The DataWeave Blog

Tag: US

  • 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.

  • How Retailers and Brands Can Navigate Skyrocketing Olive Oil Prices in 2024

    How Retailers and Brands Can Navigate Skyrocketing Olive Oil Prices in 2024

    Olive oil, renowned for its complex flavor and myriad health benefits, holds a significant place in the global market, valued at $14.64 billion in 2023. It is anticipated to reach $19.77 billion by 2032, with a steady compound annual growth rate (CAGR) of 3.42%.

    This growth is fueled by:

    • Increased consumer demand for healthier oils.
    • Olive oil’s rising popularity in skincare products.
    • Greater retail availability.

    Interestingly, this market expansion occurs alongside rising olive oil prices, mainly due to a notable decrease in production. Eight European Union countries, which are the main producers, saw a dramatic drop in output from an average of 2.17 million tons to just 1.50 million tons in 2022—a 30.88% decline. Unfortunately, this drop in production comes as no surprise.

    Erratic weather patterns, rising temperatures, and exacerbating drought conditions in the Mediterranean basin have taken their toll. These climate changes disrupt the growing cycles of olive trees, leading to poorer crop yields and lower-quality olives.

    In the US, where olive oil constitutes 19% of all cooking oils sold and 40% of sales value due to its premium pricing, the market is expected to grow at an impressive CAGR of 11.31% between 2024 and 2032. This forecast is significant despite a recent dip in domestic consumption, which may further decline due to economic pressures. As a result, consumers must make difficult choices as they battle inflation, shrinkflation, and thin budgets.

    DataWeave’s Analysis of Rising Olive Oil Prices

    At DataWeave, we utilized our advanced AI-powered data aggregation and analysis platform to scrutinize the pricing trends of olive oils across key US retailers over the past year. Our analysis covered 130+ SKUs from major chains including Walmart, Kroger, Giant Eagle, and Target.

    The data revealed a notable escalation in olive oil prices, with consumers facing a sharp 25.8% increase from April 2023 to April 2024.

    This trend of rising costs was consistent across all analyzed retailers. Specifically, Walmart and Giant Eagle each reported a substantial 30% increase in their olive oil prices over the past year. In contrast, Target and Kroger experienced somewhat more modest hikes, at 20% and 15% respectively.

    Further investigation into individual brands within our sample highlighted that no brand is immune to the impacts of the ongoing supply shortages. Walmart’s own Great Value brand saw an exceptional 60% surge in prices. Other prominent olive oil brands such as Carapelli, Terra Delysia, and Bertolli also faced significant price increases, ranging from 20% to 50%.

    This across-the-board rise in prices underscores the widespread effect of supply constraints on the olive oil market, affecting both premium and private label brands alike.

    What Strategies Can Retailers and Brands Employ?

    In a market where consumer preferences and price sensitivities are rapidly evolving, retailers and brands must adopt versatile strategies without compromising on profit margins.

    Diversifying Brand Selection

    Retailers can enhance their appeal by offering a diverse range of olive oil brands, thereby stimulating competition among brands based on price, quality, innovation, and customer satisfaction. A well-curated selection that includes well-known brands like Filippo Berio and Bertolli, alongside emerging labels such as Terra Delyss, and premium options like Carapelli, allows retailers to meet a wide array of consumer preferences and budgets.

    For premium outlets, it might be beneficial to introduce more economical options than typically offered to attract budget-conscious consumers. Employing advanced assortment intelligence tools can provide retailers with crucial data, helping them make informed decisions about which brands to stock and promote, ensuring they meet consumer demand effectively while managing inventory costs.

    Data-driven Pricing

    With rising olive oil prices, competitive pricing is more crucial than ever. Retailers must strive to balance competitiveness with margin preservation. It’s essential for retailers to not just passively respond to market price increases but to actively ensure that their offerings are competitively priced relative to the market.

    This involves using sophisticated pricing intelligence tools, such as those provided by DataWeave, which track market trends and competitor pricing actions. These tools enable retailers to implement dynamic pricing strategies that respond promptly to market conditions and consumer demand shifts, helping to optimize sales and profitability.

    Diversifying Sourcing

    The traditional powerhouses of olive oil production, Spain and Italy, are now facing stiff competition from countries like Turkey and Tunisia. This shift is influenced by various factors, including currency fluctuations and changing trade policies, such as the imposition of tariffs on European olive oils by significant importers like the US. Retailers can take advantage of these changes by diversifying their sourcing strategies to include olive oil from non-traditional regions.

    The 2022/2023 season saw remarkable production levels from countries outside the Mediterranean basin, with Iran and China setting new production records. By broadening their supply chains to incorporate these emerging markets, retailers can benefit from lower production costs and introduce unique products to their consumers, enhancing both competitiveness and profit margins.

    Double Down on Private Labels

    Large retailers have successfully used their scale to develop strong private-label brands that can buffer consumers from price hikes in the olive oil market. By focusing on expanding and promoting their private-label offerings, retailers can provide cost-effective alternatives to national brands.

    Private labels generally have lower price points, making them particularly attractive during times of economic pressure and market volatility. Additionally, the development of private labels allows retailers to control more of their supply chain, from pricing to packaging, enabling them to offer high-quality products at competitive prices, thereby retaining customer loyalty and enhancing market share.

    Navigating Market Pressures

    High olive oil prices impact the entire supply chain, presenting varied challenges and opportunities:

    • Producers benefit from higher revenues but face increased pressure to maintain quality and yields in challenging climates. Adapting to these conditions with sustainable practices is crucial.
    • Exporters and Importers navigate tighter margins and greater risks due to tariffs and volume restrictions, requiring agility and strategic planning to adapt to market changes.
    • Retailers must carefully balance competitive pricing with rising procurement costs, affecting consumer affordability and potentially leading to shifts in buying patterns.
    • Consumers may seek cheaper alternatives or reduce their olive oil consumption, which influences overall market demand and pricing stability.

    These dynamics underscore the necessity for retailers and brands to adopt innovative and proactive strategies to navigate the volatile olive oil market effectively. By focusing on adaptive pricing, diversified sourcing, and customer engagement, businesses can enhance their resilience and secure long-term success in this competitive landscape.

    To learn more, talk to us today!

  • Using Siamese Networks to Power Accurate Product Matching in eCommerce

    Using Siamese Networks to Power Accurate Product Matching in eCommerce

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

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

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

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

    Why Product Matching is Incredibly Hard

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

    Data Complexity

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

    Data Variance

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

    Industry Specific Nuances

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

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

    Diverse Downstream Use Cases

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

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

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

    Siamese Networks for Product Matching

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

    The Tech Powering Siamese Networks

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

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

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

    How Siamese Networks Work?

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

    How it works:

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

    Architecture

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

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

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

    How DataWeave Uses Siamese Networks for Product Matching

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

    Pre-processing (Image Preparation)

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

    Training The AI

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

    Image Retrieval 

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

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

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

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

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

    Leverage Our AI-Driven Product Matching To Get Insightful Data

    In summary, accurate and efficient product matching is no longer a luxury – it’s a necessity. DataWeave’s advanced product matching solution provides brands and retailers with the tools they need to navigate this complex landscape, turning the challenge of product matching into a competitive advantage.

    By leveraging cutting-edge technology and simplifying it for practical use, we empower businesses to make informed decisions, optimize their operations, and stay ahead in the ever-evolving eCommerce market. To learn more, reach out to us today!

  • Why Strategic Competitive Insights Are Key to Optimizing Your Product Assortment

    Why Strategic Competitive Insights Are Key to Optimizing Your Product Assortment

    For retailers, the breadth and relevance of their product assortment are critical for success. Amid a crowded market filled with countless products clamoring for consumer attention, retailers must find innovative ways to distinguish themselves. While pricing undeniably impacts purchasing decisions, the diversity and distinctiveness of a retailer’s product range can provide a crucial competitive advantage.

    Creating an attractive and profitable assortment that resonates with your target audience requires more than intuition; it demands deep insights into both your own and your competitors’ offerings. A well-curated assortment aligned with current trends can drive higher conversions and foster customer loyalty. However, achieving this perfect balance is a formidable challenge without the right insights.

    This is where a data-driven strategy becomes essential, enabling you to curate a product mix that captivates and converts.

    However, retailers often encounter significant challenges when attempting to strategically plan their assortments:

    • Limited Competitive Insights: Gaining a clear understanding of your competitors’ assortment strengths and weaknesses across various categories is challenging. Without this visibility, it’s difficult to know where you have an edge or where you might be falling behind.
    • Tracking Demand Patterns: Identifying top-sellers and monitoring shifts in consumer demand can be a struggle. Without the ability to easily detect trends or changes in demand, you risk missing opportunities to stock trending items.

    Attempting to navigate these challenges manually is not only arduous but also susceptible to substantial errors.

    How Assortment Analytics Solutions Help

    The ideal Assortment Analytics solution must offer a fact-based approach to:

    • Identify Strengths and Weaknesses: Understand how your assortment measures up against the competition.
    • Stay Trend-Responsive: Keep your product mix fresh and aligned with the latest consumer trends.
    • Boost Conversions: Create a relatively unique, customer-focused assortment that enhances conversions.

    Many retailers attempt to analyze competitor assortments using manual, in-house methods, which inevitably leads to significant blind spots:

    • Variations in product classifications and taxonomies across competitors make meaningful comparisons challenging.
    • Gathering complete and accurate data across a vast competitive landscape is difficult.
    • Inconsistent titles and descriptions hinder reliable product matching without AI assistance.
    • Capturing and comparing detailed product attributes efficiently is nearly impossible without advanced tools.

    To overcome these challenges, retailers need a scalable, accurate Assortment Analysis solution designed specifically for the complexities of modern retail needs.

    DataWeave’s Assortment Analytics Solution

    DataWeave addresses these challenges by providing retailers with a robust platform to gain actionable insights into their product assortments and the competitive landscape. Leveraging advanced analytics and AI-driven algorithms, Assortment Analytics empowers retailers to make informed assortment management decisions, optimize their product offerings, and stay competitive.

    Armed with our insights, retailers can lead with their strengths and stock unique and in-demand products in their assortment. Further, by recognizing the strengths in their product catalog, they can craft effective pricing strategies and optimize their logistics, creating a more competitive and appealing shopping experience for their customers. Here are a few capabilities of DataWeave’s solution:

    In-Depth Competitive Analysis Across Retailers

    The solution offers detailed competitive analysis, revealing insights into competitors’ assortments. It maps competitor products to a common taxonomy, making comparisons accurate and meaningful. Retailers can visualize assortments at granular levels like category, sub-category, and product type.

    The data for these insights is collected at configurable intervals, typically monthly or quarterly, and is consumed not only via dashboard summaries but also raw data files to enable in-depth analysis. Retailers have the flexibility to choose specific competitors, brands, products, and categories for tracking, allowing for a tailored and strategic approach to assortment optimization.

    Brand and Category Views to Assess Your Portfolio

    The solution provides a comprehensive evaluation of your product assortment through brand and category views. In brand views, your portfolio is assessed against competitors at the brand level, highlighting:

    • Newly Introduced Brands: Insights into recently introduced brands, revealing shifts in the brand landscape.
    • Absence or Limited Presence: Identification of brands lacking representation or with minimal presence compared to competitors, indicating areas for improvement.
    • Strong Presence and Exclusivity: Recognition of brands where you excel, including exclusive offerings, showcasing your competitive edge.

