes", "Alert types": "N/A", "Loudspeaker ": "Yes, with stereo speakers" }, "Tests": { "Audio quality": "Noise -92.2dB / Crosstalk -92.3dB" }, "Features": { "Java": "No", "OS": "Android OS, v4.3 (Jelly Bean), upgradable to v4.4.2 (KitKat)", "Chipset": "Qualcomm Snapdragon S4Pro", "Colors": "Black", "Radio": "No", "GPU": "Adreno 320", "Messaging": "Email, Push Email, IM, RSS", "Sensors": "Accelerometer, gyro, proximity, compass", "Browser": "HTML5", "Features_extra detail": "- Wireless charging- Google Wallet- SNS integration- MP4/H.264 player- MP3/WAV/eAAC+/WMA player- Organizer- Image/video editor- Document viewer- Google Search, Maps, Gmail,YouTube, Calendar, Google Talk, Picasa- Voice memo- Predictive text input (Swype)", "CPU": "Quad-core 1.5 GHz Krait", "GPS": "Yes, with A-GPS support" }, "title": "Google Nexus 7 (2013)", "brand": "Asus", "General": { "Status": "Available. Released 2013, July", "2G Network": "GSM 850 / 900 / 1800 / 1900 - all versions", "3G Network": "HSDPA 850 / 900 / 1700 / 1900 / 2100 ", "4G Network": "LTE 800 / 850 / 1700 / 1800 / 1900 / 2100 / 2600 ", "Announced": "2013, July", "General_extra detail": "LTE 700 / 750 / 850 / 1700 / 1800 / 1900 / 2100", "SIM": "Micro-SIM" }, "Battery": { "Talk time": "Up to 9 h (multimedia)", "Battery_extra detail": "Non-removable Li-Ion 3950 mAh battery" }, "Camera": { "Video": "Yes, 1080p@30fps", "Primary": "5 MP, 2592 x 1944 pixels, autofocus", "Features": "Geo-tagging, touch focus, face detection", "Secondary": "Yes, 1.2 MP" }, "Memory": { "Internal": "16/32 GB, 2 GB RAM", "Card slot": "No" }, "Data": { "GPRS": "Yes", "NFC": "Yes", "USB": "Yes, microUSB (SlimPort) v2.0", "Bluetooth": "Yes, v4.0 with A2DP, LE", "EDGE": "Yes", "WLAN": "Wi-Fi 802.11 a/b/g/n, dual-band", "Speed": "HSPA+, LTE" }, "Display": { "Multitouch": "Yes, up to 10 fingers", "Protection": "Corning Gorilla Glass", "Type": "LED-backlit IPS LCD capacitive touchscreen, 16M colors", "Size": "1200 x 1920 pixels, 7.0 inches (~323 ppi pixel density)" } }

    From the above document, it is clear that there are a lot of attributes that can be assigned to a mobile device. However, we would not need all of them for building our simple algorithm for labeling smartphones and tablets. I had decided to use the device screen size for separating out smartphones vs tablets, but I decided to take some suggestions from our team. After sitting down and taking a long, hard look at our dataset, Mandar had an idea of using the device dimensions also for achieving the same goal!

    Finally, the attributes that we decided to use were,

    • Size
    • Title
    • Brand
    • Device dimensions

    Screen sizeI wrote some regular expressions for extracting out the features related to the device screen size and resolution. Getting the resolution was easy, which was achieved with the following Python code snippet. There were a couple of NA values but we didn’t go out of our way to get the data by searching on the web because resolution varies a lot and is not a key attribute for determining if a device is a phone or a tablet.

    size_str = repr(doc["Display"]["Size"]) resolution_pattern = re.compile(r'(?:\S+\s)x\s(?:\S+\s)\s?pixels') if resolution_pattern.findall(size_str): resolution = ''.join([token.replace("'","") for token in resolution_pattern.findall(size_str)[0].split()[0:3]]) else: resolution = 'NA'

    But the real problems started when I wrote regular expressions for extracting the screen size. I started off with analyzing the dataset and it seemed that screen size was mentioned in inches so I wrote the following regular expression for getting screen size.

    size_str = repr(doc[“Display”][“Size”]) screen_size_pattern = re.compile(r'(?:\S+\s)\s?inches’) if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0].split()[0] else: screen_size = ‘NA’

