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  • India’s Black Friday 2025 Decoded: Discount Analysis and Brand Performance Across Major E-commerce Platforms

    India’s Black Friday 2025 Decoded: Discount Analysis and Brand Performance Across Major E-commerce Platforms

    Black Friday has evolved from a purely Western retail phenomenon into a global shopping event. India is no exception. While the country celebrates its own mega sale events like the Great Indian Festival and Big Billion Days, Black Friday has carved out its own space in the Indian retail calendar. E-commerce in India is expected to reach $325 billion by 2030, with festive shopping seasons driving significant portions of that growth.

    So how did Indian retailers and brands navigate Black Friday 2025? At DataWeave, we analyzed pricing trends across four major categories: Consumer Electronics, Home & Furniture, Health & Beauty, and Apparel. Our AI-powered retail intelligence platform tracked nearly 128,000 SKUs across leading platforms including Amazon India, Flipkart, Myntra, and others, revealing how the Indian market approached discounting and brand visibility during this high-stakes shopping period.

    Our Methodology

    DataWeave monitored average discount percentages across major Indian e-commerce platforms during two distinct periods:

    • Pre-Black Friday: Up to November 23, 2025 – capturing early promotional activity and baseline pricing
    • Black Friday Week: November 24 – December 1,2025 – spanning Thanksgiving week through Black Friday (November 28) and Cyber Monday (December 1)

    We analyzed top-ranked products across subcategories on major retail sites, alongside Share of Search data, a metric that measures brand visibility by tracking which brand names appear in the top 20 search results for high-intent keywords.

    Here’s a quick look at the overall discounts this Black Friday in India:

    Black Friday discounts snapshot India

    Consumer Electronics

    Consumer electronics remain a cornerstone of Black Friday shopping in India, with smartphones and laptops driving significant online sales. Our analysis of 20,439 SKUs reveals distinctive discount patterns across subcategories.

    Subcategory Discount Analysis

    Subcategory level discounts across consumer electronics in India this Black Friday

    The category averaged 23.2% pre-Black Friday discounts with an additional 0.5% during Black Friday Week. Drones and TVs led pre-sale discounting at 47.1% and 45.2% respectively, suggesting retailers wanted to clear high-ticket inventory ahead of the main event. Tablets (25.2%) also saw aggressive pre-sale pricing. During Black Friday Week, Earbuds saw the most substantial additional discounts at 1.1%, while categories like Laptops and Smartphones, already heavily discounted, had minimal incremental price cuts at 0.2%.

    Share of Search: Brand Visibility Trends

    Brand visibility trends in the consumer electronics category this Black Friday Cyber Monday, India

    The most notable is Cofendy, electronics accessories and speaker brand, that saw the share of search rise from 1.4% pre Black Friday to 8.2% during the event. Realme followed with a solid 2.3% gain, reinforcing its position as a rising smartphone brand. Smart gadget brand Hammer also saw visibility increase by 1.4% at par with OnePlus, Fastrack, HP and Asus.

    Samsung saw the highest brand visibility with share of search at 9.6% pre-event and 9.5% during Black Friday week, despite seeing a small drop in visibility. Audio brand Boat saw a visibility drop by 0.8%, while Xiaomi saw the share of search drop by 0.1%. This shift suggests that Indian consumers were drawn to newer brands and compelling deals across computing and mobile devices during the sale period.

    Health & Beauty

    The Indian beauty and personal care market is experiencing rapid growth, expected to reach $30 billion by 2027. Black Friday has become an important sales window for beauty brands and retailers. Our analysis of 19,854 SKUs reveals distinct patterns.

    Subcategory Discount Analysis

    Subcategory level discounts across health and beauty in India this Black Friday

    The category averaged 17% pre-Black Friday discounts with an additional 0.4% during Black Friday Week. Beard Care led early discounting at 21.3%, reflecting strong pre-sale positioning in men’s grooming. Conditioner (17.2%), Makeup (16.9%), and Moisturizer (16.9%) also saw solid baseline promotions. During Black Friday Week, Makeup and Sunscreen saw the highest additional discounts at 0.5% each, while Electric Toothbrush and Toothpaste maintained modest incremental discounts at 0.2%.

    Share of Search: Brand Visibility Trends

    Brand visibility trends in the beauty and health category this Black Friday Cyber Monday, India

    Affordable and emerging beauty brands dominated during Black Friday in India. Kellsie (beauty tools brand) surged from 1.6% to 6.5%. Classic mass-market brands like Pond’s (+3.4%) and Parachute Advanced (+2.8%) also performed strongly, alongside men’s grooming favorite Beardo (+1.6%). Other popular brands like Maybelline, Tresemme, Vaseline, all saw share of search and visibility increase during Black Friday.