    Identifying Top-Selling Competitive Products To Boost Assortment Strategy

    Beyond just comparing assortment numbers, the DataWeave solution surfaces insights into which competitor products are actually performing well. It equips category and assortment managers with indicators that assess competitor products in terms of their popularity and shelf velocity.

    It analyzes metrics like pricing fluctuations, ratings, customer reviews, search rankings, and replenishment rates to pinpoint hot sellers you may want to stock. With these insights, merchandizing managers can pinpoint top-selling products among competitors, enabling informed decisions to enhance their assortment in comparison.

    Sophisticated Attribute Tagging and Analysis

    Using AI-powered attribute tagging, the solution simplifies granular product analysis within specific categories. An Apparel retailer, for instance, can filter the data to compare assortments based on attributes like material, pattern, color, etc.

    Retailers can select attributes relevant to their products and gain detailed insights. These custom filter attributes dynamically populate the panel, facilitating targeted data exploration. Category and merchandizing managers can delve into critical details swiftly, enabling strategic decision-making and comprehensive competitive analysis within their categories.

    You can capitalize on opportunities by stocking in-demand, on-trend items and address assortment gaps quickly. At the same time, you can double down on your strengths by enhancing your exclusive or top-performing product sets.

    In summary, DataWeave’s Assortment Analytics solution provides an invaluable competitive edge. The insights enable evidence-based decisions to attract more customers, encourage bigger baskets, and maximize the value of every assortment choice.

    To learn more, read our detailed product guide here or get on a exploratory call with one of our experts today!

  • 5 Must-Have Capabilities of Your Ideal Competitive Pricing Intelligence Solution

    5 Must-Have Capabilities of Your Ideal Competitive Pricing Intelligence Solution

    In the cutthroat world of retail, where razor-thin margins and fierce competition reign supreme, pricing becomes your secret weapon to driving sales. The magic bullet unlocks sales, attracts customers, and ultimately fuels your bottom line. But with ever-changing market trends and competitor tactics shifting constantly, effective pricing strategies become even more crucial.

    A recent Bain & Company study highlights this very point. 78% of respondents acknowledged that their pricing decisions could be improved, leaving significant revenue untapped. John Furner, President and CEO of Kroger, echoes this sentiment. In a press release announcing a new pricing strategy, he emphasized their commitment to “providing our customers with predictable, affordable prices on the products they need most.” This focus on transparent and consistent pricing reflects the growing importance of building trust with customers, where value goes beyond just the lowest price tag.

    The right pricing strategy can unlock a treasure trove of benefits for retailers, including attracting new customers, boosting sales, and ultimately increasing their bottom line.

    But here’s the challenge: keeping pace with market trends and competitor strategies requires constant vigilance. This is where an advanced, user-centric pricing intelligence tool comes into play. Retailers need a platform specifically designed to address their unique challenges. It should empower them to protect margins, create a seamless pricing process, and attract and retain price-sensitive customers. To help you navigate this landscape, we’ve identified the must-have capabilities of a pricing intelligence solution that will transform your pricing strategy and propel your business toward long-term success.

    1. Reliable and Accurate Data Collection

    Retailers need a competitive intelligence solution that goes beyond merely capturing information en masse from competitor sites. An ideal solution ensures that data is consistent, extensive, and highly accurate, with an added level of granularity. This is achieved through statistical process control methods for data quality assurance, enabling highly accurate data capture and processing.

    Such a platform should be capable of scraping data from various sources, including desktop sites, mobile sites, and mobile applications, as well as a variety of online platforms: aggregators, omnichannel retailers, delivery intermediaries, quick commerce platforms, D2C sites, and more. This versatility ensures that data is captured across any global region and in dozens of languages, making the system geography and language agnostic.

    DataWeave’s solution includes a fast and automated data source configuration system, enabling a swift setup of new web sources for data capture. This capability ensures that retailers can stay ahead of the curve as the market landscape and competitor strategies evolve.

    An effective competitive pricing intelligence solution allows retailers to move away from working with incomplete or inaccurate data and instead leverage a comprehensive information stream to create strategic pricing decisions and optimize their overall business strategy. At the end of the day, the insights you base your decisions on are only as good as the data you aggregate. Even with the world’s best analytics engine, it’s always a case of “garbage in, garbage out.”

    2. Hyperlocal Insights From Store-Level Data

    Monitoring pricing and availability across specific stores is crucial for retailers to gain critical insights into a vast network of locations, enabling them to make strategic decisions that enhance pricing strategies and supply chain effectiveness, thereby minimizing stockouts or pricing inefficiencies in key markets. A platform like DataWeave provides retailers with a comprehensive view of store-level data across ZIP codes, maintaining a hyperlocal competitive strategy. It offers detailed visibility into product availability, highlighting out-of-stock scenarios across different competitors. This capability is invaluable, allowing quick identification of price improvement opportunities and providing retailers with a bird’s eye view of where products can be priced higher than usual to gain margins.

    The system operates at configurable intervals—daily, weekly, or monthly—enabling retailers to keep a vigilant eye on pricing, product availability, and delivery timelines based on the selected fulfillment option. Unlike many other providers who offer limited insights from a sample of stores, this solution delivers exhaustive analytics from every storefront. This comprehensive approach grants retailers (and brands) a strategic edge, facilitating efficient inventory tracking, precise pricing adjustments, and rapid responses to fluctuating market dynamics.

    3. Sophisticated, AI-Powered Product Matching

    A solution that matches products accurately at scale is essential for a robust and reliable competitive pricing strategy. Advanced platforms use unified systems for both text and image recognition to accurately match similar SKUs across thousands of eCommerce stores and millions of products. Deep learning architecture is employed to develop unique AI that matches text and images, grouping similar products based on their features, ensuring accurate matches even for private label products.

    This AI identifies critical elements of products in images, such as focusing on the top half of a model wearing a shirt, the sleeve length, the color of the product, etc.. Deep learning models, trained on extensive datasets of images, enhance these images by removing irrelevant background details and improving the quality of the core product image. Innovative AI then extracts unique signatures from the photos, allowing for quick and efficient identification and grouping of products across billions of indexed items.

    No matter how powerful the AI, combining it with human expertise is key to achieving true data veracity—ensuring accuracy, freshness, and comprehensive coverage required for reliable product matching. A human-in-the-loop approach elevates the AI-powered product matching process by addressing key challenges. AI algorithms may initially identify product matches with 80-90% accuracy, but human validation corrects errors, pushing accuracy closer to 100%. Humans apply contextual judgment for subjective criteria like aesthetics and design, making nuanced decisions that quantitative rules might miss. Continuous learning through an iterative feedback loop allows AI models to quickly adapt to changing trends and preferences as human experts provide context and re-label incorrect predictions. By integrating AI’s automation and scale with human validation, judgment, and knowledge curation, pricing intelligence solutions can achieve the accuracy and coverage necessary for actionable competitive pricing insights.

    This approach results in retailers being able to match products and compare prices between identical products, similar products, and private label brands.

    4. Unit of Measure Normalization

    Effective product matching and grouping are crucial for maintaining competitive pricing, but this requires a tech stack that can normalize units of measure across various sites. For example, a 10.75oz can of chicken noodle soup priced at $3 may seem cheaper than a 12.90oz can priced at $3.20, but this isn’t always the case. Initially, the larger package might appear more expensive, but when prices are compared based on the same unit amount, it often offers better value. Therefore, it is essential for retailers to standardize units to accurately compare prices. Advanced technology goes beyond simply matching products; it ensures accurate comparisons by normalizing unit measurements, including weight, quantity, and volume—crucial factors for establishing a clear pricing picture across competitors.

    Imagine comparing soup prices regardless of whether they are advertised in ounces, milliliters, or liters. By normalizing unit measurements, retailers can develop tailored pricing strategies on a level playing field, eliminating the risk of being misled by seemingly lower prices that conceal smaller quantities. Unit normalization allows retailers to uncover hidden value propositions by accurately determining the cost per unit, enabling them to set competitive prices, highlight the true value of their products, and make data-driven decisions.

    5. Ease of Actionability

    The most valuable insights are ineffective if they cannot be easily accessed and acted upon. Imagine a solution that not only provides industry insights but also customizes alerts and dashboards to show exactly how your prices compare to competitors in your specific categories and product groupings. An ideal solution would offer all this in one centralized platform, giving retailers easy access to data through intuitive dashboards, seamless data export options, and flexible API integrations. This enables a smooth, effortless process for adopting and utilizing the platform.

    Ease of use and actionable insights should be at the core of such a solution. A SaaS-based web portal can provide businesses with access to insights through user-friendly dashboards, detailed reports, and impactful visualizations. Customized insights tailored for each persona within the organization facilitate swift actions on relevant competitive intelligence. Whether it’s day-to-day tactical recommendations or inputs for long-term strategies, the platform should ensure that all insights are easily consumable and actionable.

    Moreover, the data should be accessible using plug-and-play APIs, enabling businesses to integrate external data with their internal pricing or ERP systems and BI tools. This integration generates predictive intelligence, enhances decision-making, and drives more robust business outcomes.

    Choosing the Right Pricing Intelligence Solution Will Determine Your Success

    Retailers need to leave behind generic pricing intelligence tools. For true success, retailers need a solution built to tackle their specific challenges. With capabilities like comprehensive data collection capturing granular details across regions and languages, local insights into store-level data for informed decision-making, accurate price comparisons with unit normalization, and access to actionable insights, retailers gain a complete and holistic picture of the pricing landscape, setting them up for success. Additionally, AI-powered and human-aided product matching ensures accurate competitor analysis

    These are just some of the essential capabilities DataWeave offers to retailers. By prioritizing these, retailers can transform their pricing strategy into a profit-generating machine, keeping them ahead of the curve and exceeding customer expectations in a competitive market to help them stay at the forefront of their categories.

    To learn more, talk to us today!

  • 6 Common Pricing Intelligence Challenges Retailers Face (And How to Overcome Them)

    6 Common Pricing Intelligence Challenges Retailers Face (And How to Overcome Them)

    When your product pricing is sub-optimal, you leave money on the table. This is especially significant for eCommerce retailers who must contend with their consumers ‘shopping around’ for the best price before making a purchase. All eCommerce retailers experience some amount of cart abandonment. In fact, the average cart abandonment rate is estimated at 70.19%, and the reason is often that customers find a better price elsewhere, whether at other online stores or in traditional brick-and-mortar ones.

    If you want to win the business of price-sensitive shoppers, you need a robust pricing strategy to keep up with changing competitor pricing. That’s one reason (among others) that retailers rely heavily on pricing intelligence solutions. With the right pricing intelligence solution, retailers can stay on top of market shifts, manage profit margins, maintain price perception, and of course, price their products competitively.

    Unfortunately, adding a new pricing intelligence solution to a retailer’s tech stack is not without its challenges. But the good news is there are ways to overcome them.

    In this post, we’ve rounded up six challenges most commonly cited by retailers and proposed strategies to overcome them. So if you’re considering a pricing intelligence solution that can get you closer to your business goals, read on to learn more.

    1. Scalability Constraints

    As access to the internet has expanded globally, the ratio of brick-and-mortar sales compared with eCommerce continues to narrow. A natural consequence of this is that more shoppers than ever before now browse and buy across diverse web environments, including mobile apps.

