As the holiday shopping season kicked off, savvy shoppers embraced the spirit of the season, drawn by enticing deals. The apparel category is forecasted as the second highest earning category (Source: Statista), expected to generate revenues up to $43.9 billion, closely following consumer electronics. To understand the pricing strategies of top retailers amidst the sale season, DataWeave analyzed the pricing trends for the Apparel category this Black Friday.
We leveraged our AI-powered data platform to analyze the discounting across key retailers. Our analysis focused on the Apparel category, examining how Amazon, Walmart, Target, Saks Fifth Avenue, Nordstrom, Bloomingdales, Neiman Marcus and Macy’s differentiated themselves through their discounts.
For this analysis, we tracked the average discounts of apparel products among leading US retailers during the Thanksgiving weekend sale, including Black Friday. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across during the sale.
Subcategories reported on: Footwear, Kid’s Clothing, Men’s Clothing, Women’s Clothing, Activewear, Plus Size Clothing, Accessories
Timeline of analysis: 10 to 29 November 2024
We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “athleisure” and “plus size clothing”. Our methodology distinguished between standard discounts and Black Friday-specific ‘additional discounts’ or price reductions during the sale compared to the week before, to reveal true consumer value.
Key Findings
This year’s fashion discounts were unprecedented. Let’s take a look.
Retailer Level Insights
Nordstrom leads with the highest average absolute discount at 59%, followed by Saks Fifth Avenue at 35.5% and Bloomingdale’s at 41.5%. Macy’s shows the lowest average discount at 24.1%, while Amazon has an average discount of 30.4%.
Amazon ranks lower in both average absolute and additional discounts compared to competitors, indicating a more conservative discounting strategy.
Subcategory Analysis
Kids’ Clothing saw the deep discounts (up to 55% at Nordstrom), reflecting growing pressure on family budgets and heightened competition to attract budget-conscious parents.
Plus-Size Clothing emerged as a major focus, with Nordstrom leading at 53.22% average absolute discounts, signaling that retailers are increasingly prioritizing size inclusivity and appealing to a broader consumer base.
Footwear experienced robust discounting, particularly at Bloomingdale’s with 37% average absolute discounts, showing a competitive approach to attract customers looking for seasonal footwear deals.
Activewear displayed substantial discounts, with Walmart offering up to 41% on average, aligning with the trend of consumers looking for practical and comfortable attire during the winter season.
Brand Level Insights
Apparel brands, meanwhile, also offer telling insights.
Top Discounting Brands: Aqua leads with an average absolute discount of 44.58%, followed by Boss at 42.33% and Burberry at 37.84%.
Lowest Discounts: Athletic Works shows the lowest average absolute discount at 31.23%, with a minimal additional discount of 3.73%.
Competitive Advantage: Brands like Ralph Lauren and Boss show strong discounts, indicating aggressive marketing during the sale.
Share of Search Insights
Top Gainers: Adidas and Nike each saw an increase of 1.20% in their share of search during Black Friday/Cyber Monday, highlighting their strong brand presence and consumer interest.
Top Losers: Reebok experienced a sharp decline, losing 2.60% in its share of search, while Levi’s also dropped by 0.60%.
Search Trends: The data suggests a strong consumer preference for activewear brands like Nike and Adidas and a decline in interest for traditional apparel brands like Levi’s.
Who Offered Most Value This Black Friday
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 418 matched products across Apparel specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Category-Level Analysis
At the overall category level, Macy’s emerged as the lowest-priced retailer, offering the highest average discount of 28.72%, followed closely by Nordstrom (26.06%). The steep decline in average discounts from Saks Fifth Avenue (14.42%) and Neiman Marcus (7.93%) highlights a clear gap in discounting strategies.
Macy’s and Nordstrom are aggressively competitive on pricing in the overall apparel category, likely capturing consumer attention with substantial discounts.
Saks Fifth Avenue and Neiman Marcus may rely more on brand perception and luxury positioning rather than heavy discounting.
Subcategory-Level Analysis
Neiman Marcus tops the ranking with an impressive 60.85% average discount, outperforming Macy’s (52.86%) and Nordstrom (43.04%) for Men’s Clothing. We see a similar trend with Neiman Marcus offering more value across Women’s Clothing as well, compared to other retailers.
The competition in footwear was intense, with Neiman Marcus narrowly securing the top spot at 31.03%, slightly ahead of Saks Fifth Avenue (30.28%) and Macy’s (30.07%).
Saks Fifth Avenue led by a significant margin in the Activewear category, offering 39.89% average discounts, indicating a strong push in this growing segment.
Macy’s followed at 32.16% in Activewear, while Neiman Marcus and Nordstrom had comparatively lower discounts of 26.40% and 19.52%, respectively.
Brand-Level Analysis
Kate Spade New York: Neiman Marcus leads with the highest discount of 55.23%, reflecting strong price leadership in premium fashion, closely followed by Saks Fifth Avenue at 51.66%.
Coach: Neiman Marcus dominates with a significant 75.85% discount, showcasing an aggressive promotional strategy for this luxury brand.
Spanx: While Neiman Marcus leads with 28.22%, discounts across other retailers like Saks Fifth Avenue, Macy’s, and Nordstrom are clustered within a competitive range of 17–19%.
Montblanc: Macy’s takes the lead with 20.32%, signaling its competitiveness even in high-end accessories, with Saks Fifth Avenue and Nordstrom closely behind.
Ugg: Saks Fifth Avenue leads with 31.42%, focusing on maintaining price leadership for this popular brand, while other retailers remain competitive with discounts around 25–30%.
What’s Next
To win over price-conscious shoppers, retailers need to stay competitive and consistently offer the lowest prices.
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 more insights on different categories this Black Friday and Cyber Monday.
As shoppers flocked online and to stores during Black Friday and Cyber Monday, the grocery category stood out as a key battleground for retailers. With inflation affecting consumer spending, discounted groceries have become a critical driver for both shopper savings and retailer competitiveness.
In fact, according to the NRF, one of the top shopping destinations during Thanksgiving weekend were department stores (42%), online (42%),and grocery stores and supermarkets (40%). Clearly, consumers are looking to stock up in bulk on their groceries to maximize their savings.
To understand the pricing dynamics in the grocery category, DataWeave analyzed grocery discounts across leading grocers, uncovering significant trends that shaped consumer choices during this holiday shopping period.
Our research encompassed retailers like Amazon, Target, and Walmart, examining their discounting strategies across subcategories, alongside trends in share of search for leading CPG companies.
Key Grocery Market Stats for Black Friday-Cyber Monday 2024
Retailer Discounts: Walmart offered the highest average absolute discount at 27.6%, followed by Amazon at 20.4% and Target at 14.0%
Subcategory Insights: Beverages Category at Walmart saw the deepest discounts, with an average of 33.4%
Top Gaining Brands: Cesar experienced the largest increase in share of search during the sales period (+3.89%)
This blog will dive deeper into grocery discount trends and brand-level strategies, offering insights for retailers looking to stay competitive in the grocery sector.
Our Methodology
For this analysis, we tracked the average discounts offered by major U.S. grocery retailers during the Thanksgiving weekend, including Black Friday and Cyber Monday. We focused on key subcategories within the grocery segment, capturing trends in discounting strategies.
In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Key Findings
Retailer-Level Insights
Walmart emerged as the leader in grocery discounting, offering the highest average absolute (27.6%) and additional (18%) discounts.
Amazon adopted a mid-tier discounting strategy, with average absolute discounts of 20.4%.
Target, while more conservative, maintained competitiveness in select subcategories like baby products.
Subcategory Insights
Pantry Essentials saw Walmart leading with an average discount of 31.2%, appealing to budget-conscious consumers stocking up for the holidays.
Fresh Produce showed consistent discounting across retailers, with Amazon slightly ahead at 27%.
Beverages stood out for significant discounting at Walmart, with an impressive 33.4% average discount.
Brand-Level Insights
Lay’s led in absolute discounts (37.52%) and additional discounts (26.23%) showcasing aggressive pricing in the snacks subcategory.
Good & Gather maintained its competitive edge with strong discounts, appealing to price-conscious consumers seeking value.
Brands like Blue Buffalo (pet food brand) offered significant absolute discounts, but with a low additional discount of just 2%, the overall impact of the sale event on effective value was limited.
Share of Search Insights
Cesar (dog food brand), Tide (laundry staple) and Doritos saw significant gains in share of search, reflecting successful promotional strategies.
Brands like Pampers (baby diapers brand), Healthy Choice, (frozen foods brand) and Pedigree (pet food brand) experienced a decline, indicating less effective engagement during the sale period.
Who offered the lowest prices?
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 1433 matched products across retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Category-Level Analysis
Walmart is the lowest priced retailer overall for the grocery category, with an impressive average discount of 44.60%. This significant discount advantage makes Walmart a leading option for value-seeking consumers.
Target follows with strong discounts of 36.73%, indicating solid pricing in comparison but less aggressive than Walmart.
Interestingly, Amazon was the most expensive in Grocery, with an average discount of only 6.3%.
Subcategory-Level Analysis
Walmart leads in various subcategories such as Pet Products (21.12%), Dairy & Eggs (13.79%), Household Essentials (13.05%), Frozen Foods (15.07%), and Meat & Seafood (17.60%), showcasing its extensive value across the board.
Target excels in Beverages (14.58%) and Baby Products (15.00%) with competitive discounts, standing out in these specific subcategories.
Kroger provides notable value in Pantry Essentials (20.04%) and Fresh Produce (15.85%), although its overall average discount is lower than Walmart’s.
Amazon consistently ranks lower in terms of average discounts across most subcategories, highlighting it as less competitive for consumers seeking the lowest prices.
Brand-Level Analysis
Walmart also holds the top position for several key brands like Cheetos (14.92%) and Dannon (8.81%), making it the best option for consumers looking for budget-friendly choices across popular brands.
Target takes the lead for brands like Betty Crocker (25.20%) and Chobani (11.37%), showing that it can offer value for specific products.
Kroger maintains strong discounts for brands such as Delmonte (9.19%), but it does not outpace Walmart in the overall grocery brand comparison.
Amazon generally lags behind in average discounts for most brands, with Dannon (1.12%) and Chobani (2.43%) showing significantly lower discounts.
Walmart is the lowest priced retailer in the grocery category and provides substantial value across a wide range of subcategories and popular brands. This ties in with Walmart’s ELDP pricing strategy. The retailer leads in overall average discounts and maintains its position as the go-to for price-conscious consumers. Target offers strong value in certain subcategories and brands but falls short of Walmart’s broad value based pricing advantages.
What’s Next
For grocery retailers, competitive pricing and targeted promotions are critical to driving sales during key shopping events. As consumers continue to prioritize value, staying ahead in the discounting game can significantly impact market share.
For detailed insights into grocery discounting strategies and to explore how DataWeave’s solutions can help retailers optimize their pricing, contact us today!
Stay tuned to our blog for further analyses of other categories during Black Friday and Cyber Monday.
The Home & Furniture category continues to thrive, propelled by consumer interest in creating personalized and functional living spaces. In 2023, the U.S. furniture and home furnishings market was valued at approximately $641.7 billion in 2023 and is estimated to grow at a CAGR of 5.1% from 2024 to 2032. Black Friday and Cyber Monday play a crucial role in fueling this growth, offering consumers a mix of premium and affordable options across subcategories.
To better understand market trends and discount strategies this Black Friday, at DataWeave we tracked over 18,149 SKUs across major home & furniture retailers, including Amazon, Walmart, Target, Best Buy, Home Depot, and Overstock, from November 10 to 29, 2024. Using our AI-powered pricing intelligence platform, we focused on the top 500 products in subcategories like kitchenware, furniture, decor, lighting, outdoor items, and bedding.
In our analysis, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the Black Friday sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Also check out our insights on discounts and pricing for the health & beauty category this Black Friday.
Retailer Performance: Who Led the Discount Race?
Retailers showed varying discount strategies for Home & Furniture products. Walmart emerged as the leader in absolute discounts (37.5%) while Amazon offered the highest additional discount of 14%. Best Buy maintained competitive pricing across all subcategories, while Overstock and Home Depot offered relatively modest discounts.
Subcategories in Focus
Breaking down the discounts by subcategory provides deeper insights into consumer priorities and retailer strategies:
Kitchenware saw strong competition, with Walmart (30.40% absolute discounts) and Amazon (29% absolute discounts) dominating.
Lighting became a discount hotspot, with Walmart offering up to 45.8% in absolute discounts and 25.3% additional markdowns.
Furniture remained a core focus for Target, delivering an impressive 34% average absolute discount.
Bedding stood out at Walmart, where discounts peaked at 49.6%.
Brand Spotlight: Who Stood Out?
Among top-performing brands, furniture brand Costway offered the highest discounts, with an average of 48.4%. Meanwhile, Adesso (lighting solutions), Mainstays and Safavieh (both home furnishings brands) balanced discounts and premium appeal.
Search Visibility: The Winners and Losers
Share of search dynamics revealed significant shifts in brand visibility during Black Friday:
Furniture brand Costway (+1.2%) and home improvement player Black+Decker (+1.5%) gained visibility.
On the flip side, premium brands like Safavieh known for rugs and home furnishings (-16.8%) and furniture brand Burrow ( -1.7%) saw declines.
Who Offers the Lowest Prices?
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 735 matched products across Home & Furniture specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Category-Level Highlights
Amazon emerges as the lowest-priced retailer across Home & Furniture categories, with the highest average discount of 27.50%, closely followed by Walmart (26.09%).
Overstock and Wayfair trail with average discounts of 22.93% and 20.71%, respectively, while Home Depot offers the least aggressive pricing at 18.14%. This is notable, as all 3 players are known specialists in the category.