    Skincare brand Minimalist made a notable entry, jumping from 0% to 2.0% visibility. The flip side? Premium international brand L’Oréal Paris dropped from 8.3% to 6.5%, losing visibility during Black Friday.

    Apparel

    Our analysis of 57,537 SKUs reveals interesting discount dynamics.

    Subcategory Discount Analysis

    Subcategory level discounts across apparel and fashion in India this Black Friday

    The category averaged 14.3% pre-Black Friday discounts with an additional 0.1% during Black Friday Week. Men’s Swimwear and Men’s Athleisure led pre-sale promotions at 22.2% and 18.5% respectively, while Women’s Swimwear and Women’s Shoes also saw strong pre event discounts at 19.3% and 17.6%.

    Black Friday Week saw minimal incremental discounting across all subcategories, with most adding just 0.1-0.3%. The relatively subdued incremental discounting indicates that early birds captured the best deals, or that margins were already stretched from pre-sale promotions.

    Share of Search: Brand Visibility Trends

    Brand visibility trends in the apparel and fashion category this Black Friday Cyber Monday, India

    Pepe Jeans dominated visibility, surging from 7.4% to 24.6%, a staggering gain that represents the largest visibility increase across all categories. Jockey also performed strongly with a 6% gain, solidifying its position in innerwear.

    On the flip side, athletic and footwear stalwarts faced headwinds: Speedo dropped 1.4% and Bata fell 1.1%. This data suggests that during Black Friday 2025 in India, denim and lifestyle fashion brands invested heavily in promotional visibility, capturing massive mindshare at the expense of traditional athletic and footwear brands.


    For brands and retailers navigating India’s increasingly competitive e-commerce landscape, the 2025 Black Friday data reveals a critical insight: pre-sale positioning matters more than Black Friday Week discounting. Early promotional investment and visibility campaigns delivered far greater returns than late-stage price drops, fundamentally reshaping how brands should approach this shopping event.

    Want to understand how DataWeave’s retail intelligence platform can help your business make data-driven decisions during peak sales events? Contact us to learn more about competitive insights, price intelligence, assortment analytics, content analytics, and digital shelf analytics.

    Check out our analysis on Black Friday 2025 Pricing and Discount trends in the USA, Canada, UK, and Germany. Follow our blog for more insights on retail pricing trends, brand visibility analysis, and data-driven commerce intelligence.

  • Black Friday Canada 2025: Comprehensive Pricing Analysis Reveals Strategic Discount Patterns Across Consumer Electronics and Home Categories

    Black Friday Canada 2025: Comprehensive Pricing Analysis Reveals Strategic Discount Patterns Across Consumer Electronics and Home Categories

    Black Friday 2025 marked another significant shopping event for Canadian consumers, with retailers deploying sophisticated pricing strategies to capture holiday spending. Online and in-store spending on Black Friday reached an estimated $865 million in Canada, which marked a 6% increase compared to last year.

    At DataWeave, we analyzed Black Friday 2025 pricing dynamics across two major categories in the Canadian market: Consumer Electronics and Home & Furniture. Our AI-powered pricing intelligence platform tracked approximately 16,000 SKUs across leading Canadian retailers like Target, Walmart, Wayfair, Home Depot, Amazon, Best Buy, Loblaws, Metro and more, uncovering distinct patterns in how brands and retailers structured their promotional calendars this year.

    Our Methodology

    DataWeave monitored pricing and discount trends across Canadian retailers during two key timeframes:

    • Pre-Black Friday Period: Up to November 23, 2025 – establishing baseline promotional activity and early holiday deals
    • Black Friday Week: November 24 – December 1, 2025 – spanning Thanksgiving through Black Friday (November 28) and Cyber Monday (December 1)

    Our dataset included top-performing products across multiple subcategories on major Canadian retail platforms. We also tracked Share of Search metrics, measuring brand prominence by analyzing which brand names appeared in the top 20 search results for high-value keywords during each period.

    Black Friday 2025 Canada: Overview

    Here’s how average discounts compared between the Pre-Black Friday period and Black Friday Week:

    Canada Black Friday Cyber Monday Discount in Pricing Snapshot

    Key Finding: Canadian retailers front-loaded their promotional strategy significantly, with Pre-Black Friday discounts averaging 16.3% across categories. The additional lift during Black Friday Week was modest at 1.4%.