    This means that retailers need to track pricing across not just websites and physical stores, but also across mobile apps — a sales channel that was largely sidelined before.

    Modern pricing intelligence solutions need to consolidate data from:

    • Online storefronts
    • Mobile apps


    … and also from delivery channels, which often have different assortments and pricing:

    • Standard home delivery
    • Expedited, same-day home delivery
    • Buy online, pickup in-store (BOPIS)
    • Subscription
    • Curbside pickup


    In this context, imagine having to track the pricing of millions of SKUs compared against dozens of competitors each day. When new channels and devices are added, many pricing intelligence solutions in the market are unable to handle such data complexity and scale. They’re not built to continually grow and expand to meet changing needs. Even worse, some retailers opt for homegrown DIY systems, which struggle to keep the datasets updated, accurate, and current—activities that require significant cost and human effort.

    How DataWeave Bridges This Gap:

    What you need is a platform that can track millions of SKUs across dozens of competitors and geographies. No matter where the data is coming from or how vast the demand for the product is, an ideal solution should be able to synthesize huge amounts of complex data and generate meaningful insights.

    Your competitors are continually changing their eCommerce setup, whether through subtle changes to their product attribute listings or broader changes to domains or apps. With DataWeave’s pricing intelligence solution designed to scale up as required, you never need to worry about the backend flexing to accommodate changes.

    2. Inability to Match Products Without Clear UPC/EAN Identifiers

    Another problem with many pricing intelligence solutions is their inability to match products if a UPC/EAN identifier is missing. Often, a competitor will list an identical product on their storefront and omit any clear identifiers. On Amazon, an ASIN might be listed or you might be able to bring in a DPCI from Target.com. However, without clear identifiers across eCommerce platforms, retailers struggle to aggregate every instance of the products, and as a result, are unable to achieve accurate pricing comparisons. They often face this challenge when they work with commoditized web scraping service providers who have very limited expertise or experience in refining the data into meaningful insights.

    How DataWeave Bridges This Gap:

    If you can’t match UPC/EAN codes, you need a solution that leverages artificial intelligence to match products based on other variables, such as product titles, descriptions, and images. AI, in combination with human expertise, can take on the task at a speed and accuracy that would be unfeasible for humans alone.

    Artificial Intelligence is constantly learning and improving. At DataWeave, we accelerate this process by introducing new scenarios and datasets for the product to continually learn from. At the outset, our AI product matching is roughly 80-90% accurate every time. To improve this number to over 95%, we introduce human validation and nuanced judgments. Over the years, this feedback loop has continued to refine its algorithms, resulting in near-perfect data accuracy for retailers.

    Our solution uses AI built on more than ten years of data to perform robust product matching for retailers at a massive scale. Using a unified platform with text and image recognition, DataWeave matches products from among hundreds of eCommerce websites and across millions of products.

    3. Poor and Inconsistent Data

    Retailers often complain that the data within their pricing intelligence solution isn’t accurate, is inconsistent, and may even be comprised of statistical smoothing and gap-plugging smokescreens. The root of this problem often lies in the inability of these tools to consistently track prices across diverse web environments. Poorly designed web scraping infrastructure fail when eCommerce websites change their underlying configuration and structure (which happens periodically). As a result, they don’t have enough data to see the market as a whole, and end up viewing synthetic or small sample-set data.

    How DataWeave Bridges This Gap:

    At DataWeave, transparency drives our approach to delivering insights. We only present real-world data in our data feeds and dashboards to customers. This is possible only due to the supreme confidence we have in our ability to consistently capture and present accurate data. We achieve this by using a combination of AI and sophisticated web scraping infrastructure developed and enhanced over a decade.

    In fact, we are the first in the industry to launch a Data Statistics Dashboard that helps our customers scrutinize match rates, track data freshness, highlight any gaps in the data, and manage product matches independently.

    4. Limited Integration Options with Internal Systems

    Too often, a retailer will select a pricing intelligence solution that promises exceptional insights but then fails to offer a manageable workflow for day-to-day use. This usually happens because it doesn’t integrate with the retailer’s existing tech stack.

    Without a convenient process that connects internal systems, your pricing intelligence solution is just another piece of technology that your team does not use to its full potential. You may require your competitor pricing data to flow into price optimization tools, price management tools, BI tools, ERP systems, or revenue management systems. Without this capability, you’ll see limited ROI and underwhelming results because all the insights in the world are of little use if you can’t consume them easily and put them into action.

    How DataWeave Bridges This Gap:

    At DataWeave, we understand the importance of being able to integrate external data with your internal tech stack. Our data can be accessed and extracted using plug-and-play APIs, enabling businesses to combine their external and internal data to generate predictive intelligence.

    We also have other data feeds ready to be integrated, including FTP and Amazon S3. Our integration experts can work with you to create custom integrations to existing internal pricing platforms. Our ultimate goal here is to seamlessly elevate your pricing intelligence strategy with minimal change management.

    5. Limited Custom Analysis Capabilities

    Every retailer is unique. There are various geographies, languages, markets, product categories, and pricing strategies that differentiate one retailer from the next. Many retailers find it challenging to derive actionable insights from their pricing intelligence solution because the analysis and customization capabilities are too limited.

    For example, some retailers might want to evaluate their competitiveness after applying coupons and promos to selling prices. Others may want to perform a one-time pricing analysis of just list prices across competitors. Some may want to view insights that help them take tactical decisions day-to-day, while others would like a historical view across multiple dimensions to help make strategic long-term pricing decisions.

    Without the ability to customize their views or the underlying data, retailers could feel restricted in their ability to drive meaningful impact with their pricing intelligence.

    How DataWeave Bridges This Gap:

    What you need are foundational dashboards, reports, and visualizations in a web portal that can be tailored to your business needs. Then, you need the expertise and guidance of a team of business analysts who can help you configure custom reports and dashboards.

    At DataWeave, we offer bespoke insights for each persona, enabling swift actions on relevant competitive intelligence. These include day-to-day tactical recommendations or inputs for long-term strategies. And because all DataWeave customers get access to our team of expert analysts, it’s simple and straightforward to configure unique reports and dashboards to suit your business.

    6. Sloppy Support

    No solution, at least not one that undertakes complex work, works optimally with a ‘set it and forget it’ approach. From time-to-time, you need human intervention to ensure your pricing intelligence is working in the way that it needs to for you. Unfortunately, one of the most common challenges retailers face with their pricing intelligence tool is a lack of support.

    Unavailable or patchy customer support is a significant challenge that can result in low confidence, delayed resolutions, and even abandoned pricing actions.

    How DataWeave Bridges This Gap:

    Dataweave’s global team of pricing experts are available around the clock for support and guidance. Not only do we have tech experts and business analysts that you can consult at any point, we also have an exceptional team of customer success professionals to help you overcome any technical and strategic issue you might face.

    As one customer puts it, with DataWeave you gain access to: “Excellent customer service, super collaborative staff, user-friendly interface.”

    Another verified user from the consumer goods industry had this to say:

    “Great platform and customer service! Our client service team is very helpful and always responds to ad-hoc requests in a very timely matter!”

    Read more reviews from real DataWeave users on G2: https://www.g2.com/products/dataweave/reviews

    Finding The Right Pricing Intelligence Solution

    As the competition heats up, retailers need to unlock every available opportunity to gain an edge and capture market share. At DataWeave, our AI-powered pricing intelligence software helps you uncover gaps quickly and build a pricing strategy that is as attractive as it is effective. Our ability to scale, match your products across the entire ecosystem with consistent accuracy, and slide right into your current operations to provide advanced analytics, makes us the preferred choice for many of the world’s leading retailers.

    Want to start benefiting from actionable product matching and pricing intelligence? Request a demo today.

  • Cinco de Mayo 2024 Pricing Insights: An Analysis of Discounts Amid Inflation

    Cinco de Mayo 2024 Pricing Insights: An Analysis of Discounts Amid Inflation

    Cinco de Mayo is a vibrant celebration of Mexican-American and Hispanic heritage, marked by lively parades, festive tacos, and refreshing tequila across North America. For the service industry, brands, and retailers, this day offers a golden opportunity to roll out enticing promotions on beloved Mexican foods and beverages, drawing in large crowds and boosting sales.

    Americans love to indulge in Mexican cuisine during Cinco de Mayo. Take avocados, for example: despite inflation, avocado sales soared to 52.3 million units this year, marking a 25% increase from last year, according to the Hass Avocado Board’s 2023 Holiday Report. Such festive events see a significant sales spike, largely driven by appealing discounts and special offers.

    So, what discounts did retailers roll out this Cinco de Mayo?

    At DataWeave, our cutting-edge data aggregation and analysis platform tracked and analyzed the prices and deals on Mexican food and alcohol products offered by leading retailers. Our in-depth analysis sheds light on their pricing competitiveness during Cinco de Mayo, revealing how pricing strategies differed across various subcategories and brands.

    We conducted a similar analysis in 2022, allowing us to compare the prices of identical products this year versus last year. This comparison helps us understand the impact of inflation over the past two years on the prices offered today.

    Our Methodology

    For our analysis, we monitored the average discounts offered by major US retailers on over 2,000 food and beverage products during Cinco de Mayo, as well as in the days leading up to the event. Many retailers kick off their Cinco de Mayo promotions a week before, so we included the entire week leading up to May 5th in our analysis.

    Key Details:

    • Number of SKUs: 2000+
    • Retailers Analyzed: Target, Amazon Fresh, Safeway, Walmart, Total Wines & More, Sam’s Club, Meijer, Kroger
    • Categories: Food, Alcohol
    • Analysis Period: April 28 – May 5

    To truly demonstrate the value of Cinco de Mayo for shoppers, we concentrated on price reductions and additional discounts during the event. By comparing these with regular day discounts, we were able to highlight the genuine savings and benefits that Cinco de Mayo promotions offer to budget-conscious consumers.

    Our Findings

    Safeway led the pack with the highest average additional discount of 4.91%, covering 38.6% of their food inventory for Cinco de Mayo. Total Wine & More followed closely, offering an average discount of 3.46% across 70.8% of its tequila, whiskey, mezcal, and other spirit products during the Cinco de Mayo week.

    In contrast, Target provided minimal additional discounts, averaging just 0.8% over a small fraction (11.6%) of its SKUs. Similarly, Kroger’s additional discounts were also 0.8%, but they were spread across over 60% of its tracked products. Walmart (1.4%) and Amazon Fresh (1.2%) offered relatively conservative discounts during the sale period.

    During Cinco de Mayo, various brands rolled out attractive discounts to entice shoppers. Among beverage brands, The American Plains vodka led the way with the highest average discount of 20.80%. Coffee brands also joined the festivities with significant discounts: Death Wish Coffee at 14.30%, Dunkin’ at 11.10%, and Starbucks at 5.70%. Notably, Dunkin’ and Death Wish Coffee introduced complimentary beverages such as whiskey barrel-aged coffee and spiked coffee products to celebrate the event.

    In the wine category, Erath stood out with a 10% additional discount. However, brands like Jose Cuervo and Franzia offered more modest discounts of 0.70% and 1.80%, respectively.

    Food brands associated with traditional Mexican ingredients or products, such as tortillas, salsas, and spices, provided higher discounts compared to mainstream snack brands. For instance, McCormick (25%), El Monterey (13.3%), and La Tortilla Factory (16.7%)—known for ready-to-eat frozen foods, seasonings, and condiments—delivered the highest discounts. Other notable discounts included Jose Ole (12.5%), a frozen food brand, and Yucatan (8.3%), known for its guacamole.