Subcategory Highlights
Amazon stands out as the leader in multiple subcategories, including Appliances, Furniture, Decor, and Outdoor, offering competitive average discounts of around 26-29%.
Overstock leads in Bedding and Kitchenware, with strong average discounts of 24.26% and 20.72%, respectively.
Wayfair is notable for Lighting, with an average discount of 19.95%, and is also competitive in Outdoor and Furniture categories.
Walmart consistently ranks high in several subcategories like Appliances and Bedding, providing solid discounts of around 22-23%.
What’s Next
For home & furniture retailers, driving maximum value during mega sale events like Black Friday involves offering bundles and sets to meet customer demands and trend expectations. Gaining insights into competitor discounts and pricing can help furniture retailers get an edge amid this environment.
Want to know how DataWeave’s intelligence platform can empower your business during peak sales events? Contact us to discover more about competitive insights, price intelligence, and data-driven decision-making. Stay tuned to our blog to see more coverage on Black Friday 2024.
The U.S. health and beauty retail sector shows remarkable resilience amid economic uncertainties, with the skincare market projected to hit $21.83 billion in 2024. Black Friday data reinforces this trend, with health and beauty products seeing a 14.6% surge in web traffic compared to last year.
At DataWeave, we conducted an in-depth analysis of Black Friday discounting trends in the U.S. health and beauty sector. DataWeave’s AI-powered pricing intelligence platform was used to monitor pricing and discounts across Sephora, Ulta Beauty, Walmart, Target, and Amazon during Black Friday 2024. The study covered 19985 SKUs from November 10-29. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “skincare” and “fragrance”.
The results? Beauty leads across categories in discount depth this year, with some retailers offering significant markdowns.
The Beauty Boom: More Than Just Looking Good
If there’s one thing the pandemic taught us, it’s that self-care isn’t just a luxury – it’s a necessity. This Black Friday proved that beauty has become an indispensable part of consumers’ lives, with retailers offering unprecedented discounts and crafting strategic promotions to capture the growing demand.
The Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Ulta Beauty led with 45% average discounts, followed by Sephora at 38.1% and Walmart at 35.2%. In terms of additional Black Friday discounts, Ulta maintained dominance at 35%, with Sephora following at 28%.
Hair care emerged as the standout category, with Ulta Beauty offering up to 56% discounts, reflecting sustained demand for at-home beauty routines. Skincare saw fierce competition, with Sephora emphasizing premium discounts (37%) while Walmart focused on value pricing (32.5%).
Fragrance and Makeup attracted consumers with targeted promotions from Walmart and Ulta Beauty, signaling strong demand for gifting items.
Major beauty brands echoed the sentiment. Premium skincare brand Clinique leads with 50.6% average discounts. Meanwhile, drugstore staples like Revlon (29.1%) and Maybelline (24.4%) balanced accessibility and affordability, driving mass-market appeal. Popular beauty and makeup brand L’Oreal Paris also offered a modest 22.8% average discount, reinforcing its position as a value-oriented brand.
The more interesting story? The massive shift in brand visibility, as our share of search rankings denote:
Shampoo and hair care brand Tresemmé saw an unexpected 5.5% jump in the share of search results
Beauty brand Herbal Essences gained 5.1% in share of search well
Declines in share of search were noted for brands like L’Oreal Paris (-1.8%) and Pantene (-0.6%), indicating missed opportunities in promotional visibility.
Insight: What’s driving this beauty boom? TikTok and social media continue to fuel beauty purchases, with viral products driving significant search and sales spikes. Plus, the “skinification” of hair care has turned basic shampoo shopping into a full-blown beauty ritual.
Who Offered the Lowest Prices?
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 1133 matched products across Health & Beauty specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Bloomingdale’s emerges as the overall leader, offering the highest average discount of 14.87%, closely followed by Bluemercury (12.41%).
Ulta Beauty ranks third (10.94%), demonstrating competitiveness across key subcategories, while Sephora trails with the lowest average discount (7.33%), reflecting a more premium positioning.
Ulta Beauty leads in Hair Care with the highest discount (22.62%), while Bluemercury dominates in Skin Care (13.81%), Makeup (22.98%), and Fragrance (10.6%).
Sephora consistently offers the lowest discounts across all subcategories, reflecting their premium positioning.
Bluemercury offers the lowest prices for luxury brands like Kiehl (27.02%) and Laura Mercier (34.87%), with Bloomingdale’s closely trailing.
Bloomingdale’s leads for Bumble and Bumble (13.59%) and Hourglass (23.41%), showcasing strong promotional efforts.
Sephora maintains a more restrained discount strategy, with notable leadership only for Estée Lauder (7.18%).
Ulta Beauty shines in offering the steepest discount for Briogeo (33.26%), emphasizing competitiveness in key brands.
What’s Next for Holiday Discounting?
For retailers, the message is clear: traditional holiday playbooks need a serious update. For shoppers, it means unprecedented opportunities to score deals in categories that traditionally held firm on pricing.
Want to stay ahead of retail trends and optimize your holiday shopping strategy? DataWeave’s commerce intelligence platform helps brands and retailers strategically navigate these shifts. Contact us to learn more about how we can help you make data-driven decisions in this rapidly evolving retail landscape.
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, once confined to a single weekend, has evolved into a shopping season that now stretches well before Thanksgiving. With inflation hovering around 3% and consumer confidence showing signs of recovery, retailers are adapting their promotional calendars to capture early-bird shoppers and maintain a competitive edge.
Major retailers, including Amazon, Walmart, Target, and Best Buy, have capitalized on this trend by launching promotions weeks in advance, signaling the traditional holiday rush is now a month-long event. At DataWeave, we put these deals under a microscope.
Our Methodology
Using DataWeave’s advanced, AI-powered pricing intelligence platform, we tracked early Black Friday deals across Consumer Electronics, Home & Furniture, Health & Beauty, and Apparel categories. We monitored dedicated Black Friday deal pages on Amazon, Walmart, Target, Best Buy, Nordstrom, Neiman Marcus, and Sephora to gather and analyze discount data a week prior to Black Friday weekend.
Who’s Offering the Best Deals Across Categories?
Our pre- Black Friday analysis reveals a clear pattern of premium brands offering deeper discounts across categories ahead of the holiday. Here are some key findings around retail players:
Walmart emerges as the most aggressive discounter across categories, leading in Health & Beauty (57.07%), Apparel (48.97%), and Consumer Electronics (43.35%).
Amazon maintains consistent but lower discounts (28-29%) across categories, suggesting potential deeper cuts ahead.
Best Buy and Sephora, both category specialists, play it conservative compared to mass retail players.
Let’s look at each category more closely to get a detailed snapshot of the deals this Thanksgiving week:
Health & Beauty
Our analysis reveals that it’s not electronics, but the health & beauty category that leads with the widest discount range pre Black Friday, making it the category to watch out for.
Walmart takes the lead with an aggressive 57.1% average discount in this category, capitalizing on its value-oriented reputation.
Beauty specialist Sephora holds modest beauty discounts (32.81%) compared to other retailers.
Amazon offers the broadest range of SKUs (571) in the category.
Among the health & beauty brands we analyzed, cosmetics brand Tarte and viral K-Beauty skincare brand COSRX stand out with discounts above 40%, appealing to cost-conscious beauty enthusiasts.
Consumer Electronics
Our pre- Black Friday analysis reveals interesting insights about consumer electronics deals this season.
Walmart, once again, emerges as the frontrunner in the category with 43.4% average discounts.
Best Buy plays it conservative in electronics (30.75%), despite being a category specialist, but offers the most extensive SKU coverage (3030).
Amazon’s consistent 29.7% discount across 1,749 SKUs suggests they’re probably holding back their best deals for Prime members during Black Friday.
Brand-specific data for the category reveals significant deals on Speck (48.07%) and smart TV brand Insignia (39.22%), making accessories and mid-tier electronics attractive for early shoppers. Core computing (HP at 32.14%) and electronics brands maintain more conservative discounts. It remains to be seen if this changes on Black Friday or Cyber Monday.
Apparel
Our analysis of the apparel category reveals several highlights:
In the apparel category too, Walmart dominates with an impressive 49% average discount, effectively targeting price-sensitive shoppers in the fashion segment.
Nordstrom and Neiman Marcus, both known for apparel, offer significant discounts at 43.2% and 37.8% respectively.
Amazon’s expansive SKU coverage (1344) is countered by a modest 29.5% discount, showing its focus on variety over depth of discounts.
Premium fashion brands dominate the highest discounts this Black Friday in the apparel category. Vince Camuto leads with over 45.1% average discount. Notably, Levi and Nike’s aggressive 44.43% and 43.50% discounts suggests significant inventory positions or intent to capture market share.
Home & Furniture
Our analysis reveals an interesting trend across the category.
In the home & furniture category too, Walmart leads at 41.8% average discounts. Target follows closely, but with significantly lesser SKUs on offer.
Amazon’s 28.1% discount, though the lowest among major players, spans a substantial 1,982 SKUs, reinforcing its position as a marketplace for diverse needs.
Top 3 Products With the Highest Discounts Across Retailers
To provide a clearer picture of the early Black Friday landscape, we analyzed the top 3 products with the most substantial discounts in consumer electronics and health & beauty categories. These insights highlight how retailers are leveraging strategic discounts on high-value items to attract early shoppers.
Top Discounted Products in Consumer Electronics
Premium TVs dominate the discount scene, with LG’s 83″ OLED offering up to 44.5% off on Amazon, closely followed by a 44.4% discount on Best Buy, showcasing aggressive competition. The same product has much lower discounting on Walmart, but notably, the product is retailed at $3999.9, at least $1000 less than other retailers, highlighting Walmart’s commitment to offering lowest prices.
Gaming consoles, like the PlayStation 5 Slim Bundle, show moderate discounts (ranging from 15% on Walmart and Target to 25% at Best Buy), appealing to tech-savvy shoppers.
Notable competition is evident in price matching across major retailers, particularly in TVs and high-value electronics like the Nikon Z 8 camera, where Walmart offers the deepest discount at 13.75%, edging past Amazon and Best Buy.
Top Discounted Products in Health & Beauty
Viral skincare staples like Tatcha’s Water Cream show tight discounting consistency, with Walmart offering 19.47% off compared to Amazon’s 20% and Sephora’s 20.83%.
Trending haircare brand Olaplex displays greater disparity, with Walmart leading with a 33.33% discount, surpassing Amazon and Sephora. Luxury brand, Yves Saint Laurent’s Satin Lipstick is one of the highest discounted items across retailers.
Looking Ahead
Our analysis suggests that while some early deals offer genuine value, particularly in premium beauty and high-end electronics, many retailers might be holding their best discounts for Black Friday.
For shoppers, the key is being selective: jump on premium brand discounts now (since they’re likely to remain the same though the weekend), but wait on mid-range electronics and home goods where better deals are likely to emerge on Black Friday or Cyber Monday.
For retailers, the imperative is clear: dynamic pricing intelligence is crucial for maintaining a competitive edge while protecting margins. Competitive insights will be critical as the holiday season progresses to balance market share against profitability.
Stay tuned for our Black Friday Cyber Monday analysis next week, where we’ll track how these early discounts compare to the main event’s deals!
In online retail, success hinges on more than just offering quality products at competitive prices. As eCommerce catalogs expand and consumer expectations soar, businesses face an increasingly complex challenge: How do you effectively organize, categorize, and present your vast product assortments in a way that enhances discoverability and drives sales?
Having complete and correct product catalog data is key. Effective product attribute tagging—a crucial yet frequently undervalued capability—helps in achieving this accuracy and completeness in product catalog data. While traditional methods of tagging product attributes have long struggled with issues of scalability, consistency, accuracy, and speed, a new thinking and fundamentally new ways of addressing these challenges are getting established. These follow from the revolution brought in Large Language Models but they fashion themselves as Small Language Models (SLM) or more precisely as Domain Specific Language Models. These can be potentially considered foundational models as they solve a wide variety of downstream tasks albeit within specific domains. They are a lot more efficient and do a much better job in those tasks compared to an LLM. .
Retail Domain Language Models (RLMs) have the potential to transform the eCommerce customer journey. As always, it’s never a binary choice. In fact, LLMs can be a great starting point since they provide an enhanced semantic understanding of the world at large: they can be used to mine structured information (e.g., product attributes and values) out of unstructured data (e.g., product descriptions), create baseline domain knowledge (e.g, manufacturer-brand mappings), augment information (e.g., image to prompt), and create first cut training datasets.
Powered by cutting-edge Generative AI and RLMs, next-generation attribute tagging solutions are transforming how online retailers manage their product catalog data, optimize their assortment, and deliver superior shopping experiences. As a new paradigm in search emerges – based more on intent and outcome, powered by natural language queries and GenAI based Search Agents – the capability to create complete catalog information and rich semantics becomes increasingly critical.
In this post, we’ll explore the crucial role of attribute tagging in eCommerce, delve into the limitations of conventional tagging methods, and unveil how DataWeave’s innovative AI-driven approach is helping businesses stay ahead in the competitive digital marketplace.
Why Product Attribute Tagging is Important in eCommerce
As the eCommerce landscape continues to evolve, the importance of attribute tagging will only grow, making it a pertinent focus for forward-thinking online retailers. By investing in robust attribute tagging systems, businesses can gain a competitive edge through improved product comparisons, more accurate matching, understanding intent, and enhanced customer search experiences.
Taxonomy Comparison and Assortment Gap Analysis
Products are categorized and organized differently on different retail websites. Comparing taxonomies helps in understanding focus categories and potential gaps in assortment breadth in relation to one’s competitors: missing product categories, sizes, variants or brands. It also gives insights into the navigation patterns and information architecture of one’s competitors. This can help in making search and navigation experience more efficient by fine tuning product descriptions to include more attributes and/or adding additional relevant filters to category listing pages.