    However, I noticed that I was getting a lot of ‘NA’ values for many devices. On looking up the same devices online, I noticed there were three distinct patterns with regards to screen size. They are,

    • Screen size in ‘inches’
    • Screen size in ‘lines’
    • Screen size in ‘chars’ or ‘characters’

    Now, some of you might be wondering what on earth do ‘lines’ and ‘chars’ mean and how do they measure screen size. On digging it up, I found that basically both of them mean the same thing but in different formats. If we have ‘n lines’ as the screen size, it means, the screen can display at most ‘n’ lines of text at any instance of time. Likewise, if we have ‘n x m chars’ as the screen size, it means the device can diaplay ‘n’ lines of text at any instance of time with each line having a maximum of ‘m’ characters. The picture below will make things more clear. It represents a screen of 4 lines or 4 x 20 chars.

    Thus, the earlier logic for extracting screen size had to be modified and we used the following code snippet. We had to take care of multiple cases in our regexes, because the data did not have a consistent format.

    Thus, the earlier logic for extracting screen size had to be modified and we used the following code snippet. We had to take care of multiple cases in our regexes, because the data did not have a consistent format.

    size_str = repr(doc["Display"]["Size"]) screen_size_pattern = re.compile(r'(?:\S+\s)\s?inc[h|hes]') if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0] .replace("'","").split()[0]+' inches' else: screen_size_pattern = re.compile(r'(?:\S+\s)\s?lines') if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0] .replace("'","").split()[0]+' lines' else: screen_size_pattern = re.compile(r'(?:\S+\s)x\s(?:\S+\s)\s?char[s|acters]') if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0] .replace("'","").split()[0]+' lines' else: screen_size = 'NA'

    Mandar helped me out with extracting the ‘dimensions’ attribute from the dataset and performing some transformations on it to get the total volume of the phone. It was achieved using the following code snippet.

    dimensions = doc['Body']['Dimensions'] dimensions = re.sub (r'[^\s*\w*.-]', '', dimensions.split ('(') [0].split (',') [0].split ('mm') [0]).strip ('-').strip ('x') if not dimensions: dimensions = 'NA' total_area = 'NA' else: if 'cc' in dimensions: total_area = dimensions.split ('cc') [0] else: total_area = reduce (operator.mul, [float (float (elem.split ('-') [0])/10) for elem in dimensions.split ('x')], 1) total_area = round(float(total_area),3)

    We used PrettyTable to output the results in a clear and concise format.

    Next, we stored the above data in a csv file and used PandasMatplotlib, Seaborn and IPython to do some quick exploratory data analysis and visualizations. The following depicts the top ten brands with the most number of mobile devices as per the dataset.

    Then, we looked at the device area frequency for each brand using boxplots as depicted below. Based on the plot, it is quite evident that almost all the plots are right skewed, with a majority of the distribution of device dimensions (total area) falling in the range [0,150]. There are some notable exceptions like ‘Apple’ where the skew is considerably less than the general trend. On slicing the data for the brand ‘Apple’, we noticed that this was because devices from ‘Apple’ have an almost equal distribution based on the number of smartphones and tablets, leading to the distribution being almost normal.

    Based on similar experiments, we noticed that tablets had larger dimensions as compared to mobile phones, and screen sizes followed that same trend. We made some quick plots with respect to the device areas as shown below.

    Now, take a look at the above plots again. The second plot shows the distribution of device areas in a kernel density plot. This distribution resembles a Gaussian distribution but with a right skew. [Mandar reckons that it actually resembles a Logistic distribution, but who’s splitting hairs, eh? ;)] The histogram plot depicts the same, except here we see the frequency of devices vs the device areas. Looking at it closely, Mandar said that the bell shaped curve had the maximum number of devices and those must be all the smartphones, while the long thin tail on the right side must indicate tablets. So we set a cutoff of 160 cubic centimeters for distinguishing between phones and tablets.

    We also decided to calculate the correlation between ‘Total Area’ and ‘Screen Size’ because as one might guess, devices with larger area have large screen sizes. So we transformed the screen sizes from textual to numeric format based on some processing, and calculated the correlation between them which came to be around 0.73 or 73%

    We did get a high correlation between Screen Size and Device Area. However, I still wanted to investigate why we didn’t get a score close to 90%. On doing some data digging, I noticed an interesting pattern.