    Safeway’s private label brand, Signature Select, offered a 5.20% discount. Additionally, Safeway provided deep discounts on brands like Pace, Herdez, and Taco Bell, indicating an aggressive discounting strategy. In contrast, brands closely associated with Mexican or Tex-Mex cuisine, such as Old El Paso, Mission, Rosarita, and La Banderita, offered relatively modest discounts ranging from 0.5% to 3.3%.

    The discount patterns varied between alcohol and food categories, with food brands generally offering higher discounts. This trend may be attributed to pricing being regulated in the alcohol industry. These differing discount levels highlight how brands navigated the balance between driving sales and maintaining profit margins during Cinco de Mayo, particularly in the context of inflation affecting costs.

    Impact of Inflation on Cinco de Mayo Prices (2024 vs 2022)

    To gauge the impact of inflation on popular Cinco de Mayo products, we analyzed the average prices at Walmart and Target between 2022 and 2024. These two retailers were chosen due to their prominence in the retail sector and the robustness of our sample data.

    At Walmart, the Tex Mex category saw the highest average price increase, rising by 22.51%. Other notable subcategories with significant price hikes include Condiments (23.21%), Vegetables/Packaged Vegetables (21.22%), and Lasagne (14.10%). Categories like Dips & Spreads (13.77%), Pantry Staples (14.92%), and Salsa & Dips (8.23%) experienced relatively lower increases.

    At Target, the Snacks subcategory had the steepest average price rise at 27.94%, followed by Meal Essentials (16.07%) and Deli Pre-Pack (8.82%). Categories such as Dairy (0.51%), Frozen Meals/Sides (7.11%), and Adult Beverages (7.41%) saw smaller price increases.

    Brands associated with traditional Mexican or Tex-Mex cuisine faced higher price hikes. Examples include Old El Paso (24.59% at Walmart, 8.70% at Target), Tostitos (35.44% at Walmart, 11.41% at Target), Ortega (30.59% at Walmart, 19.69% at Target), and Rosarita (14.39% at Walmart).

    In contrast, private label or store brands generally experienced lower price increases compared to national brands. For instance, Good & Gather (Target’s private label) saw a 9.55% increase, while Market Pantry (Walmart’s private label) had a 17.27% rise. This trend is understandable as retailers have more control over their costs with private label brands.

    The data clearly indicates that both Walmart and Target have significantly raised prices across various categories and brands, reflecting the broader inflationary environment where the cost of goods and services has been steadily climbing.

    Interestingly, we observed higher price increases at Walmart compared to Target. Although Walmart is renowned for its consumer-friendly pricing strategies, it too had to elevate grocery prices post-2022 to combat inflationary pressures. As consumers become more cost-conscious and reduce spending on discretionary items, Walmart and other retailers are now cutting prices across categories to align with shifting consumer behaviors.

    Mastering Pricing Strategies During Sale Events

    Our pricing analysis for Cinco de Mayo reveals compelling insights into the dynamics of retailer landscapes in the US. It highlights the enduring relevance of private label brands, even amidst fluctuating demand, showing the emergence of local, national, and small players vying for market share.

    As retailers navigate inflationary pressures and evolving consumer behaviors, understanding these pricing dynamics becomes crucial for optimizing strategies and bolstering market competitiveness. This analysis offers actionable intelligence for retailers seeking to navigate the intricate terrain of sale event promotions while addressing shifting consumer preferences and economic challenges.

    Access to reliable and timely pricing data equips retailers and brands with the tools needed to make informed decisions and drive profitable growth in an increasingly competitive environment. To learn more and gain guidance, reach out to us to speak to a DataWeave expert today!

  • How Monitoring and Analyzing  End-User Prices can Help Retailers and Brands Gain a Competitive Edge

    How Monitoring and Analyzing  End-User Prices can Help Retailers and Brands Gain a Competitive Edge

    Retailers and brands are constantly engaged in a fierce battle over prices and discounts. Whether it’s major events like Amazon Prime Day, brand-led sales, or everyday price wars, they depend on pricing intelligence and digital shelf analytics to fine-tune their strategies. With a variety of offers such as sales, promotions, and bundles, determining the actual cost to the customer becomes a complex task. The price set by the brand, the retailer’s offer, and the final amount paid by the customer often vary significantly.

    In their analysis, retailers and brands frequently focus on the listed price or the final sale price, overlooking a critical factor: the “end-user price.” This includes all discounts, taxes, and shipping costs, providing a more accurate picture of what customers are truly willing to pay at checkout.

    Grasping this end-user price is vital for both retailers and brands. For retailers, it helps them stay competitive and refine their promotional strategies. For brands, it offers insights into competitive positioning, net revenue management, and shaping customer price perception.

    However, emphasizing the end-user price is challenging, as it involves comprehending all the intricate elements of pricing.

    How end-user pricing is calculated

    The list price, also known as the manufacturer’s recommended retail price (MSRP), is the initial price set by the brand. This may not always be displayed on marketplaces, especially in categories like grocery. The selling price, on the other hand, is the amount at which a retailer offers the product, often reduced from the list price. The end-user price is the actual amount the customer pays at checkout, which includes taxes, promotions, and other factors that affect the final cost.

    The process involves 3 key stages:

    Step 1: Identifying and categorizing promotional offers

    The first critical step in calculating end-user pricing is to identify and categorize the various promotional offers available for a given product that can reduce the final amount paid by the consumer. These promotions span a wide range of types:

    • Bank Offers: Involving discounts or cash back incentives when paying with specific bank credit or debit cards. For instance, a customer may receive 10% cashback on their purchase by using a specific bank’s card.
    • Bundled Deals: Combining multiple products or services at a discounted bundle price. A common example is a smartphone bundle including the phone itself, a protective case, and earphones at a reduced total cost.
    • Promo Codes/Coupons: Customers can enter promo codes or coupons during checkout to unlock special discounted prices or percentage-off offers, like 20% off a hotel booking, or even a special brand discount personalized for their needs (think loyalty offers and in-app promotions).
    • Shipping Offers: These include free shipping or reduced shipping fees for certain products or orders, such as free delivery on orders above a set amount.
    • TPRs (Temporary Price Reductions): TPRs play a significant role in the strategies of most retailers. Brands and retailers use them to encourage shoppers to purchase more of a product or to try a new product they wouldn’t usually buy. A TPR involves reducing the price of a product by more than 5% from its regular shelf price.

    By accurately identifying and classifying each type of promotion available, brands can then calculate the potential end-user pricing points.

    Step 2: Accounting for location and fulfilment nuances (delivery, in-store pickup) that impact final pricing

    Product pricing and promotional offers can vary based on the consumer’s location or ZIP code. Additionally, customers may opt for different fulfilment modes like delivery, shipping, or in-store pickup, which can further impact the final cost. Accurately calculating the end-user price necessitates considering these location-based pricing nuances as well as the chosen fulfilment method.

    In the example below, the selling price is $4.32 for one retailer (on the left in the image) after a discount for online purchase. In another case with Meijer, the item total shows $17.91, but the consumer ends up paying $15.74 after taxes and fees are applied (on the right in the image).

    Step 3: Applying each eligible promotion or offer to the selling price to determine potential end-user price points

    With the various promotional offers and discounts categorized in the previous steps, retailers and brands can now apply each eligible promotion to the product’s selling price. This involves deducting percentages for bank cashback, implementing bundled pricing, applying coupon code discounts, and incorporating shipping promotions.

    For retailers, this step allows them to calculate their true effective selling price to customers after all discounts and promotions. They can then compare this end-user price against competitors to ensure they remain competitively priced.

    For brands, by systematically layering every applicable offer onto the baseline selling price, they can accurately calculate the multiple potential end-user price points a customer may pay at checkout for their products across different retailers and regions.

    Why the end-user price matters

    Optimizing pricing strategies using the end-user price can benefit retailers and brands in several ways:

    • Price Competitiveness: By monitoring end-user pricing, retailers can adjust for discounts and promotional offers to attract customers, while brands can refine their pricing models to stay ahead in the market.
    • Customer Acquisition and Loyalty: Offers, promotions, and discounts directly impact the final price paid by customers, playing a crucial role in attracting new customers and retaining existing ones. For example, Walmart’s competitive pricing in groceries boosts customer loyalty and repeat purchases.
    • Consumer Perception: End-user pricing significantly shapes how consumers perceive both retailers and brands. Competitive pricing and promotional transparency enhance reputation and conversion rates. Amazon, for instance, is known for its competitive pricing and fast deliveries, which strengthen its consumer perception and satisfaction.
    • Sales Volumes: The final checkout price influences affordability and perceived value, directly affecting sales volumes. Both retailers and brands benefit from understanding this, as it guides consumer purchasing decisions and drives revenue streams.
    • Brand Perception: Consistent and transparent pricing enhances the perception of both the retailer and the brand. This not only strengthens the value proposition but also builds consumer trust and fosters long-term loyalty.

    While the listed and selling prices are readily available, calculating the true end-user price is quite complex. It involves meticulous tracking and application of various types of promotions, offers, location-based pricing nuances, and fulfillment costs – an uphill task without robust technological solutions.

    Track and Analyze end-user prices with DataWeave

    DataWeave’s end-user price tracking capability empowers retailers and brands with the insights and tools necessary to comprehend the complexities of pricing dynamics. For retailers, it offers the ability to monitor end-user pricing across various products and categories compared to competitors, ensuring competitiveness after all discounts and enabling optimization of promotional strategies. Brands benefit from informed pricing decisions, optimized strategies across retail channels, and a strengthened position within their industries.

    Our intuitive dashboard presents classified promotions and corresponding end-user prices across retailers, providing both retailers and brands with a transparent, comprehensive view of the end-user pricing landscape.

    Within the detailed product view of DataWeave’s dashboard, the Price and Promotions panel showcases diverse promotions available across different retailers for each product, along with the potential end-user price post-promotions.

    Harness the power of DataWeave’s sophisticated Pricing Intelligence and Digital Shelf Analytics to gain an accurate, real-time understanding of your end-user pricing dynamics. Make data-driven pricing decisions that resonate with customers and propel your brand toward sustained success.

    Find out how DataWeave can empower your eCommerce pricing strategy – get in touch with us today or write to us at contact@dataweave.com!

  • Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    In today’s expansive omnichannel commerce landscape, pricing intelligence has become indispensable for retailers seeking to stay competitive and refine their pricing strategies. The sheer magnitude of eCommerce, spanning thousands of websites, billions of SKUs, and various form factors, adds layers of complexity. Consequently, ensuring the accuracy and reliability of competitive insights presents a formidable challenge for retailers aiming to leverage pricing data effectively.

    At the core of any robust pricing intelligence system lies product matching. This process enables retailers to recognize identical or similar products across competitors. Once these matches are identified, tracking prices is a relatively more straightforward task, facilitating ongoing analysis and informed decision-making.

    Accurate matching is crucial for meaningful price comparisons and tailoring product assortments. The challenge is matching products is often complicated, especially for non-local brands, niche categories, or items lacking consistent global identifiers. It becomes even trickier when trying to match very similar but not identical products. A comprehensive approach that compares and analyzes multiple attributes like product titles, descriptions, images and more is essential.