For instance, check out the different Backpack categories on Amazon and Staples in the images below.
Or look at the nomenclature of categories for “Pens” on Amazon (left side of the image) and Staples (right side of the image) in the image below.
Assortment Depth Analysis
Another big challenge in eCommerce is the lack of standardization in retailer taxonomy. This inconsistency makes it difficult to compare the depth of product assortments across different platforms effectively. For instance, to categorize smartphones,
Retailer A might organize it under “Electronics > Mobile Phones > Smartphones”
Retailer B could use “Technology > Phones & Accessories > Cell Phones”
Retailer C might opt for “Consumer Electronics > Smartphones & Tablets”
Inconsistent nomenclature and grouping create a significant hurdle for businesses trying to gain a competitive edge through assortment analysis. The challenge is exacerbated if you want to do an in-depth assortment depth analysis for one or more product attributes. For instance, look at the image below to get an idea of the several attribute variations for “Desks” on Amazon and Staples.
Custom categorization through attribute tagging is essential for conducting granular assortment comparisons, allowing companies to accurately assess their product offerings against those of competitors.
Enhancing Product Matching Capabilities
Accurate product matching across different websites is fundamental for competitive pricing intelligence, especially when matching similar and substitute products. Attribute tagging and extraction play a crucial role in this process by narrowing down potential matches more effectively, enabling matching for both exact and similar products, and tagging attributes such as brand, model, color, size, and technical specifications.
For instance, when choosing to match similar products in the Sofa category for 2-3 seater sofas from Wayfair and Overstock, tagging attributes like brand, color, size, and more is a must for accurate comparisons.
Taking a granular approach not only improves pricing strategies but also helps identify gaps in product offerings and opportunities for expansion.
Fix Content Gaps and improve Product Detail Page (PDP) Content
Attribute tagging plays a vital role in enhancing PDP content by ensuring adherence to brand integrity standards and content compliance guidelines across retail platforms. Tagging attributes allows for benchmarking against competitor content, identifying catalog gaps, and enriching listings with precise details.
This strategic tagging process can highlight missing or incomplete information, enabling targeted optimizations or even complete rewrites of PDP content to improve discoverability and drive conversions. With accurate attribute tagging, businesses can ensure each product page is fully optimized to capture consumer attention and meet retail standards.
Elevating the Search Experience
In today’s online retail marketplace, a superior search experience can be the difference between a sale and a lost customer. Through in-depth attribute tagging, vendors can enable more accurate filtering to improve search result relevance and facilitate easier product discovery for consumers.
By integrating rich product attributes extracted by AI into an in-house search platform, retailers can empower customers with refined and user-friendly search functionality. Enhanced search capabilities not only boost customer satisfaction but also increase the likelihood of conversions by helping shoppers find exactly what they’re looking for more quickly and with minimal effort.
Pitfalls of Conventional Product Tagging Methods
Traditional methods of attribute tagging, such as manual and rule-based systems, have been significantly enhanced by the advent of machine learning. While these approaches may have sufficed in the past, they are increasingly proving inadequate in the face of today’s dynamic and expansive online marketplaces.
Scalability
As eCommerce catalogs expand to include thousands or even millions of products, the limitations of machine learning and rule-based tagging become glaringly apparent. As new product categories emerge, these systems struggle to keep pace, often requiring extensive revisions to existing tagging structures.
Inconsistencies and Errors
Not only is reliance on an entirely human-driven tagging process expensive, but it also introduces a significant margin for error. While machine learning can automate the tagging process, it’s not without its limitations. Errors can occur, particularly when dealing with large and diverse product catalogs.
As inventories grow more complex to handle diverse product ranges, the likelihood of conflicting or erroneous rules increases. These inconsistencies can result in poor search functionality, inaccurate product matching, and ultimately, a frustrating experience for customers, drawing away the benefits of tagging in the first place.
Speed
When product information changes or new attributes need to be added, manually updating tags across a large catalog is a time-consuming process. Slow tagging processes make it difficult for businesses to quickly adapt to emerging market trends causing significant delays in listing new products, potentially missing crucial market opportunities.
How DataWeave’s Advanced AI Capabilities Revolutionize Product Tagging
Advanced solutions leveraging RLMs and Generative AI offer promising alternatives capable of overcoming these challenges and unlocking new levels of efficiency and accuracy in product tagging.
DataWeave automates product tagging to address many of the pitfalls of other conventional methods. We offer a powerful suite of capabilities that empower businesses to take their product tagging to new heights of accuracy and scalability with our unparalleled expertise.
Our sophisticated AI system brings an advanced level of intelligence to the tagging process.
RLMs for Enhanced Semantic Understanding
Semantic Understanding of Product Descriptions
RLMs analyze the meaning and context of product descriptions rather than relying on keyword matching. Example: “Smartphone with a 6.5-inch display” and “Phone with a 6.5-inch screen” are semantically similar, though phrased differently.
Attribute Extraction
RLMs can identify important product attributes (e.g., brand, size, color, model) even from noisy or unstructured data. Example: Extracting “Apple” as a brand, “128GB” as storage, and “Pink” as the color from a mixed description.
Identifying Implicit Relationships
RLMs find implicit relationships between products that traditional rule-based systems miss. Example: Recognizing that “iPhone 12 Pro” and “Apple iPhone 12” are part of the same product family.
Synonym Recognition in Product Descriptions
Synonym Matching with Context
RLMs identify when different words or phrases describe the same product. Examples: “Sneakers” = “Running Shoes”, “Memory” = “RAM” (in electronics) Even subtle differences in wording, like “rose gold” vs “pink” are interpreted correctly.
Overcoming Brand-Specific Terminology
Some brands use their own terminologies (e.g., “Retina Display” for Apple). RLMs can map proprietary terms to more generic ones (e.g., Retina Display = High-Resolution Display).
Dealing with Ambiguities
RLMs analyze surrounding text to resolve ambiguities in product descriptions. Example: Resolving “charger” to mean a “phone charger” when matched with mobile phones.
Contextual Understanding for Improved Accuracy and Precision
By leveraging advanced natural language processing (NLP), DataWeave’s AI can process and understand the context of lengthy product descriptions and customer reviews, minimizing errors that often arise at human touch points. The solution processes and interprets information to extract key information to dramatically improve the overall accuracy of product tags.
It excels at grasping the subtle differences between similar products, sizes, colors and identifying and tagging minute differences between items, ensuring that each product is uniquely and accurately represented in a retailer’s catalog.
This has a major impact on product and similarity-based matching that can even help optimize similar and substitute product matching to enhance consumer search. At the same time, our AI can understand that the same term might have different meanings in various product categories, adapting its tagging approach based on the specific context of each item.
This deep comprehension ensures that even nuanced product attributes are accurately captured and tagged for easy discoverability by consumers.
Case Study: Niche Jewelry Attributes
DataWeave’s advanced AI can assist in labeling the subtle attributes of jewelry by analyzing product images and generating prompts to describe the image. In this example, our AI identifies the unique shapes and materials of each item in the prompts.
The RLM can then extract key attributes from the prompt to generate tags. This assists in accurate product matching for searches as well as enhanced product recommendations based on similarities.
This multi-model approach provides the flexibility to adapt as product catalogs expand while remaining consistent with tagging to yield more robust results for consumers.
Unparalleled Scalability
DataWeave can rapidly scale tagging for new categories. The solution is built to handle the demands of even the largest eCommerce catalogs enabling:
Effortless management of extensive product catalogs: We can process and tag millions of products without compromising on speed or accuracy, allowing businesses to scale without limitations.
Automated bulk tagging: New product lines or entire categories can be tagged automatically, significantly reducing the time and resources required for catalog expansion.
Normalizing Size and Color in Fashion
Style, color, and size are the core attributes in the fashion and apparel categories. Style attributes, which include design, appearance, and overall aesthetics, can be highly specific to individual product categories.
Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification. By leveraging advanced technology to identify and normalize identical and similar products from competitors, you can optimize your pricing strategy and product assortment to remain competitive. Using Generative AI in normalizing color and size in fashion is key to powering competitive pricing intelligence at DataWeave.
Continuous Adaptation and Learning
Our solution evolves with your business, improving continuously through feedback and customization for retailers’ specific product categories. The system can be fine-tuned to understand and apply specialized tagging for niche or industry-specific product categories. This ensures that tags remain relevant and accurate across diverse catalogs and as trends emerge.
The AI in our platform also continuously learns from user interactions and feedback, refining its tagging algorithms to improve accuracy over time.
Stay Ahead of the Competition With Accurate Attribute Tagging
In the current landscape, the ability to accurately and consistently tag product attributes is no longer a luxury—it’s essential for staying competitive. With advancements in Generative AI, companies like DataWeave are revolutionizing the way product tagging is handled, ensuring that every item in a retailer’s catalog is presented with precision and depth. As shoppers demand a more intuitive, seamless experience, next-generation tagging solutions are empowering businesses to meet these expectations head-on.
DataWeave’s innovative approach to attribute tagging is more than just a technical improvement; it’s a strategic advantage in an increasingly competitive market. By leveraging AI to scale and automate tagging processes, online retailers can keep pace with expansive product assortments, manage content more effectively, and adapt swiftly to changes in consumer behavior. In doing so, they can maintain a competitive edge.
When egg prices surged 70% during the 2023 avian flu outbreak, grocery retailers faced a critical dilemma: maintain margins and risk losing customers, or absorb costs and watch profits evaporate. Similarly, rising olive oil and chocolate prices also had domino effects, cascading down from retailers to consumers. In each of these scenarios, those with sophisticated pricing intelligence systems adapted swiftly, finding the sweet spot between competitiveness and profitability. Others weren’t so fortunate.
This scenario continues to play out daily across thousands of products in the grocery sector. From breakfast cereals to fresh produce to bottled water, retailers must orchestrate pricing across a variety of categories – each with its own competitive dynamics, margin requirements, and price sensitivity patterns.
The Evolution of Grocery Pricing Intelligence
Imagine these scenarios in the grocery industry:
Milk prices spike during a supply shortage.
Your competitor drops egg prices by 20%.
Fresh produce costs fluctuate with an unseasonable frost.
For grocery retailers, these aren’t occasional challenges—they’re Tuesday. Reacting to each pricing crisis as it comes isn’t just exhausting—it’s a recipe for shrinking margins and missed opportunities.
Think of it this way: If you’re constantly playing defense with your pricing strategy, you’re already two steps behind. Commoditized items like milk and eggs face intense price competition, while seasonal products and fresh produce demand constant attention. Simply matching competitor prices or adjusting for cost changes isn’t enough anymore. What’s needed is a proactive approach that anticipates market shifts before they happen and turns pricing challenges into competitive advantages. This is where price management comes in.
Price management has transformed from simple competitor checks into a strategic power play that can make or break a retailer’s market position. Weekly manual adjustments have given way to a long-term strategic view, driven by data analytics and market intelligence. Here are the basics of how price management in grocery retail works today.
Three Pillars of Grocery Price Management
1. Smart Data Collection: Building Your Foundation
The journey begins with comprehensive data collection and storage across your entire product ecosystem. This means:
Complete Coverage Of All SKUs Across All Stores: Tracking prices for all SKUs across all stores, with particular attention to high-velocity items and volatile categories.
Dynamic Monitoring: Tracking prices across different time frequencies as grocery prices are highly volatile for different categories. So daily tracking for volatile items like dairy and produce, and weekly for more stable categories may be needed.
Competitive Intelligence: Gathering data not just on prices, but on promotions, pack sizes, and private label alternatives.
Infrastructure to Support Large Volumes of Data: Partnering with external data and analytics providers to bridge the gap when retailers struggle with the scale of digital infrastructure these data sets require.
2. Intelligent Data Refinement: Making Sense of the Numbers
Raw data alone isn’t enough—it needs context and structure to become actionable intelligence. This is called Data Refinement—the process of establishing meaningful relationships within the data to facilitate the extraction of valuable insights. This refinement stage is closely tied to the data collection strategy, as the quality and depth of the insights derived depend on the accuracy and coverage of the collected data.
Data refinement includes several key processes:
Advanced Product Matching
Picture this: You’re tracking a competitor’s pricing on organic apples. Simple, right? Not quite. Yes, Universal Product Codes (UPCs) and Price Lookup Codes (PLUs) are present in Grocery to standardize product identification across different retailers—unlike the fashion industry’s endless style variations. Still, product matching isn’t as straightforward as scanning barcodes.
Here’s the catch: many retailer websites don’t display them. Then there’s the private label puzzle—your “Store’s Best” organic apples need to match against competitors’ house brands, each with their own unique UPC. Throw in different sizes (4 Apples vs. 1Kg of Apples), regional product names (fancy naming for plain old arugula), and international brand variations (like the name for Sprite in the USA and China), and you’ve got yourself a complex matching challenge that would make conventional pricing intelligence providers sweat.
Custom Product Relationships for Consistent Pricing and Competitive Positioning
Think like a shopper browsing the dairy aisle. You regularly buy your family’s favorite organic yogurt, the 24oz tub. But today, you notice the larger 32oz size is on sale – except the 24oz isn’t. As you stand there, confused, you wonder: Is the sale only for the bigger size? Did I miss a promotion? Should I buy the 32oz even though it’s more than I need?
For shoppers, this inconsistent pricing across product variations creates a frustrating experience. Establishing clear relationships between related items in your catalog is essential for maintaining consistent pricing and a coherent competitive strategy.
Start by linking products based on attributes like size, brand, and packaging. That way, when you adjust the price of the 32oz yogurt, the 24oz version automatically updates too – no more scrambling to ensure uniform pricing across your assortment. Similarly, products of the same brand but with flavor variations should be connected to keep pricing consistent.