    After looking at the above results, what came to our minds immediately was: why do phones with such small screen sizes have such big dimensions? We soon realized that these devices were either “feature phones” of yore or smartphones with a physical keypad!

    Thus, we used screen sizes in conjunction with dimensions for labeling our devices. After a long discussion, we decided to use the following logic for labeling smartphones and tablets.

    device_class = None if total_area >= 160.0: device_class = 'Tablet' elif total_area < 160.0: device_class = 'Phone' if 'lines' in screen_size: device_class = 'Phone' elif 'inches' in screen_size: if float(screen_size.split()[0]) < 6.0: device_class = 'Phone'

    After all this fun and frolic with data analysis, we were able to label handheld devices correctly, just like we wanted it!

    Originally published at blog.priceweave.com.

  • Benefits of Assortment Intelligence

    Benefits of Assortment Intelligence

    In retail, product assortment plays a critical role in selling effectively. It impacts the everyday decision making of category managers, brand managers, the merchandising, planning, and logistics teams. A good assortment mix helps achieve the following objectives:

    1. Reduce acquisition costs for new customers (as well as retain existing customers)
    2. Increase penetration by catering to a variety of customer segments
    3. Optimize planning and inventory management costs.

    Increasingly, retailers are moving away from a generic one-size-fits all assortment planning model, to a more dynamic and data driven approach. As a result, assortment benchmarking followed by assortment planning are activities that take place round the year. The breadth and depth of one’s assortment achieved through assortment benchmarking can define how and when products get bought.

    A number of factors are crucial for assortment planning: analytics over internal data, intuition, experience, and understanding gained through trends. In addition to these, tracking assortment changes on competitors’ websites helps retailers track and adjust their product mix by adjusting features such as brands, colors, variants, and pricing. The goal is to help users find exactly what they are looking for, the moment they are looking for it.

    Let’s see how we can achieve this through Assortment Intelligence tools in a moment. But first, some basics.

    What is Assortment Intelligence?

    Assortment intelligence refers to online retailers tracking, analysing a competitor’s assortment, and benchmarking it against one’s one assortment. Assortment intelligence tools make this process efficient. A good assortment intelligence tool such as PriceWeave gives you information the breadth and depth of your competitors’ assortment across categories and brands. It helps you analyze assortment through different lenses: colors, variants, sizes, shapes, and other technical specifications. With the help of an assortment intelligence tool, a retailer can get a good understanding about what products competitors have, how they perform and whether they should add these products to their existing catalog.

    Who uses Assortment Intelligence?

    Assortment tracking is used by retailers operating across categories as varied as footwear, electronics, jewelry, household goods,appliances, accessories, tools, handbags, furniture, clothing, baby products, and books among others.

    Some Uses of Assortment Intelligence

    Gaps in Catalog: Discover products/brands your competitors are offering that are not on your catalog, and add them.

    Unique Offerings: Find products/brands that only you are offering and decide whether you are pricing them right. May be you want to bump up their prices.

    Compare and analyze product assortment across dimensions: Benchmark your assortments across different dimensions and combinations thereof. Understand your as well as competitors’ focus areas. You can do this in aggregate as well as at the category/brand/feature level. Below we show a few examples.

    Effectively measure discount distributions across brands and/or sources. Understand your competitors’ “sweet spots” in terms of discounts.

    Understand assortment spread across price ranges. Are you focusing on all price ranges or only a few? Is that a decision you made consciously?

    Deep dive using smart filters — monitor specific competitors, brands and sets of products with filters such as colors, variants, sizes and other product features.

    Why do it?

    Assortment Intelligence not only increases sales and improves margins, but also helps reduce planning and inventory costs. It allows retailers to strike the right balance between assortment and inventory while maximizing sales. Retailers can take informed decisions by analyzing one’s own as well as competitors’ assortments. Businesses gain an edge by identifying opportunities around changes in product mix and make quick decisions. By identifying areas that need focus, and taking timely actions, an assortment intelligence tool will help improve the bottom line.

    What does PriceWeave bring in?