    Artificial intelligence algorithms are commonly used to automate product matching, leveraging machine learning techniques to analyze patterns in images and text data. While AI can adapt and improve over time, the question remains: Can it fully address the complexities of product matching on its own?

    The reality is that many retailers still struggle with incomplete, inaccurate, or outdated product data, despite these AI-powered product matching solutions. This can lead to suboptimal pricing decisions, missed opportunities, and reduced competitiveness.

    Challenges in an ‘AI-only’ Approach to Product Matching

    While AI plays a vital role in automated product matching solutions, there are complexities that AI alone cannot fully address:

    Subjectivity in Matching Criteria

    Some product categories have subjective or hard-to-quantify criteria for determining similarity. AI learns from historical data, so it may struggle with nuanced aspects like:

    Aesthetics, style, and design: In the Fashion and Jewellery vertical, for example, products are matched according to attributes like style, aesthetics, design – all of which have some subjectivity involved.

    Quantity/packaging variations: In the grocery sector, variations in product packaging and quantities can introduce complexities that require subjective decision-making. For example, apples may be sold in different packaging like a 0.5 kg bag or a pack of 4 individual apples. Determining if these different packaging options should be considered equivalent often involves making a qualitative judgment call, rather than a clear-cut objective decision.

    Matching product sets: For categories like home furnishings, the focus is often on matching coordinated sets rather than individual items. For example, in the bedroom category, matching may involve grouping together an entire set of complementary furniture like a bed frame, dresser, and wardrobe based on their cohesive design and style. This goes beyond simply making one-to-one product associations, requiring more nuanced judgments about aesthetic coordination.

    Contextual Factor

    Products can have regional preferences, cultural differences, or evolving trends that impact how they are matched. AI may miss important context like Local/regional product names or distinct brand names across countries.

    For instance, in the image we see Sprite (in the US) is branded Xubei in China. Continuous human curation is needed to help AI adapt to this context.

    High Accuracy & Coverage Expectations

    Retailers rely on AI powered and automated pricing adjustments based on product matching for insight. To ensure that pricing recommendations and updates are accurate, accurate product matching is crucial. For this, simply identifying similar top results is not enough – the process must comprehensively capture all relevant matches. While AI excels at finding the top groupings with around 80% accuracy, even small matching errors can have significant consequences.

    As AI matching improves, customer expectations may rise even higher. If AI achieves 90% accuracy, for instance, SLAs may demand over 95%. Reaching such a high level of accuracy is very challenging for AI alone, especially when faced with incomplete data, contextual nuances, evolving trends, and subjective matching criteria across products and categories.

    The solution is to combine the power of AI with human expertise. This is the key to achieving true data veracity – the accuracy, freshness, and comprehensive coverage required for precise and reliable product matching.

    Human-in-the-Loop Approach for Elevated Product Matching

    Human intelligence and quality testing can elevate the AI powered product matching process by addressing key challenges:

    • Matching Validation: AI algorithms may identify product matches with 80-90% accuracy initially. Having humans validate these AI-suggested matches allows for correcting errors and pushing the accuracy close to 100%. As humans flag issues, provide context, and re-label incorrect predictions, it allows the AI model to learn and enhance its reliability for complex, high-stakes decisions.
    • Applying Contextual Judgment: For subjective matching criteria like aesthetics, design, and categorizing product sets, human discernment is needed. Humans can make nuanced judgments beyond just quantitative rules, ensuring meaningful apples-to-apples product comparisons. Their contextual understanding augments AI’s capabilities.
    • Continuous Learning Via Feedback Loop: Product experts possess rich category knowledge across markets. Integrating this human insight through an iterative feedback loop helps AI models quickly learn and adapt to changing trends, preferences, and context. As humans explain their match assessments, the AI continuously enhances its precision over time.

    By combining AI’s automation and scale with human validation, judgment, and knowledge curation, pricing intelligence solutions can achieve the accuracy and coverage demanded for actionable competitive pricing insights.

    DataWeave’s Data Veracity Framework: A Scalable Workflow Combining AI and Human Expertise

    Given the vast number of products, retailers, and brands that exist today, any product matching solution must be highly scalable. At DataWeave, we bring you such a scalable workflow to address these complexities by integrating human expertise with AI-driven automation. The image below outlines our approach for combining AI with human intelligence in a seamless, scalable workflow for accurate product matching:

    Retailers and brands can benefit in several ways with this workflow, as listed below.

    Several Rounds of Data Verification Due to Hierarchical Validation Teams

    The workflow employs a hierarchical validation team of Leads and Executives to efficiently integrate human expertise without creating bottlenecks. Verification Leads play a pivotal role in managing the distribution of product matches identified by DataWeave’s AI model to the Verification Executives.

    The Executives then meticulously validate these AI-suggested matches, adding any missing product associations and removing inaccurate matches. After validation, the matched product groups are sent back to the Leads, who perform random sampling checks to ensure quality.

    Throughout this entire workflow, feedback and suggestions are continuously gathered from both the Executives and Leads. This curated input is then incorporated back into DataWeave’s AI model, allowing it to learn and improve its matching accuracy on an ongoing basis.

    This hierarchical structure ensures that human validation seamlessly scales alongside the AI’s matching capabilities. Leveraging the respective strengths of AI automation and human expertise in an iterative feedback loop prevents operational bottlenecks while steadily elevating overall accuracy.

    Confidence-based Distribution of Matched Articles for Validation

    The AI model assigns confidence scores, differentiating high-confidence (>95%) and low-confidence matches. For high-confidence groups, executives simply remove incorrect matches – a quicker process. Low-confidence matches require more human effort in adding/removing matches.

    As the AI model improves over time with feedback, the share of high-confidence matches increases, making validation more efficient and swift.

    Automated, Standardized Process with Iterative Feedback Loop

    The entire workflow is standardized and automated, with verification metrics seamlessly tracked. At each step, feedback captured from both leads and executives flows back into the AI, enhancing its matching accuracy and coverage iteratively.

    DataWeave’s closed-loop system of AI automation with hierarchical human validation allows product matching to achieve comprehensive accuracy at a vast scale.

    Unleash the Power Accurate and Comprehensive Product Matching

    In summary, combining AI and human expertise in product matching is crucial for retailers navigating the complexities of omnichannel retail. While AI algorithms excel in automation, they often struggle with subjective criteria and contextual nuances. DataWeave’s approach integrates AI-driven automation with human validation, delivering the industry’s most accurate product matching capabilities, enabling actionable competitive pricing insights.

    To learn more, reach out to us today!

  • How Gas Stations and Convenience Stores in the U.S. Can Adapt To Evolving Fuel Pricing Trends in 2024

    How Gas Stations and Convenience Stores in the U.S. Can Adapt To Evolving Fuel Pricing Trends in 2024

    As we move into the second quarter of 2024, the US energy landscape is poised for notable shifts that will impact gasoline and diesel prices. The shift towards renewable energy sources, evolving consumer preferences, and volatile global market forces are all converging to reshape the fuel retail industry.

    For fuel retailers, understanding these projections is crucial – changes in consumer demand and cost pressures can significantly affect their bottom line. In this article, we provide insights on the factors shaping the fuel pricing environment for the remainder of the year, covering a variety of fuel types.

    Gasoline Prices: A Downward Trend Ahead

    According to the January Short-Term Energy Outlook by the EIA, US retail gasoline prices are projected to decline in 2024. Similarly, the forecast also predicts reduced gasoline consumption in 2025. This is attributed to a significant increase in inventories, thanks to expanded refinery capacity. US operable refinery capacity has grown from 18.06 million barrels per day in January 2023 to 18.31 million barrels per day by December 2023.

    Meanwhile, the World Bank reports that global trade growth in 2024-25 is expected to be only half the average in the decade before the pandemic, leading to reduced consumption and demand.

    The increase in supply, coupled with this dip in demand and consumption expected in 2025, sets the stage for further price reductions. Such expansion not only enhances supply but also alleviates price pressures for consumers.

    Diesel Dynamics: Supply Up, Prices Down

    Similar supply-side dynamics are at play in the diesel market, with retail prices expected to fall in both 2024 and 2025. Despite a forecasted uptick in US diesel consumption in these two years, an increase in production capacity and easing inventory strains are likely to keep prices in check. This is particularly noteworthy, as diesel fuel plays a critical role in transportation and logistics, underpinning the movement of goods and services nationwide.

    Crude Oil and Crack Spreads: The Refining Equation

    Crude oil prices, a pivotal factor in the fuel price equation, are expected to mirror 2023 levels through 2024.

    The anticipated decrease in gasoline and diesel prices is largely attributed to narrowing crack spreads—the differential between wholesale fuel prices and crude oil. A lower crack spread signifies reduced refining costs, a welcome development for both refiners and consumers. This expectation is grounded in the increasing availability of refinery capacity and, consequently, fuel supply, even as demand shows signs of tapering off.

    Global Influences and Economic Implications

    The outlook is further buoyed by new refinery capacities coming online internationally, particularly in the Middle East. This global increase in refined product supplies is poised to ease price pressures for consumers not just domestically but across international markets. Interestingly, this forecast comes at a time when gasoline consumption is expected to remain flat or slightly decrease, a rare occurrence in the context of positive economic growth. This decoupling of fuel consumption from economic expansion highlights evolving consumer behaviors and efficiency gains across the automotive sector.

    Looking Ahead: Uncertainties and Transformations

    While the projections offer a glimpse into a future of potentially lower fuel prices, they are not without uncertainties. Factors such as crude oil price fluctuations, refinery shutdowns, and logistical challenges could sway outcomes.

    The projected decrease in US gasoline and diesel prices presents both opportunities and challenges.

    • For American consumers, lower fuel costs offer relief for household budgets and transportation expenses, potentially freeing up disposable income and stimulating broader economic activity.
    • However, these pricing trends pose a need for strategic planning and adaptation within the US energy sector. Companies must navigate shifting supply dynamics and the ongoing transition towards renewable energy sources – a pivotal chapter in the quest for sustainable and affordable solutions.
    • Energy firms will need to carefully analyze the implications, aligning their business models through refining capacity expansions, logistical optimizations, and a focus on renewable fuels.

    Staying Ahead of Competition with Fuel Price Tracking

    In this evolving landscape, closely tracking fuel prices and having access to up-to-date data is crucial for informed decision-making and staying competitive in the market for fuel retailers. While prices may go down in the long- to medium-term, ensuring short-term price competitiveness at a hyperlocal level is essential for gas stations and convenience stores navigating the changing tides.

    DataWeave’s real-time fuel pricing data, covering a wide range of fuel types from gasoline to diesel and updated as frequently as every 30 minutes, empowers retailers to quickly adapt to market changes and remain strategically aligned with evolving consumer preferences.

    By closely monitoring hyperlocal fuel price fluctuations across their coverage areas, retailers can quickly adapt their pricing strategies to remain competitive and align with shifting consumer behaviors.

    Further, DataWeave’s real-time fuel pricing intelligence can help retailers understand the relationship between crude oil prices, crack spreads (the differential between wholesale fuel prices and crude oil), and their own pricing strategies. Our solution offers real-time insights and analytics to help retailers navigate the evolving fuel pricing landscape.

    Visit our recently launched U.S. Fuel Price Interactive Dashboard which displays weekly fuel prices across 400+ unique ZIP codes, delivering insights into price changes by region, store, fuel type, and other dimensions.

    To learn more about DataWeave’s solutions or to discuss how we can support your fuel retail business, reach out to our team 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.