Taking this one step further, mapping your competitors’ exact and similar products is crucial for comprehensive competitive intelligence. Distinguishing between premium and private label tiers, national brands, and regional players gives you a holistic view of the landscape. With this understanding, you can hone your pricing strategies to maintain a clear, compelling position across your entire category lineup.
Consistent pricing, whether across your own product variations or against competitors, provides clarity and accuracy in your overall competitive positioning. By establishing these logical connections, you avoid the customer confusion of seemingly random, inconsistent discounts – and ensure your pricing strategies work in harmony, not disarray.
The Role of AI and Data Sciences in Data Refinement
On the surface, linking products based on attributes like size, brand, and packaging seems like a no-brainer. But developing and maintaining the systems to accurately and automatically identify these connections? That’s a whole different animal.
Think about it – you’re not just dealing with text-based product titles and UPCs. There are images, videos, regional variations, private labels, and a whole host of other data types and industry nuances to account for.
Luckily, DataWeave is one of the few companies that’s truly cracked the code. Our multimodal AI models are trained to process all those diverse data formats – from granular product specs to zany regional produce names. And it’s not just about technology; we also harness the power of human intelligence.
See, in the grocery world, category managers are the real decision makers. They know their shelves inside and out and can spot those tricky connections in product matching, especially when they are not UPC-based. That’s why DataWeave built in a Human-in-the-Loop (HITL) process, where their AI systems continuously learn from expert feedback. It’s a feedback loop that allows our customers to pitch in and keep product relationships accurate, reliable, and always adapting to new market realities.
So while product mapping may seem straightforward on the surface, the reality is it takes some serious horsepower to do it right. Thankfully, DataWeave has both the technical chops and the grocery industry know-how to make it happen. Because when it comes to pricing intelligence, getting those product connections right is half the battle.
3. Strategic Implementation: Turning Insights into Action
The true value of pricing intelligence (PI) is realized through its strategic application. Although many view PI as a technical function, its strategic significance is increasing, particularly in the context of recent economic pressures like inflation. Here’s why:
Tactical vs Strategic Use of Data: From Standard Reporting to Competitive Analysis
Pricing intelligence has come a long way from the days of simply reacting to daily price changes. These days, it’s not just about firefighting—it’s about driving long-term strategy.
You can use pricing data to make quick, tactical adjustments, like matching a competitor’s sudden price drop on milk. Or, you can leverage that same data to predict market trends, optimize your product lineup, and shape your overall pricing strategy. Retailers who take that strategic view can get out ahead of the curve, anticipating shifts instead of just chasing them.
DataWeave supports both of these approaches. Our Standard Reporting tools give pricing managers the nitty-gritty details they need—current practices, historical patterns, and operational KPIs. It’s all the insights you’d expect for making those tactical, day-to-day tweaks.
In addition, DataWeave offers something more powerful: Competitive analysis. This is where pricing intelligence becomes a true strategic weapon. By providing a high-level view of market positioning, competitor moves, and untapped opportunities, competitive analysis empowers leadership to make proactive, big-picture decisions.
Armed with this broader perspective, retailers can start taking a more surgical approach. Maybe you need to adjust pricing zones to better meet customer demands. Or rethink your overall strategies to stay ahead of the competition, not just keep pace. It’s the difference between constantly putting out fires and systematically fortifying your entire pricing fortress.
Beyond Pricing: Comprehensive Data for Broader Insights
Pricing intelligence is just the tip of the iceberg. When you really start to refine and harness your data, the possibilities for grocery retailers expand far beyond simple price comparisons. Think about it – all that information you’re collecting on products, markets, and consumer behavior? That’s a goldmine waiting to be tapped. Sure, you can use it to keep a pulse on competitor pricing. But why stop there?
What if you could leverage that data to optimize your product assortment, making sure you’re stocking the right mix to meet customer demands? Or tap into predictive analytics to get a glimpse of future market shifts, so you can get out ahead of the curve? How about using it to streamline your supply chain, identify availability inefficiencies, and get products to shelves faster?
Sure, pricing intelligence will always be mission-critical. But when you couple it with these other data-driven insights, that’s when grocery retailing gets really interesting. It’s about evolving from a price-matching robot to a true strategic visionary, armed with the intelligence to take your business to new heights.
Looking Ahead: The Future of Grocery Pricing Intelligence
The grocery pricing landscape continues to evolve, driven by:
Integration of AI and machine learning for predictive pricing
Enhanced focus on omnichannel pricing consistency
Growing importance of personalization in pricing strategies
Pricing intelligence isn’t just about having data—it’s about having the right data and knowing how to use it strategically. Success requires a comprehensive approach that combines robust data collection, sophisticated analysis, and strategic implementation.
By embracing modern pricing intelligence tools and strategies, grocery retailers can navigate market volatility, maintain competitive positioning, and drive sustainable growth. The key lies in building a pricing ecosystem that’s both sophisticated enough to handle complex data and flexible enough to adapt to changing market conditions.
Ready to transform your pricing strategy? Check out our grocery price tracker to get month-on-month updates on grocery prices in the real world. Contact us to learn how our advanced pricing intelligence solutions can help your business stay ahead in the competitive grocery market.
Today, the first name that comes to anybody’s mind when they hear about online shopping is Amazon. In the US alone, Amazon accounted for over 37.6 percent of total online retail sales in 2023 with the second place Walmart not even managing to win double-digit numbers on the same scale.
With such a phenomenal market share, it is not surprising that any retail brand would want to have their products listed on Amazon for sale. However, as enticing as the potential exposure could be, the overwhelming presence of brands selling similar products on Amazon is so huge that getting fair visibility for your products may require some heavy-lifting support.
Will the Same SEO You Use for Google Work with Amazon?
Unfortunately, no, as Google and Amazon have different objectives when it comes to search rankings on their respective customer platforms. Google makes the lion’s share of its revenue from search advertising, whereas Amazon makes money when customers buy products listed on its platform by sellers.
Relying on traditional search engine optimization (SEO) techniques may not get the desired results as they are more optimized for search engines like Google. Amazon embraces its unique DNA when it comes to product display rankings on its search option.
How Does SEO Work in Amazon?
Over the years, Amazon amassed data about shopping experiences that billions of customers globally had on its platform. With this data, they developed their custom search algorithm named A9. Contrary to the gazillion objectives that Google has for its intelligent search algorithms, Amazon has tasked A9 with just a simple straightforward target—when a customer keys in a search query, provide the best choice of products that they will most probably purchase, as search results.
A9 works to fulfill the mission of guiding shoppers to the right product without worrying about semantics, context, intent, mind mapping, etc. of the search query in contrast to what Google does. As with Google search, Amazon does have paid advertising and sponsored results options such as Amazon PPC, Headline ads, etc. but their SEO algorithms are aware of how to support and boost search rankings of genuine products and brands that have taken an effort to follow best practices in Amazon SEO as well as have a great offering with attractive prices.
As additional knowledge, Amazon also has clear guidelines on what it prioritizes for search rankings. Known in the SEO world as Amazon ranking signals, these are core factors that influence how a product is ranked for search queries. Some of the top Amazon ranking signals that carry heavy influence on search rankings include on-page signals, off-page signals, sales rank, best sellers rank, etc.
What Brands Need to Strategize to Master the Amazon SEO Algorithms
From a broad perspective, we can classify the actions brands need to take in this regard in 3 core stages:
Pre-Optimization
This deals with getting first-hand knowledge about both customers who are likely to purchase your product and the competitors who are vying for sales from these very same customers. Filtering your target customer or audience is essential to ensure that you get the most ROI from marketing initiatives and that sales cycles are accelerated. For example, if your product is a premium scented candle, there is no point in wasting advertising dollars trying to win attention from customers who are not likely to ever spend on luxury home décor items.
Knowing how your competitors are performing on Amazon search, the keywords, and SEO strategies they have adapted is critical to ensure that you stay one step ahead.
Product Listing Page Optimization
This includes strategies that a brand can adopt so that its product description page gets the much-needed content optimizations to sync with Amazon’s A9 algorithm. It has a mix of keyword-integrated content, relevant images, descriptions in easy-to-understand language, localized content flavors to resonate with target buyers, etc. For example, a kitchen tool like a grater might be used for different kinds of food preparation techniques in different regions of the same country.
The brand must ensure that the description adequately localizes the linguistic or usage preference representation of the target audience. If the grater is used for grating coconut shells to extract the fibrous pulp in the Midlands and for grating ginger skin in the Far East, both use cases should be part of the product description if the target customers are from both regions.
Sales Optimization
This deals with options that have more sales strategies integrated into their core. For example, blogs on popular websites with the Amazon purchase link embedded in the content, collaboration with social media influencers, paid advertising on Amazon itself as well as on search engines, video ads, banner and display ads, etc.
The key intent here is to drive organic and inorganic traffic to the Amazon product listing page and ultimately win sales.
How Can Your Products Rank High in Amazon Search Results? Top 10 Tactics
Now that you have a clear understanding of the strategies that help in mastering Amazon’s ranking algorithms, here are some great tips to help achieve higher search rankings for your products on Amazon search:
1. Target Relevant Keywords
You need to figure out the best keywords that match what customers put as queries into the Amazon search bar. Your brand needs to clearly understand customer behavior when they arrive on Amazon to search for a product or category of products. The best place to begin looking for the same would be on competitor pages on Amazon. The keywords that helped them rank well on Amazon can help you as well. Manually investigating such a large pool of competitors is nearly impossible but with the right tools, you can easily embrace capabilities to know which keywords can help you in mimicking the success of your competitors.
2. Focus on Product Titles
Every single part of the content in your brand’s Amazon storefront or product page needs dedicated focus. Beginning with the product titles, effort needs to be made to ensure that they include the brand name, key product category or features, and other relevant keyword information.
In other words, product titles must be optimized for searchability. This searchability for product titles needs to be optimized for both mobile and desktop screens.
3. Create Product Descriptions that Resonate with the Audience
For product descriptions on your Amazon webpage, you need to figure out the optimal quality levels needed for the intended audience. Effective content can help achieve better search ranking visibility and convince the incoming traffic of shoppers to make a purchase. It is important to periodically review and modify your page content to suit the interests of visitors from both web and mobile devices.
Leveraging solutions like DataWeave can help with regular content audits to ensure you are putting out the best product content that will delight shoppers and deliver on sales conversion targets.
4. Use High-Quality Media Assets like Images and Videos
Promoting your product doesn’t have to be restricted to just textual content in Amazon product description sections. You can use other multimedia assets of high quality. These include images, videos, brochure images, etc. Every content asset must aim to educate shoppers on why your product should be their number one choice. For example, look at this detailed product description for the viral K-Beauty product COSRX Mucin Essence.
Moreover, images can help attract more attention span from visitors, thereby increasing the probability of purchases.
5. Strengthen the Backend Keywords As Well
Amazon also supports hidden backend keywords that sellers add to their product listings. They help add more relevance to products similar to meta descriptions and titles in traditional SEO for search engines like Google. A typical backend keyword may comprise synonyms, misspelled keywords, textual variations, etc. However, knowing how to pick the right ones is crucial. By analyzing your keyword rankings against competitors and higher-ranking product results in search, the platform can help you consistently optimize your content backend to help grow visibility.
6. Focus on Reviews and Ratings
Reviews and ratings on product pages are key insights that help customers with their purchasing decisions. So, it is natural for brands to keep a close eye on how their products are faring in this regard. Reviews and ratings are a direct indication of the trustworthiness of your product. When previous buyers rate you high and leave favorable reviews on your product, it will directly promote trust and help you secure a better rapport with new customers.
This upfront advantage can help boost sales conversions better. Leveraging solutions like DataWeave can help you understand the sentiments that customers have for your products by intelligently analyzing reviews and ratings.
7. Implement Competitive Pricing Strategies
The goal of most customers when shopping online is to get their desired product at the most affordable prices. The eCommerce price wars every year are growing in scale today and getting your product pricing right is crucial for sales. However, there is a need to gain comprehensive insights into how your competitors are pricing their offerings and how the market responds to specific price ranges. Solutions like DataWeave help your brand access specific insights into pricing. By analyzing competitor pricing, you can create a winning price model that is sustainable for your brand and favorable for target customers.
8. Track Share of Search
Content and other SEO activities will help improve your search rankings on Amazon. However, it is equally important to know how well your products are performing periodically against your competitors for the same set of specific keyword searches. You need to understand the share of search that your products are achieving to formulate improvement strategies. DataWeave’s Digital Shelf Analytics solution provides share of search insights helping you uncover deep knowledge on your discoverability on Amazon (and other marketplaces) for your vital search keywords.
9. Ensure Stock Availability
To achieve better ranking results, brands need to ensure that the relevant products matching the search keywords are available for quick delivery at the desired ZIP codes where users are more likely to search and order them. Out-of-stock items seldom show up high on search results. Certain products, especially if they’re popular, can get stocked out frequently in certain locations. Keeping a close eye on your stock availability across the map can help minimize these scenarios.
10. Optimize Your Brand Presence
While optimizing content and other key areas within the Amazon webpage for your product is critical, there are other avenues to help boost search rankings. One such option includes registering in the Amazon Brand Registry, which provides more beneficial features like protection against counterfeits and ensuring that your brand page is optimized according to Amazon storefront standards.
The Bottom Line
Winning the top spot in Amazon search ranking is crucial for brands that aim to capitalize on online sales revenue to grow their business. Knowing your workaround for Amazon’s proprietary SEO frameworks and algorithms is the first step to succeeding. The key element of success is your ability to gain granular insights into the areas we covered in this blog post such as competitor prices, sentiments of customers, market preferences, and content optimization requirements.
This is where DataWeave’s Digital Shelf Analytics solution becomes the biggest asset for your eCommerce business. Contact us to explore how we can empower your business to build the most visible and discoverable Amazon storefront that guarantees higher search rankings and ultimately increased sales. Talk to us for a demo today.