    With a feature-rich product such as PriceWeave, you can do all of the above and more everyday (or more frequently if you like). In addition, you can get all assortment related data as reports in case you want to do your own analysis. You can also set alerts on any changes that you want to track.

    PriceWeave lets you drill down as deep as you like. Assortments do not have to be based on high level dimensions or standard features like colors and sizes. You can analyze assortments based on technical specs of products (RAM size, cloth material, style, shape, etc.) or their combinations.

    Assortment Intelligence is an important part of the PriceWeave offering. If you’d like us to help you make smarter assortment intelligence decisions talk to us

    About Priceweave

    PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.

    Originally published at blog.priceweave.com.

  • Mining Twitter to Analyze Product Trends | DataWeave

    Mining Twitter to Analyze Product Trends | DataWeave

    Due to the massive growth of social media in the last decade, it has become a rage among data enthusiasts to tap into the vast pool of social data and gather interesting insights like trending items, reception of newly released products by society, popularity measures to name a few.

    We are continually evolving PriceWeave, which has the most extensive set of offerings when it comes to providing actionable insights to retail stores and brands. As part of the product development, we look at social data from a variety of channels to mine things like: trending products/brands; social engagement of stores/brands; what content “works” and what does not on social media, and so forth.

    We do a number of experiments with mining Twitter data, and this series of blog posts is one of the outputs from those efforts.

    In some of our recent blog posts, we have seen how to look at current trends and gather insights from YouTube the popular video sharing website. We have also talked about how to create a quick bare-bones web application to perform sentiment analysis of tweets from Twitter. Today I will be talking about mining data from Twitter and doing much more with it than just sentiment analysis. We will be analyzing Twitter data in depth and then we will try to get some interesting insights from it.

    To get data from twitter, first we need to create a new Twitter application to get OAuth credentials and access to their APIs. For doing this, head over to the Twitter Application Management page and sign in with your Twitter credentials. Once you are logged in, click on the Create New App button as you can see in the snapshot below. Once you create the application, you will be able to view it in your dashboard just like the application I created, named DataScienceApp1_DS shows up in my dashboard depicted below.

    On clicking the application, it will take you to your application management dashboard. Here, you will find the necessary keys you need in the Keys and Access Tokens section. The main tokens you need are highlighted in the snapshot below.

    I will be doing most of my analysis using the Python programming language. To be more specific, I will be using the IPython shell, but you are most welcome to use the language of your choice, provided you get the relevant API wrappers and necessary libraries.

    Installing necessary packages

    After obtaining the necessary tokens, we will be installing some necessary libraries and packages, namely twitter, prettytable and matplotlib. Fire up your terminal or command prompt and use the following commands to install the libraries if you don’t have them already.

    Creating a Twitter API Connection

    Once the packages are installed, you can start writing some code. For this, open up the IDE or text editor of your choice and use the following code segment to create an authenticated connection to Twitter’s API. The way the following code snippet works, is by using your OAuth credentials to create an object called auth that represents your OAuth authorization. This is then passed to a class called Twitter belonging to the twitter library and we create a resource object named twitter_api that is capable of issuing queries to Twitter’s API.

    If you do a print twitter_api and all your tokens are corrent, you should be getting something similar to the snapshot below. This indicates that we’ve successfully used OAuth credentials to gain authorization to query Twitter’s API.

    Exploring Trending Topics

    Now that we have a working Twitter resource object, we can start issuing requests to Twitter. Here, we will be looking at the topics which are currently trending worldwide using some specific API calls. The API can also be parameterized to constrain the topics to more specific locales and regions. Each query uses a unique identifier which follows the Yahoo! GeoPlanet’s Where On Earth (WOE) ID system, which is an API itself that aims to provide a way to map a unique identifier to any named place on Earth. The following code segment retrieves trending topics in the world, the US and in India.

    Once you print the responses, you will see a bunch of outputs which look like JSON data. To view the output in a pretty format, use the following commands and you will get the output as a pretty printed JSON shown in the snapshot below.

    To view all the trending topics in a convenient way, we will be using list comprehensions to slice the data we need and print it using prettytable as shown below.

    On printing the result, you will get a neatly tabulated list of current trends which keep changing with time.

    Now, we will try to analyze and see if some of these trends are common. For that we use Python’s set data structure and compute intersections to get common trends as shown in the snapshot below.