  • Easter Candy Pricing Trends 2024: Winning Strategies for Retailers and Brands Amid Cocoa Price Surge

    Easter Candy Pricing Trends 2024: Winning Strategies for Retailers and Brands Amid Cocoa Price Surge

    Easter egg hunts just got more challenging for families this year as the price of chocolate and other candies has soared. The root of this price surge lies in a cocoa deficit, attributed to diseases affecting crops and the adverse effects of climate change on West African farms, which supplies over 70% of the world’s cocoa. This has resulted in a tripling of cocoa prices over the last year, causing a “cocoa crunch,” and severely impacted confectioners and chocolate makers.

    Reuters recently reported that Iconic brands such as Hershey’s and Cadbury find themselves grappling with the need to adjust to escalating costs for raw materials. Given that Easter is one of the top three candy-purchasing occasions, these manufacturers are contemplating raising their prices to sustain their profit margins.

    Despite the challenges posed by the cocoa shortfall and persistent inflation, the National Confectioners Association anticipates that Easter candy sales in the U.S. will match or even exceed last year’s figures, which amounted to approximately $5.4 billion. This expectation is predicated more on price increases than on a rise in sales volume.

    At DataWeave, our ongoing analysis of pricing trends across various consumer categories among retailers has provided insight into the evolving landscape of chocolate and candy prices in 2023 and 2024.

    Our Analysis of Inflation in Candy and Chocolate Prices

    Our study encompassed a broad array of 3,300 products from leading U.S. retailers, Amazon, Target, Kroger, and Giant Eagle. As illustrated in the following chart, the trajectory of prices over the past 15 months was compared against the average prices in January 2023. Our tracking focused on two key price points: the selling price, which represents the final cost to consumers after applying any discounts or promotions, and the Manufacturer’s Suggested Retail Price (MSRP), as determined by the brands themselves.

    The findings from our analysis indicate that the average selling price, primarily influenced by retailer decisions, has experienced a steady increase throughout 2023, reaching a peak at 16.2% above January 2023’s figures by December. As of March 2024, coinciding with the Easter season, the selling prices are approximately 10% higher than they were at the beginning of the previous year.

    Simultaneously, the MSRP has seen a consistent uptick, driven by the climbing costs of cocoa. Brands have adjusted their suggested prices accordingly, with the current MSRP standing about 7% above its January 2023 level, after having peaked at a 7.6% increase by December 2023. This reflects the direct impact of rising cocoa costs on product pricing strategies.

    Chocolate Candies Are Hit The Hardest

    Across all candies, chocolate-based products have witnessed significantly sharper price increases than their non-chocolate counterparts. In the past 14 months, the selling prices of chocolate items have surged by 14.9%, a stark contrast to the modest 4% rise observed in non-chocolate candies.

    This price escalation was particularly pronounced during the Christmas shopping period, a response to heightened demand, before experiencing a temporary decline in February.

    The diminishing availability of cocoa, coupled with rising costs for packaging and transportation, has compelled brands and retailers alike to transfer these added expenses onto the consumer. This dynamic underpins the distinct pricing trends observed across the candy spectrum, with chocolate items bearing the brunt of these cost pressures.

    Discounts Offered By Retailers and Brands to Entice Easter Shoppers

    In our analysis, we delved deeper to identify the retailers and brands offering the most compelling prices for Easter-centric confections, including Chocolate Eggs, Chocolate Bunnies, and Easter-themed gift packs.

    Kroger emerged as the frontrunner among the retailers we monitored, offering an impressive 19% discount on Easter candies. Giant Eagle followed with a solid 14% average markdown. Meanwhile, Amazon and Target provided more modest promotional discounts at 12% and 10%, respectively.

    Kroger is making significant efforts to ensure consumers have access to attractively priced Easter treats. The retailer planned to keep its doors open throughout the Easter weekend, featuring baskets brimming with discounted items such as Russell Stover chocolate bunnies, Brach’s jelly beans, Reese’s eggs, and assorted bags of popular candies from Snickers, Twix, and Starburst, among others. Additionally, Kroger is enhancing its value proposition through gift card offers and exclusive Easter deals for its loyalty program members.

    On the brand front, Starburst by Mars Wrigley leads with the steepest discount of 25%. Cadbury, under Mondelez, is not far behind, offering 21% off its mini eggs and other Easter treats, marking an increase from last year’s 17% discount. Ferrero Rocher is making a strong pricing move with an average 20% markdown on its Easter selections, including the chocolate bunny and squirrel figures.

    The beloved Peeps marshmallow candies by Just Born are being offered at an 18% discount this year, slightly less than the 23% discount seen in 2023, likely reflecting the impact of rising sugar costs, given their sugar and corn composition.

    Other notable brands, including M&M’s and the premium Swiss chocolatier Lindt, have elevated their average Easter discounts to 17% this year, up from the previous year’s discounts of 12%, and 10% respectively, showcasing a competitive pricing strategy to delight consumers this Easter season.

    Coping With Inflation This Easter Season

    Retailers and brands aiming to remain profitable and competitive in the current challenging environment can adopt a few strategic approaches:

    • Creative Product Bundling: Design innovative combo packs that mix chocolate and non-chocolate items. Such bundles can cater to diverse consumer preferences and budget ranges while preserving profit margins.
    • Encouragement of Bulk Purchases: Offer enticing discounts on larger quantities to promote bulk buying. This strategy can help amplify sales volumes, compensating for increased costs per item and fostering economies of scale.
    • Strategic Competitive Pricing: Keeping a vigilant eye on competitors’ pricing strategies is vital. Aim to capture market share through well-thought-out discount strategies that balance competitiveness with margin preservation. Leveraging advanced pricing intelligence, such as that offered by DataWeave, can provide invaluable insights for making informed pricing decisions.
    • Product Size Adjustments: Consider revising the size or weight of products as a cost management measure, a strategy known as “shrinkflation.” It’s crucial to approach this transparently, ensuring clear communication on packaging to uphold consumer trust.

    Adopting these strategies—focusing on bundle offerings, incentivizing bulk purchases, optimizing pricing strategies based on competitive intelligence, and thoughtfully adjusting product sizes—will be pivotal for confectioners to navigate the challenges posed by the cocoa price surge.

    For more information, reach out to us to speak to a DataWeave expert today!


  • How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    As eCommerce increasingly becomes a prolific channel of sales for consumer brands, they find that maintaining a consistent and trustworthy brand image is a constant struggle. In an ecosystem filled with dozens of marketplaces and hundreds of third-party merchants, ensuring that customers see what aligns with a brand’s intended image is quite tricky. With many fakes and counterfeit products doing the rounds, brands may further struggle to get the right representation.

    One way brands can track and identify inconsistencies in their brand representation across marketplaces is to use Digital Shelf Analytics solutions like DataWeave’s – specifically the Content Audit module.

    This solution uses advanced AI models to identify image similarities and dissimilarities compared with the original brand image. Brands could then use their PIM platform or work with the retailer to replace inaccurate images.

    But here’s the catch – AI can’t always accurately predict all the differences. Relying solely on scores given by these models poses a challenge in tracking the subtle differences between images. Often, image pairs with seemingly high match scores fail to catch important distinctions. Fake or counterfeit products and variations that slip past the AI’s scrutiny can lead to significant inaccuracies. Ultimately, it puts the reliability of the insights that brands depend on for crucial decisions at risk, impacting both top and bottom lines.

    Dealing with this challenge means finding a balance between the number-based assessments of AI models and the human touch needed for accurate decision-making. However, giving auditors the ability to pinpoint variations precisely goes beyond simply sharing numerical values of the match scores with them. Visualizing model-generated scores is important as it provides human auditors with a tangible and intuitive understanding of the differences between two images. While numerical scores are comparable in the relative sense, they lack specificity. Visual interpretation empowers auditors to identify precisely where variations occur, aiding in efficient decision-making.

    How AI-Powered Image Scoring Works

    At DataWeave, our approach involves employing sophisticated computer vision models to conduct extensive image comparisons. Convolutional Neural Network (CNN) models such as Resnet-50 or YOLO, in conjunction with feature extraction models, analyze images quantitatively. This AI-powered image scoring process yields scores that indicate the level of similarity between images.

    However, interpreting these scores and understanding the specific areas of difference can be challenging for human auditors. While computer vision models excel at processing vast amounts of data quickly, translating their output into actionable insights can be a stumbling block. A numerical score may not immediately convey the nature or extent of the differences between images

    In the assessment of these images, all fall within the 70 to 80 range of scores (out of a maximum of 100). However, discerning the nature of differences—whether they are apparent or subtle—poses a challenge for the AI models and human auditors. For example, there are differences in the placement or type of images in the packaging, as well as packing text that are often in an extremely small font size. It is, of course, possible for human auditors to identify the differences in these images, but it’s a slow, error-prone, and tiring process, especially when auditors often have to check hundreds of image pairs each day.

    So how do we ensure that we identify differences in images accurately? The answer lies in the process of visual highlighting.

    How Visual Highlighting Works

    Visual highlighting is a method that enhances our ability to comprehend differences in images by combining sophisticated algorithms with human understanding. Instead of relying solely on numerical scores, this approach introduces a visual layer, resembling a heatmap, guiding human auditors to specific areas where discrepancies are present.

    Consider the scenario depicted in the images above: a computer vision model assigns a score of 70-85 for these images. While this score suggests relatively high similarity, it fails to uncover major differences between the images. Visual highlighting comes into play to overcome this limitation, precisely indicating regions where even subtle differences are seen.

    Visual highlighting entails overlaying compared images and emphasizing areas of difference, achieved through techniques like color coding, outlining, or shading specific regions. The significance of the difference in a particular area determines the intensity of the visual highlight.

    For instance, if there’s a change in the product’s color or a discrepancy in the packaging, these variations will be visually emphasized. This not only streamlines the auditing process but also enables human evaluators to make well-informed decisions quickly.

    Benefits of Visual Highlighting

    • Intuitive Understanding: Visual highlighting offers an intuitive method for interpreting and acting upon the outcomes of computer vision models. Instead of delving into numerical scores, auditors can concentrate on the highlighted areas, enhancing the efficiency and accuracy of the decision-making process.
    • Accelerated Auditing: By bringing attention to specific regions of concern, visual highlighting speeds up the auditing process. Human evaluators can swiftly identify and address discrepancies without the need for exhaustive image analysis.
    • Seamless Communication: Visual highlighting promotes clearer communication between automated systems and human auditors. Serving as a visual guide, it enhances collaboration, ensuring that the subtleties captured by computer vision models are effectively conveyed.

    The Way Forward

    As technology continues to evolve, the integration of visual highlighting methodologies is likely to become more sophisticated. Artificial intelligence and machine learning algorithms may play an even more prominent role in not only detecting differences but also in refining the visual highlighting process.

    The collaboration between human auditors and AI ensures a comprehensive approach to maintaining brand integrity in the ever-expanding digital marketplace. By visually highlighting differences in images, brands can safeguard their visual identity, foster consumer trust, and deliver a consistent and reliable online shopping experience. In the intricate dance between technology and human intuition, visual highlighting emerges as a powerful tool, paving the way for brands to uphold their image with precision and efficiency.

    To learn more, reach out to us today!