    Consumer Electronics

    Consumer electronics consistently drives Black Friday traffic, and Canadian retailers delivered compelling early-season value. Our analysis of 11,772 SKUs reveals nuanced subcategory performance.

    Subcategory Discount Breakdown

    Subcategory-wise Discounts Black Friday in Consumer Electronics - Canada

    Consumer Electronics averaged 13.6% discounts in the Pre-Black Friday period, with Black Friday Week adding 1.7%. Audio & Video dominated early promotional activity at 23.8%, signalling retailers’ intent to clear inventory ahead of the main event. Wearables followed at 16.9%, likely timed to capture gift-buying momentum.

    During Black Friday Week, Audio & Video maintained leadership with an additional 2.5% discount, the highest incremental lift in the category. Meanwhile, Computers and Gaming, already positioned at modest pre-sale discounts of 6.2% and 6.4%, saw minimal additional movement, suggesting constrained inventory or deliberate pricing discipline in these segments.

    Share of Search: Brand Visibility Trends

    Our Share of Search data reveals which brands gained or lost prominence during Black Friday Week:

    Brand visibility for Consumer electronics brands on Black Friday Canada

    Audio brands dominated visibility gains during Black Friday Week. Beats by Dr. Dre nearly tripled its Share of Search from 3.1% to 9.2%, showing the strongest gains. Visibility for Dutchman climbed 3.6%. Motorola and Samsung also captured increased search prominence with gains of 2.5% and 2.2% respectively.

    Conversely, some established tech brands experienced visibility declines. Gaming brand Viprtech by 2.9%. Asus declined by 1.6%, and SanDisk dropped by 1.3%.

    Home & Furniture

    Home & Furniture showed the most aggressive early discounting of any category in our Canadian analysis. Tracking 4,362 SKUs, we found retailers heavily promoted home goods well before Black Friday weekend.

    Subcategory Discount Breakdown

    Subcategory-wise Discounts Black Friday in Home and Furniture - Canada

    Home & Furniture led all categories with Pre-Black Friday discounts averaging 23.5%, but Black Friday Week added only 0.7%, the smallest incremental discount in our entire study. Kitchenware saw extraordinary pre-sale discounts of 43.2%, the highest in any subcategory we tracked. Bedding followed at 33.6%, while Furniture reached 24.3%. These deep early discounts likely aimed to drive early conversions and manage bulky inventory ahead of peak logistics constraints.

    During Black Friday Week, additional discounting remained minimal across the board. Bedding posted the highest lift at 1.1%, while Kitchenware saw just 0.4%. The data suggests Canadian home goods retailers viewed the pre-sale period as their primary promotional window, with Black Friday serving more to sustain momentum than deliver new value.

    Share of Search: Brand Visibility Trends

    Visibility for home and furniture brands on Black Friday Canada

    Mattress brand Zinus led gains with a 3.9% increase, positioning itself as a value-focused furniture leader during the sale period. Cuisinart climbed by 1.5%, suggesting strategic amplification of this established kitchenware brand.

    Most home brands maintained relatively stable visibility throughout the sale period. Safavieh and Dorel showed essentially no change, while Better Homes & Gardens and Viscologic experienced only minor declines of 0.1% and 0.2% respectively.


    Want to understand how DataWeave’s pricing intelligence platform can help your business make data-driven decisions during peak sales events? Contact us to learn more about competitive insights, price intelligence, assortment analytics, content analytics, and digital shelf analytics.

    Check out our coverage on Black Friday 2025 across US, UK, and Germany. Follow our blog for more insights on retail pricing trends, brand visibility analysis, and data-driven commerce intelligence.

  • Black Friday 2025 Germany: Full Analysis of Discount Trends and Brand Search Visibility Across Key Categories

    Black Friday 2025 Germany: Full Analysis of Discount Trends and Brand Search Visibility Across Key Categories

    Black Friday 2025 solidified its position as a major retail event in the German market. German consumers demonstrated an average Black Friday budget of €317, the highest among select European countries.

    The German market, however, exhibited unique consumer behavior patterns during Black Friday 2025. Rather than meticulous planning, 59% of German shoppers allowed deals to inspire their purchases, compared to just 32% who shopped with predetermined lists and budgets. This spontaneous yet budget-conscious approach distinguishes German consumers from other markets, where FOMO-driven purchasing tends to dominate.