Fashion is as dynamic a market as any—and more competitive than most others. Consumer trends and customer needs are always evolving, making it challenging for fashion and apparel brands to keep up.
Despite the inherent difficulties fashion and apparel sellers face, this industry is one of the largest grossing markets in the world, estimated at $1.79 trillion in 2024. Global revenue for apparel is expected to grow at an annual rate of about 3.3% over the next four years. That means companies in this space stand to make significant revenue if they can competitively price their products, keep up with the competition, and win customer loyalty with consistent product availability.
There are three main categories in fashion and apparel. These include:
Apparel and clothing (i.e., shirts, pants, dresses, and other apparel)
Footwear (i.e., sneakers, sandals, heels, and other products)
Accessories (i.e., bags, belts, watches, and so on)
If you look at all of these product types across all sorts of retailers, there is a massive amount of overlapping data based on product attributes like style and size that are difficult to normalize.
Fashion Attributes
Style, color, and size are the main attribute categories in fashion and apparel. Style attributes include things like design, look, and overall aesthetics of the product. They’re very dependent on the actual product category of fashion as well. A shirt might have a slim fit attribute associated with it, whereas a belt might have a length. All these different attributes are usually labeled within a product listing and affect the consumer’s decision-making process:
Color (red, blue, sea green, etc.)
Pattern (solid, striped, checked, floral, etc.)
Material (cotton, polyester, leather, denim, silk, etc.)
Fit (regular, slim, relaxed, oversized, tailored, etc.)
Type (casual, formal, sporty, vintage, streetwear)
Color Complexity in Fashion
Color is perhaps the most visually distinctive attribute in fashion, yet it presents unique challenges for retailers. This is because color naming can vary across retailers and marketplaces. There are several major differences in color convention:
A single color can be labeled differently across brands (e.g., “navy,” “midnight blue,” “deep blue”)
Seasonal color names (e.g., “summer sage” vs. “forest green”)
Marketing-driven names (e.g., “sunset coral” vs. “pale orange”)
Size: The Other Critical Dimension
Size in fashion refers to the dimensions or measurements that determine how fashion products fit. Depending on whether the product is a clothing item, shoes, or a hat, there will be different sizing options. Types of sizes include:
Standard sizes (XS, S, M, L, XL, XXL, XXL)
Custom sizes (based on brand, retailer, country, etc.)
A single type of product may have different sizing labels. For instance, one pants listing may use traditional S, M, L, XL sizing, while another pants listing may use 24, 25, or 26, to refer to the waist measurement.
Size is a dynamic attribute that changes based on current trends. For example, there has recently been a significant shift towards inclusive sizing. Size inclusivity refers to the practice of selling apparel in a wide range of sizes to accommodate people of all body types. Consumers are more aware of this trend and are demanding a broader range of sizing offerings from the brands they shop from.
In the US market, in particular, some 67% of American women wear a size 14 or above and may be interested in purchasing plus-size clothing. There is a growing demand in the plus-size market for more options and a wider selection. Many brands are considering expanding their sizes to accommodate more shoppers and tap into this growing revenue channel.
Pricing Based on Size and Color
Many fashion products are priced differently based on size and color. Let’s take a look at an example of what this can look like.
A popular beauty brand (see image) is known for its viral lip tint. While most of the color variants are priced at $9.90 on Amazon, a specific colorway option, featuring less pigmented options, is priced at $9.57. This price differential is driven by both material costs and market demand.
Different colorways (any of a range of combinations of colors in which a style or design is available) of the same product often command different prices also. This is based on:
Dye costs (some colors require more expensive processes)
Seasonal demand (traditional colors vs. trend colors)
Exclusivity (limited edition colors)
An example of price variations by size is a women’s shirt that is being sold on Amazon as shown below. For this product, there are no style attributes to choose from. The only parameter the shopper has to select is the size they’d like to purchase. They can choose from S to XL. On the top, we can see that the product in size S is ₹389. Below, the size XL version of this same shirt is ₹399. This price increase is correlated to the change in size.
So why are these same products priced differently? In an analysis of One Six, a plus-size clothing brand, several reasons for this difference in plus-size clothing were determined.
Extra material is needed, hence an increase in production costs
Extra stitching costs, hence an increase in production costs
Production of plus-size clothing often means acquiring specialized machinery
Smaller scale production runs for plus-size clothing means these initiatives often don’t benefit from cost savings
Some sizes are sold more than others, meaning that in-demand sizes for certain apparel can affect pricing as well. Brands want to be able to charge as much as possible for their listing without risking losing a sale to a competitor.
The Competitive Pricing Challenge: Normalizing Product Attributes Across Competitors in Apparel and Fashion
There are hundreds of possible attribute permutations for every single apparel product. Some retailers may only sell core sizes and basic colors; some may sell a mix of sizes for multiple style types. Most retailers also sell multiple color variants for all styles they have on catalog. Other retailers may only sell a single, in-demand size of the product. Also, when other retailers are selling the product, it’s unlikely that their naming conventions, color options, style options, and sizing match yours one-for-one.
In one analysis, it was found that there were 800+ unique values for heel sizes and 1000+ unique values for shirts and tops at a single retailer! If you’re looking to compare prices, the effort involved in setting up and managing lookup tables to identify discrepancies when one retailer uses European sizes and another uses USA sizes, for example, is simply too onerous to contemplate doing. Colors only add to the complexity – as similar colors may have new names in different regions and locations as well!
Even if you managed to find all the discrepancies between product attributes, you would still need to update them any time a competitor changed a convention.
Still, monitoring your competitors and strategically pricing your listings is essential to maintain and grow market share. So what do you do? You can’t simply eyeball your competitor’s website to check their pricing and naming conventions. Instead, you need advanced algorithms to scan the entire marketplace, identify individual products being sold, and normalize their data and attributes for analysis.
Getting Color and Size Level Pricing Intelligence
With DataWeave, size and color are just two of several dimensions of a product instead of an impossible big data problem for teams. Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification.
This works by using AI built on more than 10 years of product catalog data across thousands of retail websites. It matches common identifiers, like UPC, SKU code, and other attributes for harmonization before employing a large language model (LLM) prompts to normalize color variations and sizing to a single standard.
For example, if a competitor has the smallest size listed as Sm but has your smallest listing identified as S, DataWeave can match those two attributes using AI. Similar classification can be performed on color as well.
Complex LLM prompts are pre-established so that this process is fast and efficient, taking minutes rather than weeks of manual effort.
Harmonizing products along with their color and sizing data across different retailers for further analysis has several benefits. Most importantly, product matching helps teams conduct better competitive analysis, allowing them to stay informed about market trends, competitors’ offerings, and how those competitors are pricing various permutations of the same product. It helps ensure that you’re offering the most competitive assortment of sizing in several colors to win more market share as well. Overall, it’s easier for teams to gain insights and exploit their findings when all the data is clean and available at their fingertips.
Product Matching Size and Color in Apparel and Fashion
Color and size are crucial attributes for retailers and brands in the apparel and fashion industry. It adds a level of complexity that can’t be overstated. While it’s a necessity to win consumers (more colors and sizes will mean a wider potential reach), the more permutations you add to your listing, the more complicated it will be to track it against your competition. However, This challenge is worth undertaking as long as you have the right solutions at your disposal.
With a strategy backed by advanced technology to discover identical and similar products across the competitive landscape and normalize their color and sizing attributes, you can ensure that you are competitively pricing your products and offering the best assortment possible. Employing DataWeave’s AI technology to find competitor listings, match products across variants, and track pricing regularly is the way to go.
Interested in learning more about DataWeave? Click here to get in touch!
Fuel retailers today operate in a highly competitive and volatile market. Consumer behavior is increasingly driven by price sensitivity, particularly in industries like fuel where small changes in price can significantly influence where consumers choose to fill up. The stakes are even higher when you consider the razor-thin margins many fuel retailers work with, making every cent count.
For years, retailers have relied on third-party apps and services to provide them with location-based competitive fuel price data. These services collect pricing data based on customer transactions. While these platforms offer a convenient way for consumers to find cheaper fuel prices, their value to retailers is limited. The data they provide is often riddled with inaccuracies, lags, and incomplete coverage, leaving retailers vulnerable to missed pricing opportunities.
In this rapidly shifting landscape, retailers need data that is not only accurate but also real-time. Solving this involves directly tapping into retailers’ own data sources (first-party or 1P data) —such as websites and apps. This is believed to be the most comprehensive and reliable source of fuel price data in the market.
To validate this hypothesis, we conducted a comprehensive analysis comparing first-party and third-party (3P) fuel price data. Our analysis compared pricing (at the same time of the day) across more than 40 gas stations—including major players like Circle K, Costco, Speedway, and Wawa. The data was captured several times a day for over a week.
Accurate Pricing Matters More Than Ever
Our analysis revealed that nearly a quarter (24.4%) of the fuel pricing data provided by third-party sources was inaccurate when compared to first-party data. On average, these inaccuracies amounted to a price difference of 10.9%.
Such discrepancies, though seemingly minor, can significantly affect consumer behavior. Inaccurate prices could drive customers to competitors who are listed with lower prices—even if the real difference is negligible. For fuel retailers, this leads to lost revenue, missed opportunities, and reduced market share.
The implications are clear: relying on third-party competitive data alone puts retailers at risk. With inaccurate data, retailers may fail to adjust their prices in time to respond to market changes, losing customers to competitors.
The Core Challenges of Third-Party Data
Third-party data comes with inherent limitations. The way this data is collected presents significant challenges for fuel retailers looking to optimize pricing strategies. Here are the main issues:
Inconsistent Data Frequency: Third-party pricing data is often gathered through customer card transactions. As a result, pricing data updates only when and where transactions occur. This can lead to irregular data availability, particularly in stations with lower transaction volumes. For instance, in rural areas or during off-peak hours, fewer transactions lead to fewer updates. Retailers are left with outdated data, making it difficult to keep pace with real-time price fluctuations.
Limited Geographic Coverage: Regions with lower transaction volumes are particularly affected by data gaps. While urban centers may enjoy more frequent updates, rural and less-frequented stations often suffer from a lack of data. This limited geographic coverage creates blind spots, making it impossible for retailers in these regions to stay competitive.
Potential Data Inaccuracies Across Fuel Types: Our analysis showed that inaccuracies in third-party pricing data were most pronounced for Unleaded fuel, with errors occurring nearly 80% of the time. While Diesel prices fared slightly better, inaccuracies were still frequent. This inconsistency across fuel types further complicates the challenge for retailers relying on third-party data.
Leveraging First-Party Data
At DataWeave, our Fuel Pricing Intelligence solution leverages real-time 1P data directly from fuel retailers’ websites and mobile apps, ensuring that retailers always have access to the most up-to-the-minute and accurate pricing information.
Here’s why first-party data stands out:
Real-Time Updates: Our solution provides near-instantaneous updates across more than 30,000 ZIP codes, ensuring that retailers always have the most up-to-date pricing information. This real-time accuracy is essential for making dynamic pricing adjustments in a highly competitive market.
Wide Geographic Coverage: DataWeave’s first-party solution captures data across a broad geographic range, ensuring no blind spots in coverage. Retailers in rural or less-frequented areas benefit from the same level of insight as their urban counterparts, giving them the ability to optimize pricing in real-time.
Complementary to Existing Solutions: For retailers already using third-party data, DataWeave’s first-party solution can complement and enhance their current systems. By filling in data gaps and providing more frequent updates, our solution ensures that retailers are never left in the dark when it comes to competitive pricing.
Retailer-Wise Variances
Among the retailers analyzed, we found that some were more affected by third-party data inaccuracies than others. Speedway and Wawa, for instance, experienced inaccuracies in up to 28% of third-party price data. In contrast, Circle K exhibited fewer discrepancies, but even they were not immune to the challenges posed by third-party data.
For their competition, relying on third-party data alone presents a significant risk. By switching to first-party data sources, or complementing their existing third-party data with DataWeave’s first-party solution, retailers can ensure they stay competitive in the eyes of price-sensitive consumers.
In an industry as price-sensitive as fuel retail, accurate data is a strategic asset. Leveraging first-party data allows fuel retailers to:
Maximize Revenue: By using real-time, accurate data, retailers can avoid under- or over-pricing their fuel, ensuring they capitalize on high-demand periods while minimizing losses during low-demand times.
Enhance Margins: First-party data provides the precision needed to fine-tune margins, ensuring profitability even in fiercely competitive markets.
Boost Customer Retention: Competitive pricing fosters customer loyalty. With better data, retailers can maintain customer trust and retention, even during volatile market shifts.
Shift into High Gear with DataWeave
As the fuel retail industry becomes increasingly competitive, the need for accurate, real-time pricing data has never been more important. DataWeave’s Fuel Pricing Intelligence solution empowers retailers with the insights they need to stay ahead of the competition, optimize pricing strategies, and boost profitability.
With first-party data, fuel retailers can eliminate the blind spots and inaccuracies associated with third-party sources. This shift toward data-driven pricing strategies ensures that every price adjustment is backed by real-time insights, giving retailers the edge they need to succeed.
Brands are investing millions of dollars in digital retail media to make their products stand out amid unrelenting competition.
The ad spend on digital retail media worldwide was estimated at USD 114.4 billion in 2022, and the current projections indicate that it will grow to USD 176 billion by 2028. This amounts to a 54% increase in just six years.
The current surge in digital retail media advertising has led brands to find an effective way to monitor the efficacy of their ad spend. While Share of Search has long been used to measure brand visibility effectively, the metrics often missed tracking ads on retail sites.
DataWeave’s Share of Media solution helps solve this problem.
What is the Share of Media?
At DataWeave, Share of Media is a metric used to measure a brand’s presence in sponsored listings and banner ads on eCommerce platforms. It captures how often a brand appears in paid promotions compared to competitors, offering insights into advertising visibility and effectiveness.