    (This article was co-authored by Apurva Naik)

  • How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    Retailers heavily depend on pricing intelligence solutions to consistently achieve and uphold their desired competitive pricing positions in the market. The effectiveness of these solutions, however, hinges on the quality of the underlying data, along with the coverage of product matches across websites.

    As a retailer, gaining complete confidence in your pricing intelligence system requires a focus on the trinity of data quality:

    • Accuracy: Accurate product matching ensures that the right set of competitor product(s) are correctly grouped together along with yours. It ensures that decisions taken by pricing managers to drive competitive pricing and the desired price image are based on reliable apples-to-apples product comparisons.
    • Freshness: Timely data is paramount in navigating the dynamic market landscape. Up-to-date SKU data from competitors enables retailers to promptly adjust pricing strategies in response to market shifts, competitor promotions, or changes in customer demand.
    • Product matching coverage: Comprehensive product matching coverage ensures that products are thoroughly matched with similar or identical competitor products. This involves accurately matching variations in size, weight, color, and other attributes. A higher coverage ensures that retailers seize all available opportunities for price improvement at any given time, directly impacting revenues and margins.

    However, the reality is that untimely data and incomplete product matches have been persistent challenges for pricing teams, compromising their pricing actions. Inaccurate or incomplete data can lead to suboptimal decisions, missed opportunities, and reduced competitiveness in the market.

    What’s worse than poor-quality data? Poor-quality data masquerading as accurate data.

    In many instances, retailers face a significant challenge in obtaining comprehensive visibility into crucial data quality parameters. If they suspect the data quality of their provider is not up to the mark, they are often compelled to manually request reports from their provider to investigate further. This lack of transparency not only hampers their pricing operations but also impedes the troubleshooting process and decision-making, slowing down crucial aspects of their business.

    We’ve heard about this problem from dozens of our retail customers for a while. Now, we’ve solved it.

    DataWeave’s Data Statistics and SKU Management Capability Enhances Data Transparency

    DataWeave’s Data Statistics Dashboard, offered as part of our Pricing Intelligence solution, enables pricing teams to gain unparalleled visibility into their product matches, SKU data freshness, and accuracy.

    It enables retailers to autonomously assess and manage SKU data quality and product matches independently—a crucial aspect of ensuring the best outcomes in the dynamic landscape of eCommerce.

    Beyond providing transparency and visibility into data quality and product matches, the dashboard facilitates proactive data quality management. Users can flag incorrect matches and address various data quality issues, ensuring a proactive approach to maintaining the highest standards.

    Retailers can benefit in several ways with this dashboard, as listed below.

    View Product Match Rates Across Websites

    The dashboard helps retailers track match rates to gauge their health. High product match rates signify that pricing teams can move forward in their pricing actions with confidence. Low match rates would be a cause for further investigation, to better understand the underlying challenges, perhaps within a specific category or competitor website.

    Our dashboard presents both summary statistics on matches and data crawls as well as detailed snapshots and trend charts, providing users with a holistic and detailed perspective of their product matches.

    Additionally, the dashboard provides category-wise snapshots of reference products and their matching counterparts across various retailers, allowing users to focus on areas with lower match rates, investigate underlying reasons, and develop strategies for speedy resolution.

    Track Data Freshness Easily

    The dashboard enables pricing teams to monitor the timeliness of pricing data and assess its recency. In the dynamic realm of eCommerce, having up-to-date data is essential for making impactful pricing decisions. The dashboard’s presentation of freshness rates ensures that pricing teams are armed with the latest product details and pricing information across competitors.

    Within the dashboard, users can readily observe the count of products updated with the most recent pricing data. This feature provides insights into any temporary data capture failures that may have led to a decrease in data freshness. Armed with this information, users can adapt their pricing decisions accordingly, taking into consideration these temporary gaps in fresh data. This proactive approach ensures that pricing strategies remain agile and responsive to fluctuations in data quality.

    Proactively Manage Product Matches

    The dashboard provides users with proactive control over managing product matches within their current bundles via the ‘Data Management’ panel. This functionality empowers users to verify, add, flag, or delete product matches, offering a hands-on approach to refining the matching process. Despite the deployment of robust matching algorithms that achieve industry-leading match rates, occasional instances may arise where specific matches are overlooked or misclassified. In such cases, users play a pivotal role in fine-tuning the matching process to ensure accuracy.

    The interface’s flexibility extends to accommodating product variants and enables users to manage product matches based on store location. Additionally, the platform facilitates bulk match uploads, streamlining the process for users to efficiently handle large volumes of matching data. This versatility ensures that users have the tools they need to navigate and customize the matching process according to the nuances of their specific product landscape.

    Gain Unparalleled Visibility into your Data Quality

    With DataWeave’s Pricing Intelligence, users gain the capability to delve deep into their product data, scrutinize match rates, assess data freshness, and independently manage their product matches. This approach is instrumental in fostering informed and effective decisions, optimizing inventory management, and securing a competitive edge in the dynamic world of online retail.

    To learn more, reach out to us today!

  • Capturing and Analyzing Retail Mobile App Data for Digital Shelf Analytics: Are Brands Missing Out?

    Capturing and Analyzing Retail Mobile App Data for Digital Shelf Analytics: Are Brands Missing Out?

    Consumer brands around the world increasingly recognize the vital role of tracking and optimizing their digital shelf KPIs, such as Content Quality, Share of Search, Availability, etc. These metrics play a crucial role in boosting eCommerce sales and securing a larger online market share. With the escalating requirements of brands, the sophistication of top Digital Shelf Analytics providers is also on the rise. Consequently, the adoption of digital shelf solutions has become an essential prerequisite for today’s leading brands.

    As brands and vendors continue to delve further and deeper into the world of Digital Shelf Analytics, a significant and often overlooked aspect is the analysis of digital shelf data on mobile apps. The ability of solution providers to effectively track and analyze this mobile-specific data is crucial.

    Why is this emphasis on mobile apps important?

    Today, the battle for consumer attention unfolds not only on desktop web platforms but also within the palm of our hands – on mobile devices. As highlighted in a recent Insider Intelligence report, customers will buy more on mobile, exceeding 4 in 10 retail eCommerce dollars for the first time.

    Moreover, thanks to the growth of delivery intermediaries like Instacart, DoorDash, Uber Eats, etc., shopping on mobile apps has received a tremendous organic boost. According to an eMarketer report, US grocery delivery intermediary sales are expected to reach $68.2 billion in 2025, from only $8.8 billion in 2019.

    In essence, mobile is increasingly gaining share as the form factor of choice for consumers, especially in CPG. In fact, one of our customers, a leading multinational CPG company, revealed to us that it sees up to 70% of its online sales come through mobile apps. That’s a staggering number!

    The surge in app usage reflects a fundamental change in consumer behavior, emphasizing the need for brands to adapt their digital shelf strategies accordingly.

    Why Brands Need To Look at Apps and Desktop Data Differently

    Conventionally, brands that leverage digital shelf analytics rely on data harnessed from desktop sites of online marketplaces. This is because capturing data reliably and accurately from mobile apps is inherently complex. Data aggregation systems designed to scrape data from web applications cannot easily be repurposed to capture data on mobile apps. It requires dedicated effort and exceptional tech prowess to pull off in a meaningful and consistent way.

    In reality, it is extremely important for brands to track and optimize their mobile digital shelf. Several digital shelf metrics vary significantly between desktop sites and mobile apps. These differences are natural outcomes of differences in user behavior between the two form factors.

    One of these metrics that has a huge impact on a brand’s performance on retail mobile apps is their search discoverability. Ecommerce teams are well aware of the adverse impact of the loss of even a few ranks on search results.

    Anyone can easily test this. Searching something as simple as “running shoes” on the Amazon website and doing the same on its mobile app shows at least a few differences in product listings among the top 20-25 ranks. There are other variances too, such as the number of sponsored listings at the top, as well as the products being sponsored. These variations often result in significant differences in a brand’s Share of Search between desktop and mobile.

    Share of Search is the share of a brand’s products among the top 20 ranked products in a category or subcategory, providing insight into a brand’s visibility on online marketplaces.

    Picture a scenario in which a brand heavily depends on desktop digital shelf data, confidently assuming it holds a robust Share of Search based on reports from its Digital Shelf Analytics partner. However, unbeknownst to the team, the Share of Search on mobile is notably lower, causing a detrimental effect on sales.

    To fully understand the scale of these differences, we decided to run a small experiment using our proprietary data analysis and aggregation platform. We restricted our analysis to just Amazon.com and Amazon’s mobile app. However, we did cover over 13,000 SKUs across several shopping categories to ensure the sample size is strong.

    Below, we provide details of our key findings.

    Share of Search on The Digital Shelf – App Versus Desktop

    Our analysis focused on three popular consumer categories – Electronics, CPG, and Health & Beauty.

    In the electronics category, brands like Apple, Motorola, and Samsung, known for their mobile phones, earbuds, headphones, and more, have a higher Share of Search on the Amazon mobile app compared to the desktop.

    Meanwhile, Laptop brands like Dell, Acer, and Lenovo, as well as other leading brands like Google have a higher Share of Search on the desktop site compared to the app. This is the scenario that brands need to be careful about. When their Share of Search on mobile apps is lower, they might miss the chance to take corrective measures since they lack the necessary data from their provider.

    In the CPG category, Ramen brand Samyang, with a lot of popularity on Tiktok and Instagram, shows a higher Share of Search on Amazon’s mobile app. Speciality brands like 365 By Whole Foods, pasta and Italian food brands La Moderna, Divinia, and Bauducco too have a significantly higher Share of Search on the app.

    Cheese and dessert brands like Happy Belly, Atlanta Cheesecake Company, among others, have a lower Share of Search on the mobile app. Ramen brand Sapporo is also more easily discovered on Amazon’s desktop site. Here, we see a difference of more than 5% in the Share of Search of some brands, which is likely to have a huge impact on the brand’s mobile eCommerce sales levels and overall performance.

    Lastly, in the Health & Beauty category, Shampoos and hair care brands like Olaplex, Dove, and Tresemme exhibited a higher Share of Search on the mobile app compared to the desktop.

    On the other hand, body care brands like Neutrogena and Hawaiian Tropic, as well as Beardcare brand Viking Revolution displayed a higher Share of Search on Amazon’s desktop site.

    Based on our data, it is clear that there are several examples of brands that do better in either one of Amazon’s desktop sites or mobile apps. In many cases, the difference is stark.

    So What Must Brands Do?

    Our findings emphasize the imperative for brands to move beyond a one-size-fits-all approach to digital shelf analytics. The striking variations in Share of Search between mobile apps and desktops conclusively demonstrate that relying solely on desktop data for digital shelf optimization is inadequate.

    If brands see that they’re falling behind on the mobile digital shelf, there are a few things they can do to help boost their performance:

    • If a brand’s Share of Search is lower on the mobile app, they can divert their retail spend to mobile in order to inorganically compensate for this difference. This way, any short-term impact due to lower discoverability is mitigated. This is also likely to result in optimized budget allocation and ROAS.
    • Brands also need to ensure their content is optimized for the mobile form factor, with images that are easy to view on smaller screens, and tailored product titles that are shorter than on desktops, highlighting the most important product attributes from the consumer’s perspective. Not only will this help brands gain more clicks from mobile shoppers, but this will also gradually lead to a boost in their organic Share of Search on mobile.
    • CPG brands, specifically, need to optimize their digital shelf for delivery intermediary apps (along with marketplaces). The grocery delivery ecosystem is booming with companies like DoorDash, Delivery Hero, Uber Eats, Swiggy, etc. leading the way. Using Digital Shelf Analytics to optimize performance on delivery apps is quite an involved process with a lot of bells and whistles to consider. Read our recently published whitepaper that specifically details how brands can successfully boost their visibility and conversions on delivery apps.