    Interestingly, some of the trending topics at this moment in the US are common with some of the trending topics in the world. The same holds good for US and India.

    Mining for Tweets

    In this section, we will be looking at ways to mine Twitter for retrieving tweets based on specific queries and extracting useful information from the query results. For this we will be using Twitter API’s GET search/tweets resource. Since the Google Nexus 6 phone was launched recently, I will be using that as my query string. You can use the following code segment to make a robust API request to Twitter to get a size-able number of tweets.

    The code snippet above, makes repeated requests to the Twitter Search API. Search results contain a special search_metadata node that embeds a next_results field with a query string that provides the basis of making a subsequent query. If we weren’t using a library like twitter to make the HTTP requests for us, this preconstructed query string would just be appended to the Search API URL, and we’d update it with additional parameters for handling OAuth. However, since we are not making our HTTP requests directly, we must parse the query string into its constituent key/value pairs and provide them as keyword arguments to the search/tweets API endpoint. I have provided a snapshot below, showing how this dictionary of key/value pairs are constructed which are passed as kwargs to the Twitter.search.tweets(..) method.

    Analyzing the structure of a Tweet

    In this section we will see what are the main features of a tweet and what insights can be obtained from them. For this we will be taking a sample tweet from our list of tweets and examining it closely. To get a detailed overview of tweets, you can refer to this excellent resource created by Twitter. I have extracted a sample tweet into the variable sample_tweet for ease of use. sample_tweet.keys() returns the top-level fields for the tweet.

    Typically, a tweet has some of the following data points which are of great interest.

    The identifier of the tweet can be accessed through sample_tweet[‘id’]

    • The human-readable text of a tweet is available through sample_tweet[‘text’]
    • The entities in the text of a tweet are conveniently processed and available through sample_tweet[‘entities’]
    • The “interestingness” of a tweet is available through sample_tweet[‘favorite_count’] and sample_tweet[‘retweet_count’], which return the number of times it’s been bookmarked or retweeted, respectively
    • An important thing to note, is that, the retweet_count reflects the total number of times the original tweet has been retweeted and should reflect the same value in both the original tweet and all subsequent retweets. In other words, retweets aren’t retweeted
    • The user details can be accessed through sample_tweet[‘user’] which contains details like screen_name, friends_count, followers_count, name, location and so on

    Some of the above datapoints are depicted in the snapshot below for the sample_tweet. Note, that the names have been changed to protect the identity of the entity that created the status.

    Before we move on to the next section, my advice is that you should play around with the sample tweet and consult the documentation to clarify all your doubts. A good working knowledge of a tweet’s anatomy is critical to effectively mining Twitter data.

    Extracting Tweet Entities

    In this section, we will be filtering out the text statuses of tweets and different entities of tweets like hashtags. For this, we will be using list comprehensions which are faster than normal looping constructs and yield substantial perfomance gains. Use the following code snippet to extract the texts, screen names and hashtags from the tweets. I have also displayed the first five samples from each list just for clarity.

    Once you print the table, you should be getting a table of the sample data which should look something like the table below but with different content ofcourse!

    Frequency Analysis of Tweet and Tweet Entities

    Once we have all the required data in relevant data structures, we will do some analysis on it. The most common analysis would be a frequency analysis where we find out the most common terms occurring in different entities of the tweets. For this we will be making use of the collection module. The following code snippet ranks the top ten most occurring tweet entities and prints them as a table.

    The output I obtained is shown in the snapshot below. As you can see, there is a lot of noise in the tweets because of which several meaningless terms and symbols have crept into the top ten list. For this, we can use some pre-processing and data cleaning techniques.

    Analyzing the Lexical Diversity of Tweets

    A slightly more advanced measurement that involves calculating simple frequencies and can be applied to unstructured text is a metric called lexical diversity. Mathematically, lexical diversity can be defined as an expression of the number of unique tokens in the text divided by the total number of tokens in the text. Let us take an example to understand this better. Suppose you are listening to someone who repeatedly says “and stuff” to broadly generalize information as opposed to providing specific examples to reinforce points with more detail or clarity. Now, contrast that speaker to someone else who seldom uses the word “stuff” to generalize and instead reinforces points with concrete examples. The speaker who repeatedly says “and stuff” would have a lower lexical diversity than the speaker who uses a more diverse vocabulary.