These days most marketplaces seamlessly blend banner ads and sponsored listings into organic search results. Let’s take a closer look.
Banner Advertising
Banner advertising strategically places creative banners across websites—often at the top, bottom, or sides. Some eCommerce platforms also integrate these banners into product search listings.
What makes banner ads so special is the unique ability to allow marketers to use various types of media in a single ad, such as images, auto-play videos, and animations. Brands can also present curated collections of products. This flexibility provides marketers with creative opportunities to differentiate from competitors, capture customer interest, and encourage conversions.
Sponsored Listings
Sponsored listings are paid placements within search engine results or eCommerce platforms. They are usually marked as ‘sponsored’ or ‘ad,’ and they often appear at the top of search results and alongside organic product listing results.
Unlike organic search results, sponsored listings are prioritized based on the advertiser’s bid amount and relevance to users’ search queries.
Sponsored listings offer a strategic advantage by enabling businesses to connect directly with consumers who are actively searching for their products. This targeted approach ensures that marketing efforts are focused on individuals with high intent of making a purchase, maximizing the potential return on investment.
The Power of Banner Ads and Sponsored Listings
Banner ads and sponsored listings are great choices for boosting customer engagement and product sales. Here are four key advantages they offer:
Enhanced Visibility: Digital retail media strategically places your brand where it will stand out—outshining competitors and grabbing the attention of high-purchase-intent consumers.
Precision in Reach: These ads target specific keywords or categories, allowing for highly focused advertising based on demographics and search intent.
Minimal Conversion Friction: Smooth transitions from ads to a brand’s native store or product listing on the marketplace keep conversion friction to a minimum.
Brand Awareness and Recall: Consistent exposure to your brand through banner ads and sponsored product listings can leave lasting impressions and build brand recognition.
The bottom line is that it’s increasingly important for brands to monitor their Share of Media.
How to Monitor Your Brand’s Share of Media
DataWeave’s Digital Shelf Analytics (DSA) platform extends beyond the traditional Share of Search metrics and provides robust support for monitoring the Share of Media.
DataWeave monitors the Share of Media in two ways: keywords and product categories. Users can view Share of Media insights through aggregated views, trend charts, and detailed tables. The views are designed to show brand visibility and the overall competitive landscape. For example, the screenshot below, taken from DataWeave’s dashboard, showcases the Share of Media across keywords, categories, and retailers.
Share of Media by Keyword
The Share of Media metric captures a brand’s advertising presence within search listings for a designated keyword. This provides a comprehensive view of a brand’s visibility and promotional efforts across retail platforms, helping brands validate and gauge the effectiveness of their ad spend.
For example, the screenshot below shows the trend of manufacturer’s Share of Media by keyword—‘baby food.’
Share of Media by Category
The Share of Media metric measures the presence of brands’ banner ads and sponsored listings across product categories on retail sites. This helps brands see which product categories require more investment, making it easier for them to spend their ad budget wisely.
The screenshot below illustrates manufacturers’ Share of Media by category across retailers.
Share of Media: An Essential Ecommerce Metric
As retail media continues to evolve, our analytics must follow—after all, knowledge is a competitive advantage. In the dynamic world of eCommerce, where competition is fierce and consumer attention is scarce, understanding your share of media is crucial.
Analyzing the Share of Media can give brands a competitive edge. By regularly monitoring and analyzing this metric, you can make data-driven decisions to improve your brand’s visibility, attract more customers, and ultimately drive sales growth. With a deeper understanding of their target audience and market dynamics, brands can refine promotional efforts to drive more effective results and optimize return on ad spend (ROAS).
For more information on how Digital Shelf Analytics can enhance your brand’s digital shelf presence, request a demo or contact us at contact@dataweave.com.
Your budget-conscious customers are hunting for value and won’t hesitate to switch brands or shop at other retailers.
In saturated and fiercely competitive markets, how can you retain customers? And better yet, how can you attract more customers and grow your market share? One thing you can do as a brand or retailer is to set the right prices for your products.
Competitive or competition-based pricing can help you get there.
So what exactly is competitive pricing? Let’s dive into this strategy, its advantages and disadvantages, and how it can be used to stay ahead of the competition.
What is Competitive Pricing?
Competitive or competition-based pricing is a strategy where brands and retailers set product prices based on what their competitors charge. This method focuses entirely on the market landscape and sets aside the cost of production or consumer demand.
It is a good pricing model for businesses operating in saturated markets, such as consumer packaged goods (CPGs) or retail.
Competitive Pricing Models
Competitive pricing isn’t a one-size-fits-all strategy. The approach includes various pricing models that can be customized to fit your business goals and market positioning.
Here’s a closer look at five of the most common competition-based pricing models:
Price Skimming
If you have a new product entering the market, you can initially set a high price. Price skimming allows you to maximize margins when competition is minimal.
This strategy taps into early adopters’ willingness to pay a premium for new project categories. As competitors enter the market, you can gradually reduce the price to maintain competitiveness.
Premium Pricing
Premium pricing lets you position your product as high-quality or luxurious goods.
When you charge more than your competitors, you’re not just selling a product—you’re selling status and an experience. This strategy is effective when your offering is of superior quality or has unique features that justify a higher price point.
Price Matching
Price matching—also known as parity pricing—is a defensive pricing tactic.
By consistently matching your competitors’ prices, you can retain customers who might otherwise, be tempted to switch to an alternative.
This approach signals your customers that they don’t need to look elsewhere for what they need and can feel comfortable remaining loyal to your brand.
Penetration Pricing
Penetration pricing is when you set a low price for a new product to gain market share quickly. The opposite of price skimming, this strategy can be particularly effective in price-sensitive or highly competitive industries.
By attracting customers early, you can also deter some competitors from entering the market. This bold move can establish your product as a market leader from the get-go.
Loss Leader Pricing
Loss leader pricing is a strategic sacrifice that can lead to greater gains in the long run.
By offering a product at a low price—sometimes even below cost—you can attract new customers to your brand and strengthen your current customers’ loyalty.
Eventually, you can cross-sell other higher-margin products to your loyal customer base to cover the loss from your loss leader pricing and increase sales of other more profitable products.
Key Advantages of Competitive Pricing
Although it’s not the only pricing strategy available, competitive pricing has some significant advantages.
It is Responsive
Agility is synonymous with profit in industries where consumer preferences and market conditions shift rapidly.
Competitive pricing allows you to adapt quickly—if a competitor lowers their prices, you can respond promptly to maintain your positioning.
It is Simple to Execute and Manage
Competitive pricing is straightforward, unlike cost-based pricing, which requires complex calculations and spans various factors and facets.
By closely monitoring competitors’ prices and adjusting your prices accordingly, you can implement this pricing strategy with relative ease and speed.
It Can Be Combined with Other Pricing Strategies
Competitive pricing is not a standalone strategy—it’s a versatile approach that can easily be combined with other pricing strategies. For example, say you want to use competitive pricing without losing money on a product. In this case, you could use cost-plus pricing to determine a base price that you won’t go below, then use competitive pricing as long as the price stays above your base price.
Key Disadvantages of Competitive Pricing
While competition-based pricing has its advantages, it’s not without its pitfalls. Here are some potential disadvantages of competitive pricing.
It De-emphasizes Consumer Demand
If you focus solely on what competitors are charging, you could overlook consumer demand.
For example, you could underprice items that consumers could be willing to purchase for more. Or, you might overprice items that consumers perceive as low-value, which can reduce sales.
You Risk Price Wars
If you and your competition undercut each other for customer acquisition and loyalty, you will eventually erode profit margins and harm the industry’s overall profitability. It’s a slippery slope where everyone loses in the end.
There’s Potential for Complacency
When you base your prices on beating those of competitors, you might neglect to differentiate your offerings through innovation and product improvements. Over time, this can weaken your brand’s position and lead to a loss of market share. Staying competitive means more than just matching prices—it means continuously evolving and adding value for the consumer.
4 Tips for a Successful Competitive Pricing Strategy in Retail
Here are four competition-based pricing tips for retailers:
Retailer Tip #1. Know Where to Position Your Products in the Market
For competitive pricing to work, you must understand your optimal product positioning in the overall market. To gain this understanding, you must regularly compare your offerings and prices with those of your key competitors, especially for high-demand products.
Then, you can decide which competition-based pricing model is suitable for you.
Retailer Tip #2. Price Dynamically
Dynamic pricing is a tactic with which you automatically adjust prices on your chosen variables, such as market conditions, competitor actions, or consumer demand.
When it comes to competitive pricing, a dynamic pricing system can track your competitors’ price changes and update yours in lockstep.
Price-monitoring tools like DataWeave allow you to stay ahead of the game with seasonal and historical pricing trend data.
Retailer Tip #3. Combine Competitive Pricing with Other Pricing Strategies
Competitive pricing can be powerful, but it doesn’t have to stand alone. You can enhance its benefits with complementary marketing tactics.
To illustrate, you can bundle products to offer greater value than what your competitors are offering. You can also leverage loyalty programs to offer exclusive discounts or rewards so customers keep returning, even when your competitors offer them lower prices.
Retailer Tip #4. Stay in Tune with Consumer Demand
Competition-based pricing aligns you with your competitor, but don’t lose sight of what your customers want. Routinely test your pricing strategy against consumer behavior to ensure that your prices reflect the actual value of your offerings.
5 Tips for a Successful Competitive Pricing Strategy for Consumer Brands
If you’re thinking about how to create a competitive pricing strategy for your brand, consider these five tips:
Brand Tip #1. Identify Competing Products for Accurate Comparisons
The first step in competitive pricing is to know the value of what you’re selling and how it compares to that of your competitors’ products. This extends to private-label products, similar but not identical products, and use-case products.
Product matching ensures your pricing decisions are based on accurate like-for-like comparisons, allowing you to compete effectively.
Brand Tip #2. Understand Your Product’s Relative Value
Knowing how your product competes on value is key to setting the right price. If your product offers higher value, price it higher; if it offers less, price it accordingly. This ensures your pricing strategy reflects your product’s market placement.
Brand Tip #3. Consider Brand Perception
Even if your product is virtually the same as a competitor’s, your brand’s perceived value may be different, which plays a crucial role in pricing.
If your brand is perceived as premium, you can justify higher prices. Conversely, if customers perceive you as a value brand, your pricing should reflect affordability.
Brand Tip #4. Leverage Value-Based Differentiation
When your prices are similar to competitors’, you must differentiate your products by expressing your product value through branding, packaging, quality, or something else entirely.
This differentiation will compel consumers to choose your product over other similarly priced options.
Brand Tip #5. Stay Vigilant with Price Monitoring
Your competitors will update their pricing repeatedly, and you will, too.
It can be difficult and time-consuming to monitor your competitive pricing, so you’ll need a system like DataWeave to monitor competitors’ pricing and manage dynamic pricing changes.
This vigilance ensures your brand remains competitive and relevant in real time.
4 Essential Capabilities You Need to Implement Successful Competition-Based Pricing
You’ll need four key capabilities to implement a competitive pricing strategy effectively.
AI-Driven Product Matching
Product matching means you’ll compare many products (sometimes tens or hundreds) with varying details across multiple platforms. Accurate product matching at that scale requires AI.
For instance, AI can identify similar smartphones to yours by analyzing features like screen size and processor type. DataWeave’s AI product matches start with 80–90% matching accuracy, and then human oversight can fine-tune the data for near-perfect matches.
You can make informed pricing decisions once you know which competing products to base your prices on.
Accurate and Comprehensive Data
A successful competition-based pricing strategy depends on high-quality, comprehensive product and pricing data from many retailers and eCommerce marketplaces.
By tracking prices on large online platforms and niche eCommerce sites across certain regions, you’ll gain a more comprehensive market view, which enables you to make quick and confident price changes.
Normalized Measurement Units
Accurate price comparisons are dependent on normalized unit measurements.
For example, comparing laundry detergent sold in liters to laundry detergent sold in ounces requires converting either or both products to a common base like price-per-liter or price-per-ounce.
This normalization ensures accurate pricing analysis.
Timely Actionable Insights
Timely and actionable pricing insights empower you to make informed pricing decisions.
With top-tier competitive pricing intelligence systems, you get customized alerts, intuitive dashboards, and detailed reports to help your team quickly act on insights.
In Conclusion
Competitive pricing or competition-based pricing is a powerful strategy for businesses navigating crowded markets, but you must balance competitive pricing with your brand’s unique value proposition.
Competitive pricing should complement innovation and customer-centric strategies, not replace them. To learn more, talk to us today!
As summer winds down, families across the US have been gearing up for the annual back-to-school shopping season. The back-to-school season has always been a significant event in the retail calendar, but its importance has grown in recent years. With inflation still impacting many households, parents and guardians are more discerning than ever about their purchases, seeking the best value for their money.
The National Retail Federation has forecasted that this season could see one of the highest levels of spending in recent years, reaching up to $86.6 billion. As shoppers eagerly stock up on back-to-school and back-to-college essentials, it’s crucial for retailers and brands to refine their pricing strategies in order to capture a larger share of the market.
To understand how retailers are responding to the back-to-school rush this season, our proprietary analysis delves into pricing trends, discount strategies, and brand visibility across major US retailers, including Amazon, Walmart, Kroger, and Target. By examining 1000 exactly matching products in popular back-to-school categories, our analysis provides valuable insights into the pricing strategies adopted by leading retailers and brands this year.
Price Changes: A Tale of Moderation
The most notable trend in our analysis is the much smaller annual price increases this year, in contrast to last year’s sharp price hikes. This shift is a reaction to growing consumer frustration about rising prices. After enduring persistent inflation and steep price growth, which peaked last year, consumers have become increasingly frustrated. As a result, retailers have had to scale back and implement more moderate price increases this year.