    But first, brands need to identify and work with a Digital Shelf Analytics partner that is able to capture and analyze mobile app data, enabling tailored optimization approaches for all eCommerce platforms.

    DataWeave leads the way here, providing the world’s most comprehensive and sophisticated digital shelf analytics solution, rising above all other providers to provide digital shelf insights for both web applications and mobile apps. Our data aggregation platform successfully navigates the intricacies of capturing public data accurately and reliably from mobile apps, thereby delivering a comprehensive cross-device view of digital shelf KPIs to our brand customers.

    So reach out to us today to find out more about our digital shelf solutions for mobile apps!

  • AI-powered Product Matching: The Key to Competitive Pricing Intelligence in eCommerce

    AI-powered Product Matching: The Key to Competitive Pricing Intelligence in eCommerce

    With thousands of products and hundreds of online retailers to choose from, the average modern-day shopper usually compares prices across several e-commerce sites effortlessly before often settling for the lowest priced option. As a result, retailers today are forced to execute millions of price changes per day in a never-ending race to be the lowest priced – without losing out on any potential margin.

    Identifying, classifying, and matching products is the first step to comparing prices across websites. However, there is no standardization in the way products are represented across e-commerce websites, causing this process to be fairly complex.

    Here’s an example:

    What’s needed is a pricing intelligence solution that first matches products across several websites swiftly and accurately, and then enables automated tracking of competitor pricing data on an ongoing basis.

    Pricing intelligence solutions already exist. What’s wrong with using them?

    There are several challenges with the incumbent solutions in the market – the biggest one being that they don’t work in a timely manner. In essence, it’s like deferring the process of finding actionable information that helps retailers acquire a competitive advantage, and instead doing it in hindsight. Like an autopsy of sorts.

    Here are the various solution types we have in the market today:

    • Internally developed systems – Solutions developed by retailers themselves often rely on heavy manual data aggregation and have poor product matching capabilities. Since these solutions have been developed by professionals not attuned to building data crunching machines, they pose significant operational challenges in the form of maintenance, updates, etc.
    • Web scraping solutions – These solutions have no data normalization or product matching capabilities, and lack the power to deliver relevant actionable insights. What’s more, it’s a struggle to scale them up to accommodate massive volumes of data during peak times such as promotional campaigns.
    • DIY solutions – These solutions require manual research and entry of data. It goes without saying that due to the level of human intervention and effort required, they’re expensive, difficult to scale, slow, and of questionable accuracy.

    As common as it is nowadays, AI has the answer

    DataWeave’s competitive pricing intelligence solution is designed to help retailers achieve precisely the competitive advantage they need by providing them with accurate, timely, and actionable pricing insights enabled by matching products at scale. We provide retailers with access to detailed pricing information on millions of products across competitors, as frequently as they need it.

    Our technology stack broadly consists of the following.

    1. Data Aggregation

    At DataWeave, we can aggregate data from diverse web sources across complex web environments – consistently and at a very high accuracy. Having been in the industry for close to a decade, we’re sitting on a lot of data that we can use to train our product matching platform.

    Our datasets include data points from tens of millions of products and have been collected from numerous geographies and verticals in retail. The datasets contain hierarchically arranged information based on retail taxonomy. At the root level, there’s information such as category and subcategory, and at the top level, we have product details such as title, description, and other <attribute, value> relationships. Our machine learning architectures and semi-automated training data building systems, augmented by the skills of a strong QA team, help us annotate the necessary information and create labeled datasets using proprietary tools.

    2. AI for Product Matching

    Product matching at DataWeave is done via a unified platform that uses both text and image recognition capabilities to accurately identify similar SKUs across thousands of e-commerce stores and millions of products. We use an ensemble deep learning architectures tailored to NLP and Computer Vision problems specific to us and heuristics pertinent to the Retail domain. Products are also classified based on their features, and a normalization layer is designed based on various text/image-based attributes.

    Our semantics layer, while technically an integral part of the product matching process, deserves particular mention due to its powerful capabilities.

    The text data processing consists of internal, deep pre-trained word embeddings. We use state-of-the-art, customized word representation techniques such as ELMO, BERT, and Transformer to capture deeply contextualized text with improved accuracy. A self-attention/intra-attention mechanism learns the correlation between the word in question and a previous part of the description.

    Image data processing starts with object detection to identify the region of interest of a given product (for example, the upper body of a fashion model displaying a shirt). We then leverage deep learning architectures such as VggNet, Inception-V3, and ResNet, which we have trained using millions of labeled images. Next, we apply multiple pre-processing techniques such as variable background removal, face removal, skin removal, and image quality enhancing and extract image signatures via deep learning and machine learning-based algorithms to uniquely identify products across billions of indexed products.

    Finally, we efficiently distribute billions of images across multiple stores for fast access, and to facilitate searches at a massive scale (in a matter of milliseconds, without the slightest compromise on accuracy) using our image matching engine.

    3. Human Intelligence in the Loop

    In scenarios where the confidence scores of the machine-driven matches are low, we have a team of Quality Assurance (QA) specialists who verify the output.

    This team does three things:

    • Find out why the confidence score is low
    • Confirm the right product matches
    • Figure out a way to encode this knowledge into a rule and feed it back to the algorithm

    In this way, we’ve built a self-improving feedback loop which, by its very nature, performs better over time. This system has accumulated knowledge over the 8 years of our operations, which is going to be hard for anyone to replicate. Essentially, this process enables us to match products at massive scale quickly and at very high levels of accuracy (usually over 95%).

    4. Actionable Insights Via Data Visualization

    Once the matching process is completed, the prices are aggregated at any frequency, enabling retailers to optimize their prices on an ongoing basis. Pricing insights are typically consumed via our SaaS-based web-portal, which consists of dashboards, reports, and visualizations.

    Alternatively, we can integrate with internal analytics platforms through APIs or generate and deliver spreadsheet reports on a regular basis, depending on the preferences of our customers.

    To summarize

    The benefits of our solution are many. Detailed price improvement opportunity-related insights generated in a timely manner empower retailers to significantly enhance their competitive positioning across categories, product types, and brands, as well as ability to influence their price perception among consumers. These insights, when leveraged at a higher granularity over the long term, can help maximize revenue through price optimization at a large scale.

    Our solution also helps drive process-based as well as operational optimizations for retailers. Such modifications help them better align themselves to effectively adopt a data-driven approach to pricing, in turn helping them achieve much smarter retail operations across the board.

    All of this wouldn’t be possible if the product matching process, inherent to this system, was unreliable, expensive, or time-consuming.

    If you would like to learn more about DataWeave’s proprietary product matching platform and the benefits it offers to eCommerce businesses and brands, talk to us now!

  • Black Friday Cyber Monday 2023: Unveiling Health & Beauty Pricing and Discount Trends

    Black Friday Cyber Monday 2023: Unveiling Health & Beauty Pricing and Discount Trends

    On Black Friday this year, Health & Beauty brands saw a significant increase with a 13% jump in foot traffic, according to a report by RetailNext. Despite caution from various sources, higher prices for everyday goods, and high interest rates, consumers chose to spend big this cyber week.

    So what kind of deals did top retailers and brands offer in the Health & Beauty category this BFCM? At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of Health & Beauty 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.

    Also check out our insights on discounts and pricing for Consumer Electronics, Apparel, and Home & Furniture categories this Black Friday and Cyber Monday.

    Our Methodology

    For this analysis, we tracked the average discounts among leading US retailers in the Health & Beauty 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: 15,253 SKUs
    • Retailers tracked: Amazon, Walmart, Target, Sephora, Ulta Beauty
    • Subcategories reported on: Shampoo, Toothpaste, Conditioner, Sunscreen, Makeup, Electric Toothbrush, Beard Care, Moisturizer
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Average Discounts Across Retailers

    Amazon leads the pack with a huge margin, offering an average discount of 31.9%, covering 62% of its products analyzed. Target follows an 18.8% average discount across only 5% of its analyzed assortment. The other retailers aren’t even close.

    Ulta Beauty was the next in line, providing a 9.2% average discount followed by Walmart with a 6.8% average discount. Sephora, known for its premium beauty offerings, adopted a more conservative approach with a 3.5% average discount, targeting only 9% of its top products

    Across retailers, it is clear that Amazon led the charge by far this cyber week, with the other retailers choosing to markdown prices conservatively in the Health & Beauty category.

    Average Discounts: Subcategories

    Amazon offered high discounts on lower priced subcategories like Toothpaste (49.4%), Sunscreen (46.3%), Moisturizers (38.5%), and Conditioners (37.5%), highlighting its focus on products with high demand that consumers would look to stock up on. Ulta Beauty also focused its discounts on Toothpaste (15.6%), Moisturizers (14.9%), and Conditioners (12.6%), targeting skincare and grooming.

    Sephora, meanwhile, offered the most attractive deals on the Makeup subcategory at 5.3% across 12.67% of its analyzed assortment, banking on the demand generated due to the brand’s popularity in this subcategory.

    Target prioritized discounts on Toothpaste (22.5%), Shampoo (21.6%), and Moisturizers (18.9%). Walmart too offered significant discounts on Shampoo (21.6%) and Toothpaste (22.5%).

    Retailers prioritized staple subcategories like Toothpaste and Moisturizer with substantial discounts during this Black Friday Cyber Monday, ensuring a broad consumer appeal. In contrast, discretionary items like Makeup may be less motivated by discounts alone, and hence saw lower discounts during the sale.

    Average Discounts: Brands

    Brands offered the most attractive deals on Amazon, with OGX leading the pack at 58.4% average discount. Neutrogena and Colgate followed with an average discount of 50.4% and 44%. This mirror’s Amazon’s subcategory focus on shampoos, conditioners, and toothpastes.

    Other instances of brands offering attractive deals across retailers include Belif (27.9%) and Anastasia Beverly Hills (17.6%) on Sephora, Johnson’s (20%) and Philips Sonicare (18.8%) on Target, and Olay (12.2%) and Colgate (10.6%) on Walmart.

    Ulta Beauty hosted several attractive deals by specific brands, including Moon (30.7%), Joico (24%), and Clinique (22.3%).

    Share of Search For Health & Beauty Brands Across Subcategories

    Our Share of Search analysis illuminates the strategic moves made by brands to enhance their visibility, playing a crucial role in influencing consumer choices during Black Friday and Cyber Monday.

    Among some of the leading brands, Head & Shoulders and Oral-B increased their Share of Search by 2.3% and 1% respectively, reflecting a successful strategy to boost brand visibility during the Black Friday and Cyber Monday shopping events. On the other hand, L’Oreal Paris, Colgate, and Neutrogena faced marginal decreases in Share of Search.

    Overall, since the difference in Share of Search values did not change dramatically, the visibility levels of leading brands across key subcategories remained consistent during the Thanksgiving weekend.

    —

    For deeper insights on pricing and discounting trends across a diverse range of shopping categories during Black Friday and Cyber Monday, check out our blog!

    To learn more about our AI-powered Pricing Intelligence and Digital Shelf Analytics platform, contact us today!

  • 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!