    The following code snippet, computes the lexical diversity for status texts, screen names, and hashtags for our data set. We also measure the average number of words per tweet.

    The output which I obtained is depicted in the snapshot below.

    Now, I am sure you must be thinking, what on earth do the above numbers indicate? We can analyze the above results as follows.

    • The lexical diversity of the words in the text of the tweets is around 0.097. This can be interpreted as, each status update carries around 9.7% unique information. The reason for this is because, most of the tweets would contain terms like Android, Nexus 6, Google
    • The lexical diversity of the screen names, however, is even higher, with a value of 0.59 or 59%, which means that about 29 out of 49 screen names mentioned are unique. This is obviously higher because in the data set, different people will be posting about Nexus 6
    • The lexical diversity of the hashtags is extremely low at a value of around 0.029 or 2.9%, implying that very few values other than the #Nexus6hashtag appear multiple times in the results. This is relevant because tweets about Nexus 6 should contain this hashtag
    • The average number of words per tweet is around 18 words

    This gives us some interesting insights like people mostly talk about Nexus 6 when queried for that search keyword. Also, if we look at the top hashtags, we see that Nexus 5 co-occurs a lot with Nexus 6. This might be an indication that people are comparing these phones when they are tweeting.

    Examining Patterns in Retweets

    In this section, we will analyze our data to determine if there were any particular tweets that were highly retweeted. The approach we’ll take to find the most popular retweets, is to simply iterate over each status update and store out the retweet count, the originator of the retweet, and status text of the retweet, if the status update is a retweet. We will be using a list comprehension and sort by the retweet count to display the top few results in the following code snippet.

    The output I obtained is depicted in the following snapshot.

    From the results, we see that the top most retweet is from the official googlenexus channel on Twitter and the tweet speaks about the phone being used non-stop for 6 hours on only a 15 minute charge. Thus, you can see that this has definitely been received positively by the users based on its retweet count. You can detect similar interesting patterns in retweets based on the topics of your choice.

    Visualizing Frequency Data

    In this section, we will be creating some interesting visualizations from our data set. For plotting we will be using matplotlib, a popular Python plotting library which comes inbuilt with IPython. If you don’t have matplotlib loaded by default use the command import matplotlib.pyplot as plt in your code.

    Visualizing word frequencies

    In our first plot, we will be displayings the results from the words variable which contains different words from the tweet status texts. Using Counter from the collections package, we generate a sorted list of tuples, where each tuple is a (word, frequency) pair. The x-axis value will correspond to the index of the tuple, and the y-axis will correspond to the frequency for the word in that tuple. We transform both axes into a logarithmic scale because of the vast number of data points.

    Visualizing words, screen names, and hashtags

    A line chart of frequency values is decent enough. But what if we want to find out the number of words having a frequency between 1–5, 5–10, 10–15… and so on. For this purpose we will be using a histogram to depict the frequencies. The following code snippet achieves the same.

    What this essentially does is, it takes all the frequencies and groups them together and creates bins or ranges and plots the number of entities which fall in that bin or range. The plots I obtained are shown below.

    From the above plots, we can observe that, all the three plots follow the “Pareto Principle” i.e, almost 80% of the words, screen names and hashtags have a frequency of only 20% in the whole data set and only 20% of the words, screen names and hashtags have a frequency of more than 80% in the data set. In short, if we consider hashtags, a lot of hashtags occur maybe only once or twice in the whole data set and very few hashtags like #Nexus6 occur in almost all the tweets in the data set leading to its high frequency value.

    Visualizing retweets

    In this visualization, we will be using a histogram to visualize retweet counts using the following code snippet.

    The plot which I obtained is shown below.

    Looking at the frequency counts, it is clear that very few retweets have a large count.

    I hope you have seen by now, how powerful Twitter APIs are and using simple Python libraries and modules, it is really easy to generate very powerful and interesting insights. That’s all for now folks! I will be talking more about Twitter Mining in another post sometime in the future. A ton of thanks goes out to Matthew A. Russell and his excellent book Mining the Social Web, without which this post would never have been possible. Cover image credit goes to Social Media.