Kroger led the pack with the highest price increases, showing a 5.3% increase this year, which follows a staggering 19.9% rise last year. Walmart’s dramatic price increase of 14.9% is now followed by a muted 3.1% hike. Amazon and Target demonstrated a similar pattern of slowing price hikes, with increases of 2.3% and 2.7% respectively in the latest period. This trend indicates that retailers are still adjusting to increased costs but are also mindful of maintaining customer loyalty in a competitive market.
When examining specific product categories, we observe diverse pricing trends. Electronics and apparel saw the largest price increases between 2022 and 2023, likely due to supply chain disruptions and volatile demand. However, the pace of these increases slowed in 2024, indicating a gradual return to more stable market conditions. Notably, backpacks remain an outlier, with prices continuing to rise sharply by 22%.
Interestingly, some categories, such as office organization and planners, experienced a price decline in 2024. This could signal an oversupply or shifting consumer preferences, presenting potential opportunities for both retailers and shoppers.
Brand Visibility: The Search for Prominence
In the digital age, a brand’s visibility in online searches can significantly impact its success during the back-to-school season. Our analysis of the share of search across major retailers provides valuable insights into brand prominence and marketing effectiveness.
Sharpie and Crayola emerged as the strongest performers overall, with particularly high visibility on Target. This suggests strong consumer recognition and demand for these traditional school supply brands. BIC showed strength on Amazon and Target but lagged on Kroger, while Pilot maintained a more balanced presence across most retailers.
The variation in brand visibility across retailers also hints at potential partnerships or targeted marketing strategies. For instance, Sharpie’s notably high visibility on Target (5.16% share of search) could indicate a specific partnership.
Talk to us to get more insights on the most prominent brands broken down by specific product categories.
Navigating the 2024 Back-to-School Landscape
As we look ahead to the 2024 back-to-school shopping season, several key takeaways emerge for retailers and brands:
Price sensitivity remains high, but the rate of increase is moderating. Retailers should carefully balance the need to cover costs with maintaining competitive pricing.
Strategic discounting can be a powerful tool, especially for lesser-known brands looking to gain market share. However, established brands would need to rely more on quality, visibility, and brand loyalty.
Online visibility is crucial. Brands should invest in strong SEO and retail media strategies, tailored to different retail platforms.
Category-specific strategies are essential. What works for backpacks may not work for writing instruments, so a nuanced approach is key.
Retailers and brands should be prepared for potential shifts in consumer behavior, such as increased demand for value-priced items or changes in category preferences.
By staying attuned to these trends and remaining flexible in their strategies, businesses can position themselves for success in the competitive back-to-school retail landscape of 2024. As always, the key lies in understanding and responding to consumer needs while maintaining a keen eye on market dynamics.
Stay tuned to our blog to know more about how retailers can stay aware of changing pricing trends. Reach out to us today to learn more.
As the retail landscape continues to evolve, events like Amazon Prime Day have become more than just shopping extravaganzas—they’ve transformed into strategic battlegrounds where retailers assert their market positions and brand identities. Prime Day 2024 was no exception, serving as a crucial moment for retailers to showcase their pricing prowess, customer loyalty programs, and category expertise.
In an era where consumer expectations for deals are at an all-time high, the impact of Prime Day extends far beyond Amazon’s ecosystem. Retailers like Walmart, known for its “everyday low prices,” Target with its emphasis on style and value, and Best Buy, the electronics specialist, have all adapted their strategies to compete. These companies didn’t just react to Prime Day; they proactively launched their own pre-emptive sales events, with Target Circle Week, Walmart July Deals and more, effectively extending the shopping bonanza and challenging Amazon’s dominance.
For Prime Day, we analyzed over 47,000 SKUs across major retailers and product categories to publish insights on Amazon’s pricing strategies as well as the performance of leading consumer brands. Here, we go further to delve into the discounts offered (or not offered) by Amazon’s competitors during Prime Day. Our analysis reveals that some retailers chose to compete on price during the sale for certain categories, while others did not.
Below, we highlight our findings for each product category. The Absolute Discount is the total discount offered by each retailer during Prime Day compared to the MSRP. These are the discounts consumers are familiar with, displayed on retail websites prominently during sale events. The Additional Discount, on the other hand, is the reduction in price during Prime Day compared to the week prior to the sale, revealing the level of price markdowns by the retailer specific to a sale event.
Consumer Electronics
In the Consumer Electronics category, Best Buy stood out as a strong competitor, offering an Additional Discount of 5.9%—the highest among all competitors analyzed. This is unsurprising, as Best Buy is well-known for its focus on consumer electronics and is likely aiming to reinforce its reputation for offering attractive deals in order to maintain its strong consumer perception in the category.
Walmart was a close second with a 4.3% Additional Discount while Target reduced its prices by only 2% during the sale.
Apparel
In the Apparel category, Walmart’s Additional Discount was 3.1%, demonstrating its willingness to be priced competitively on a small portion of its assortment during the sale, without compromising much on margins.
Target, on the other hand, opted out of competing with Amazon on price during the sale, choosing instead to maintain its Absolute Discount level of around 11%.
Home & Furniture
The Home & Furniture category showcased diverse strategies from retailers. Specialty furniture retailers such as Overstock and Home Depot provided Additional Discounts of 3.9% and 2.5%, respectively, compared to Amazon’s 6.9%. This indicates a clear intent to maintain market share and remain top-of-mind for consumers despite Amazon’s competitive pricing.
Although Target didn’t significantly lower its prices during the sale, its Absolute Discount remains substantial at 18.9%. This suggests that Target’s markdowns were already steep before the event, which could explain the lack of further reductions during the sale.
Health & Beauty
The Health & Beauty category saw minimal participation from Amazon’s competitors, with the exception of Sephora, which reduced prices by 3.7% during Prime Day.
Ulta Beauty chose not to adjust its prices, likely reflecting its strategy to uphold a premium brand image. Walmart, on the other hand, offered a modest Additional Discount of 2% on select items. Given Walmart’s generally affordable product range, its total discount remained relatively low, around 3.5%.
In Conclusion
During Prime Day, Walmart was the only major retailer that made an effort to compete, albeit modestly. Target, on the other hand, largely chose not to offer any additional markdowns. However, several category-specific retailers, such as Best Buy in Consumer Electronics, Overstock and Home Depot in Furniture, and Sephora in Health & Beauty, aimed to retain market share by providing notable discounts.
What this means for consumers is that even on Amazon’s Prime Day, it’s not a bad idea to compshop to identify the best deal.
For retailers, the key takeaway is the importance of quickly analyzing competitor pricing and making agile, data-driven decisions to improve both revenues and margins. By utilizing advanced pricing intelligence solutions like DataWeave, retailers can optimize their discount strategies, better navigate pricing complexities, and drive revenue growth — all while staying prepared for major shopping events and beyond.
Amazon Prime Day 2024 saw U.S. shoppers spending a staggering $14.2 billion online during the two-day event—an 11% increase from last year. This surge in spending reflects a significant shift in consumer behavior and presents a wealth of insights for brands and retailers alike.
Unlike last year’s focus on essentials, Prime Day 2024 saw Americans enthusiastically embracing both necessities and discretionary purchases. The Consumer Electronics and Health & Beauty categories, for example, experienced a notable uptick in interest, driven by major retailers slashing prices across CPG and Grocery segments, amid other reasons. Check out our first article in the Prime Day series 2024, analyzing retail insights across categories during the event.
This year, small businesses gained unprecedented visibility on Amazon, pushing relatively new brands into visibility.
At DataWeave, we recognize the critical importance of understanding these market dynamics for brands navigating the competitive eCommerce landscape. To provide actionable insights, we conducted an extensive analysis of over 47,000 SKUs across key categories before and during Amazon during Prime Day 2024. Our study delves into:
Pricing strategies: How did brands adjust their discounts to capitalize on the Prime Day frenzy?
Share of Search: Which brands achieved the highest visibility for major search keywords?
Dive into these insights below to uncover how brands performed during Amazon Prime Day 2024, and learn how you can leverage these findings to enhance your brand’s digital shelf performance.
Our Findings
Most brands offered substantial discounts before Prime Day, then added smaller discounts during the event. This strategy creates a perception of value while still allowing room for Prime Day-specific deals. To understand the real value offered by brands, we conducted an extensive analysis of brand performance, examining both pricing strategies and visibility on the platform. Our approach focuses on two key metrics:
Discounts: We analyzed both the Absolute Discount (total markdown relative to MSRP a week before Prime Day) and the Additional Discount (the price reduction during Prime Day compared to the week before).
Share of Search (SoS): We examined the visibility of brands in the top 20 search results. We also separately tracked this metric for organic and sponsored search results.
Let’s dive into the category and brand specific insights:
Consumer Electronics
Once again, in 2024, the Consumer Electronics category dominated discounts. Amazon’s own brands lead with the highest average Absolute Discount (44.2%) and a significant Additional Discount (12.5%), showcasing its aggressive push for Prime Day.
In a surprising twist, Amazon’s homepage wasn’t dominated by its own brands. Instead, tech giants like Apple and Samsung took centre stage. Despite this, Amazon’s own brands offered significant discounts across electronics products, including Amazon Kindle, Fire TVs, Fire TV Sticks, Echo Dot, and more, aiming to capture market share via markdowns.
Soundcore (earphone audio products brand) offered the highest discount during Prime Day, at 30.10%. Other headphone, earbuds, and wireless headphone brands including Sony, Beats, JBL, and more also offered significant discounts.
Premium brands like Apple (17.90% Absolute, 9.00% Additional) and Bose (23.10% Absolute, 16.00% Additional) offered relatively modest discounts, aligning with their brand positioning, but also taking advantage of the Prime Day frenzy.
Share of Search Insights in Consumer Electronics
JBL emerged as the standout performer, with the most significant increase in SoS, jumping from 14.3% pre-event to 25.8% during Prime Day, driven entirely by organic growth. Beats also saw a remarkable rise, increasing from 2.8% to 12.8%, again through organic listings only. Samsung maintained its strong presence, growing from 18.6% to 26.8%, with most of its growth influenced by increased ad spend.
Apple, despite already having a high pre-event SoS, managed to increase its share further from 23.0% to 31.0%, with some contribution via sponsored ads. LG saw a substantial increase from 1.3% to 6.9%, primarily through sponsored listings, opting for an inorganic approach to drive visibility during the sale.
Amazon and its AmazonBasics brand both saw notable increases in SoS, relying solely on organic growth. This is, of course, not surprising since Amazon controls its organic ranking algorithm.
Interestingly, some brands experienced decreases in SoS. Sony, Motorola, and Hisense all saw reductions in their share, with Hisense’s decline coming entirely from a reduction in sponsored listings.
Key Takeaway: Prime Day 2024 saw a significant reshuffling of brand visibility in the Consumer Electronics category. While some established brands like JBL, Beats, Samsung, and Apple strengthened their positions through a mix of organic and sponsored growth, others faced increased competition for consumer attention. The event highlighted the importance of a balanced approach to visibility, with successful brands leveraging both organic search optimization and strategic use of sponsored listings to maximize their presence during this high-traffic period.
Apparel
In the Apparel category, Adidas led with the highest Absolute Discount (27.2%) and a significant Additional Discount (9.8%). Value brands like Hanes (innerwear brand) and Anrabess offered substantial discounts, while Amazon Essentials maintained high discounts across the board (16.5% Absolute, 15.3% Additional).
Some brands like Cupshe (swimwear and vacation apparel brand) offered relatively lower additional markdowns. Meanwhile CRZ Yoga (athleisure brand) did not offer additional markdowns on Prime Day.
Share of Search Insights in Apparel
Gildan (activewear brand) emerged as the top performer in terms of SoS growth, increasing from 8.1% pre-event to 12.1% during Prime Day, driven entirely by organic growth. CRZ Yoga (an athleisure apparel brand) and Dokotoo (women’s casualwear brand) also saw significant increases in their SoS, rising by 2.8 and 2.3 percentage points respectively, again through organic listings only.
Amazon Essentials continued to perform well, increasing its visibility from 7.0% to 9.1%, aligning with its competitive pricing strategy. Coofandy also saw a notable increase, growing from 7.8% to 9.9%.
Interestingly, some brands that were previously highlighted for growth actually experienced decreases in SoS. Automet (clothing & accessories brand) saw a slight decline from 12.5% to 12.1%, while Anrabess (women’s fashion brand) dropped from 15.8% to 14.5%. Cupshe (swimwear brand) experienced the most significant decrease, falling from 10.6% to 5.5%.
Adidas, despite leading in discounts, saw only a modest increase in SoS from 7.0% to 8.0%. Notably, none of the brands visible in the top search results utilized sponsored listings, with all changes in SoS coming from organic growth or decline. This indicates a lack of maturity in this category in leveraging retail media.
Key Takeaway: Prime Day 2024 in the apparel category showcased the importance of organic search optimization. While some brands like Gildan and CRZ Yoga significantly improved their visibility, others faced challenges in maintaining their pre-event positions. The absence of sponsored listings across all brands highlights a unique dynamic in the apparel category, where organic search performance appears to be the primary driver of visibility during high-traffic events like Prime Day.
This suggests that Apparel brands may need to focus more on SEO strategies and organic content optimization to maximize their presence during major shopping events, rather than relying on paid promotions. On the other hand, smartly leveraging retail media to boost visibility can give apparel brands a competitive edge.
Health & Beauty
The Health & Beauty category this year got a push thanks to Amazon’s subscription offering. Prime members who subscribed for regular usage products like toothpaste and health aids or medicines availed higher discounts.
Amid Health & Beauty brands, Neutrogena led with the highest Absolute Discount (32.7%) and a significant Additional Discount (11.7%). Sun Bum moisturizers & sunscreen (23.3%) and Viking Revolution (23.1%) offered the highest Additional Discounts, indicating a strong Prime Day focus.
Premium brands like L’Oreal Paris and Philips Sonicare offered moderate discounts, balancing promotions with their intended brand image.
Share of Search Insights in Health & Beauty
Banana Boat (sunscreen brand) emerged as the standout performer, seeing the largest increase in SoS from 6.5% to 13.4%, achieved entirely through organic growth. Nyx Professional Makeup also saw a significant jump, rising from 3.9% to 8.9%, again solely through organic listings.
Contrary to previous analysis, e.l.f. actually experienced substantial growth, increasing from 9.0% to 13.1% SoS, with a strong focus on organic growth (4.4%) slightly offset by a minor decrease in sponsored listings (-0.2%).
Neutrogena maintained its strong performance, aligning with its aggressive discounting strategy, as its SoS increased from 14.6% to 18.5% through organic growth. Colgate also saw a notable increase from 11.0% to 13.7% SoS.
Interestingly, some brands employed a mixed strategy. Dove and Garnier saw overall increases in SoS, but achieved this through different means. Dove relied heavily on sponsored growth, while Garnier offset a decrease in organic listings with strong sponsored content growth.
Contrary to previous observations, Oral-B experienced a decrease in SoS from 18.5% to 15.3%, entirely in organic listings. Without any additional spend on sponsored listings to compensate, it lost significant ground in its visibility. Other brands facing significant declines include Tresemme, OGX, Philips Sonicare, and most notably, Viking Revolution, which dropped from 17.2% to 10.7% in its SoS.
Key Takeaway: The Health & Beauty category during Prime Day 2024 showcased a diverse range of strategies and outcomes. While some brands like Banana Boat and Nyx Professional Makeup achieved significant visibility gains through organic growth, others like Dove and Garnier relied more on sponsored content.
The success of e.l.f. and Neutrogena in aligning discounting strategies with increased visibility stands in contrast to the challenges faced by previously strong performers like Oral-B and Viking Revolution. This varied landscape shows the fierce competition in the category and the need for brands to employ multi-faceted strategies that balance organic optimization, sponsored content, and competitive pricing to succeed in high-stakes events like Prime Day.
Brand Strategies and Future Implications
Our analysis reveals several key trends:
Brand Positioning Matters: Premium brands like Apple and Bose maintained their positioning with modest discounts, while value-oriented brands like Soundcore and Hanes offered deeper cuts to attract price-sensitive shoppers.
Visibility vs. Discounting: Some brands, particularly in the Consumer Electronics category, prioritized increasing their visibility (Share of Search) over offering steep discounts. This strategy suggests a focus on long-term visibility and brand perception rather than short-term sales boosts.
Category-Specific Approaches: Apparel brands uniquely relied on organic search visibility, eschewing sponsored listings entirely. In contrast, several Health & Beauty brands leveraged sponsored content significantly to boost their presence.
Emerging Brand Opportunities: Lesser-known brands, especially in the Apparel and Health & Beauty categories, used Prime Day as a launchpad to increase their visibility, often outpacing established names in Share of Search growth.
Amazon’s Dual Strategy: As both a platform and a brand, Amazon showcased its ability to offer deep discounts on its own products while also providing a stage for other brands to shine.
Stay tuned to our blog for more in-depth analyses of brand and retailer performance and strategies across various retail events. Reach out to us today to learn how you can leverage data-driven insights to optimize your brand’s eCommerce strategy and performance.
Amazon Prime Day 2024 has once again shattered records, with more items sold during the two-day event than any previous Prime Day. Prime members worldwide saved billions across all categories, while independent sellers moved an impressive 200 million items.
At DataWeave, we conducted an extensive analysis of the discounts offered by Amazon across major categories. By examining over 47,000 SKUs, we’ve uncovered compelling insights into pricing strategies, competitive positioning, and emerging trends in the eCommerce space.
Since products on Amazon and other eCommerce websites are often sold at discounts even on normal days not linked to a sale event, we delved into the real value that Prime Day offers to shoppers by focusing on price reductions or the Additional Discount during the sale compared to the week before. As a result, our approach highlights the genuine benefits of the event for shoppers who count on lower prices during the sale. At the same time, our report also includes the Absolute Discounts offered during Prime Day, which represents the total markdown relative to the MSRP.
Amazon’s Cross-Category Discount Strategy
Our analysis reveals that the Electronics category saw the highest discounts with an average absolute discount of 20.4% and additional discounts on Prime Day amounting to 10.4%. Meanwhile the Home & Furniture had the lowest discount at 13.1%.
The Health & Beauty category saw significant additional discounts during Prime Day, at 9.26%. The Apparel category offered attractive absolute (16.10%) and additional (8.90%) discounts.
Category Deep Dive
Consumer Electronics
Still the star of the show, the electronics category saw the highest markdowns this Prime Day with absolute discounts at 20.40% and across 14.61% of their inventory.
Across Electronics subcategories, Earbuds had the highest markdowns at 34.80%, followed closely by Wireless Headphones at 30.60% and Headphones at 29.00%, with steep additional discounts during Prime Day as well. Apple AirPods Pro, for example, retailed at $168 (down from $249) at a 32% discount.
Meanwhile, smartphones had the lowest markdowns at 9.30%, followed by Laptops at 10.50%. Laptops also had the lowest additional discount during Prime Day at just 1.28%, significantly lower than other subcategories. Speakers (20.80%), Drones (19.10%), and Smartwatches (25.00%) offered moderate to high markdowns.
Notably, all Amazon products including Kindle, Echo, Echo Earbuds, Alexa, Fire TV, Fire TV Stick, and Fire Tablets, were aggressively discounted upwards of 30% this Prime Day. These products also came with the label “Climate Pledge Friendly.”
These aspects indicate Amazon’s push to promote its own ecosystem of products to the top, as well as cater to changing consumer preferences.
Apparel
Discounts offered this Prime Day increased from 13.2% in 2023 to 16.1% in 2024.
Amid apparel subcategories, Amazon appears to be pushing Women’s apparel categories more aggressively, particularly in Tops, Shoes, and Athleisure.
Women’s Shoes lead with the highest discounts at 26.50%, followed by Women’s Tops at 22.50% and Men’s Shoes at 22.80%. Women’s Tops also maintained the highest additional discount at 15.27%, followed by Women’s Athleisure at 13.03% and Men’s Swimwear at 12.44%.
Similar to 2023, Men’s Innerwear offered significantly lower discounts, with only 1% absolute discount and 0.72% additional discount. Women’s Innerwear also shows low discounts at 3.20% absolute and 2.23% additional.
Health & Beauty
Amid health & beauty subcategories, Moisturizes witnessed the highest markdowns at 20.10%, followed by Make Up at 18.90%. The Moisturizer subcategory also offers highest additional discounts at 12.20%, followed closely by Sunscreen at 10.25% and Beard Care at 10.22%.
The Toothpaste subcategory has the lowest discounts, at 10.90%. The lower discounts on everyday essentials like this might indicate a steady demand or an attempt to maintain margins on frequently purchased items.
Most Health & Beauty subcategories fall in the 15-18% range for actual discounts and 8-10% range for additional discounts. Electric Toothbrush (16.90% actual, 9.91% additional) and Shampoo (16.50% actual, 8.78% additional) represent the middle of the pack. There were a few highly attractive deals though, such as the Philips Sonicare toothbrush retailing at $122.96 (down from $199.99), with a 39% discount.
Amazon also offered significant discounts on Open Box products (products that are returned, but unused, out of mint condition boxes) to Prime members.
Home & Furniture
This category saw the lowest discounts for this Prime Day event at 13.1%. Across subcategories, Rugs lead with the highest average discount at 21.50%, closely followed by Luggage at 20.90%. Amazon seems to be pushing decorative and organizational items (Rugs, Bookcases) more aggressively, possibly due to higher margins. Rugs also stood out as the subcategory with the highest additional discount of 11.54%.
Sofas have the lowest additional discount at 2.76%, followed by Dining Tables at 3.21%. Items like Cabinets (15.80% absolute, 6.66% additional) and Coffee Tables (14.40% absolute, 6.25% additional) represent the middle range of discounts.
Watch Out For More
As the holiday season approaches, it’s clear that the retail landscape continues to evolve. While Amazon remains a formidable force, there are opportunities for savvy competitors to carve out their niches and attract deal-hungry shoppers. By analyzing these trends and adjusting strategies accordingly, retailers can position themselves for success in the high-stakes world of summer sales events.
Stay tuned to our blog for more insights on how Amazon’s competitors reacted to Prime Day, and how leading brands across categories fared in terms of their pricing and their visibility during the sale event. Reach out to us today to learn more.
Virtually every cuisine in the world uses eggs. They’re in your breakfast, lunch, dinner, and dessert — which is perhaps why the global egg market is expected to generate $130.70 billion in revenue in 2024 and is projected to grow to approximately $193.56 billion by 2029.
More specifically, the United States is the fourth-largest egg producer worldwide. The country’s egg market is projected to generate $15.75 billion in 2024 and increase to $22.51 billion by 2029.
Health-consciousness among consumers: Consumers value eggs for their essential nutrients and rich protein content.
Demand for convenience foods: Consumers’ preferences are shifting toward quick and easy foods, which drives demand for shell eggs and pre-packaged boiled or scrambled eggs.
Population Growth: A growing worldwide population increases the demand for eggs.
Affordability and accessibility: Eggs are an affordable and accessible nutrient-dense food source for many.
Despite these factors contributing to the U.S. egg market’s growth, recent times have seen egg prices fall dramatically.
Based on a sample of 450 SKUs, DataWeave discovered that egg prices in the U.S. fell by 6.7% between April 2023 and April 2024, dipping to its lowest (-12.6%) in December 2023.
So, what’s causing the decrease in egg prices?
The Rise and Fall of Egg Prices: A Recent History
In 2022, avian influenza severely impacted the United States. The disease affected wild birds in nearly every state and devastated commercial flocks in approximately half of the country.
The 2022 incident was the first major outbreak since 2015 and led to the culling of more than 52.6 million birds, mainly poultry, to prevent the disease from spreading uncontrollably.
With almost 12 million fewer egg-laying hens, the United States produced around 109.5 billion eggs in 2022 — a drop of nearly two billion from the previous year.
Consequently, the cost of eggs soared, peaking at $4.82 a dozen — more than double the price of eggs in the previous year.
The avian flu continues to affect egg-laying hens and other poultry birds across the United States. As of April 2024, farms have killed a total of 85 million poultry birds in an attempt to contain the disease.
Despite the disease’s effects, production facilities have made significant efforts to repopulate flocks, leading to a steady increase in supply – and a much anticipated decrease in egg prices.
According to the U.S. Bureau of Labor Statistics, there was an increase in producer egg prices in 2022, reaching a peak in November 2022, at which point they began to fall.
Retailer’s egg prices followed suit. The egg price chart below depicts retailers’ declining egg prices over one year, from April 2023 to April 2024, with Giant Eagle showing the most significant price reductions and Walmart the least.
What Does the Future Hold for Egg Prices?
The USDA reported recent severe avian flu outbreaks in June 2024. These outbreaks are estimated to have affected 6.23 million birds.
With a reduction in egg-laying hens, egg prices are likely to increase — time will tell.
Nonetheless, the annual per capita consumption of eggs in the U.S. is projected to reach 284.4 per person in 2024 from 281.3 per person in 2023. So for now, producers and retailers can rest assured of the growing demand for eggs.
How Can Retailers Adapt to the Unpredictability of Egg Prices?
Egg prices were down to $2.69 for a dozen in May 2024. However, they are still significantly higher than consumers were used to just a few years ago—eggs were, on average, $1.46 a dozen in early 2020.
Additionally, while the avian flu puts pressure on producers, inflation and supply chain disruptions exert pressure on retailers.
With such challenging egg market conditions, what can retailers do to maintain customer loyalty amid reduced consumer spending while maintaining profitability?
1. Give the Customer What They Want: Increase Offerings of Organic, Cage-Free, and Free-Range Eggs
As mentioned, Data Bridge Market Research’s trends and forecast report highlighted a significant increase in consumer health consciousness. Additionally, animal welfare increasingly influences consumers’ purchasing decisions when buying meat and dairy products.
DataWeave data shows that the prices of organic, cage-free, and free-range eggs—such as those by brands like Happy Eggs and Marketside—have fallen less than those of non-organic, caged egg brands.
2. Increase Private-Label Offerings
Private labels typically offer retailers higher margins than national brands. These margins can shield consumers from sudden wholesale egg price swings, helping to preserve brand trust and consumer loyalty without sacrificing profitability.
Moreover, eggs are particularly suited to private labeling, given their uniform appearance and taste and the lack of product innovation opportunities.
Undoubtedly, this is why sales of private-label eggs dwarf sales of national egg brands in the United States. Statista reports that across three months in 2024, private label egg sales amounted to $1.55 billion U.S. dollars, while the combined sales of the top nine national egg brands totaled just $617.88 million U.S. dollars.
3. Price Intelligently
With the current and predicted fluctuations in egg prices over the foreseeable future, price competitiveness is paramount to margin management and customer loyalty.
This is especially true when lower prices are the primary factor influencing the average consumer’s choice of supermarket for daily essentials purchases.
AI-driven pricing intelligence tools like DataWeave give retailers valuable highly granular and reliable insights on competitor pricing and market dynamics. In today’s data-motivated environment, these insights are necessary for competitiveness and profitability.
Final Thoughts
Egg prices have fluctuated significantly due to the impact of avian flu. Despite recent price drops, future egg price increases are possible due to ongoing outbreaks. Retailers should adapt to unstable egg prices by increasing organic, free-range, cage-free, and private-label egg offerings while leveraging AI-driven pricing tools to maintain margins and customer loyalty.
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.
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!
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.