    Against this backdrop of rational deal-seeking and regional diversity, how did retailers and brands perform across key categories this Black Friday?

    At DataWeave, we conducted a comprehensive analysis of Black Friday 2025 discounting trends across five major categories in the German market: Consumer Electronics, Home & Furniture, Health & Beauty, Grocery, and Apparel. Our AI-powered pricing intelligence platform monitored over 16,000+ SKUs across leading German retailers like Amazon, Aldi, Douglas, Nutritienda, Carethy, Bofrost, and more, revealing fascinating patterns in how retailers and brands approached this year’s sale season.

    Our Methodology

    For this analysis, DataWeave monitored average discounts across leading German retailers during two distinct periods:

    • Pre-Black Friday: November 6 – November 23, 2025 – capturing early holiday deals and baseline pricing
    • Additional Discounts During Black Friday Week: November 24 – December 1, 2025 – covering Thanksgiving week through Black Friday (November 28) and Cyber Monday (December 1)

    Our sample encompassed the top-ranked products across subcategories on major retail sites. We also analyzed Share of Search data, which measures brand visibility by monitoring which brand names appear in the top 20 search results for critical keywords.

    Black Friday 2025 Germany: The Big Picture

    Our analysis covered 16,653 SKUs across five major categories. Here’s the snapshot of average discounts during the Pre-Black Friday period compared to Black Friday Week:

    Black Friday Discounts across categories - Germany

    Key Insight: The German market presented a notably different discounting pattern compared to other markets. Pre-Black Friday discounts averaged 7% across all categories, with Black Friday Week adding 5.1% on average. Unlike the UK and US markets where retailers front-loaded promotions heavily, German retailers took a more balanced approach, with several categories actually seeing higher discounts during the main Black Friday Week.

    Beauty led pre-sale discounts at 10%, followed by Electronics at 9.3%. Consumer electronics stood out with the highest Black Friday Week discounts at 9.4%, slightly exceeding even the pre-sale period at 9.3%.

    Shopping Behavior Patterns: The relatively modest discount levels reflect the rational, value-oriented approach of German consumers. With the majority of shoppers setting budgets between €100-€299 and prioritizing wish fulfillment over impulse purchases, German retailers focused on strategic discounting rather than aggressive margin erosion.

    Consumer Electronics

    Consumer electronics dominated German Black Friday interest, with 77% of German shoppers most interested in electronics deals, making it the most anticipated category during the sales period. Our analysis of 2,077 SKUs reveals how discounts varied across subcategories and which brands dominated search visibility in this highly competitive category.

    Subcategory Discount Analysis

    Discounts across key consumer electronics categories in Germany - Black Friday 2025

    This was one of the few categories where Black Friday Week discounts matched or exceeded pre-sale levels. Wearables led pre-sale discounting at 16.3%, followed closely by Audio & Video at 15.8%. During Black Friday Week, these categories saw additional discounts of 12.2% and 12.4% respectively.

    The standout performer was Home Automation, which nearly doubled its additional discount from 7.2% to 13.9% during Black Friday Week. Mobile Devices saw additional discounts dropping from 13.4% pre-sale to 7.1% during Black Friday Week. Meanwhile Gaming doubled additional discounting from 2.1% to 4.3%.

    Share of Search: Brand Visibility Trends

    Our Share of Search analysis reveals significant shifts in brand visibility during Black Friday Week:

    Share of search across electronics brands in Germany - Black Friday

    HP saw the highest share of search across Black Friday at 10.5% pre event and 11.6% during the event. Apple led with a 4.7% increase in visibility, jumping from 1.6% to 6.3%. Asus followed at 4.6%. Motorola surged 3.6 %, while wearables brand Fitbit gained 1.7%.

    In stark contrast, Logitech dropped by 7.8 %, Beats fell 6.2 %, and SanDisk declined 5.5 %. Computer manufacturers Dell and Acer also struggled, falling 2.5 and 2.3 % respectively.

    Home & Furniture

    The Home & Furniture category in Germany showed unique discounting behavior compared to other markets, with additional Black Friday Week discounts (6.7%) actually exceeding Pre-Black Friday levels (6.1%). Our analysis of 1,403 SKUs shows how discounts were distributed across subcategories.

    Subcategory Discount Analysis

    Discounts across key Home and furniture categories in Germany - Black Friday 2025

    Kitchenware led pre-sale discounts at 8.7%. Decor surged from 5% to 7.5%, lighting jumped from 5% to 6.9%, and bedding more than doubled from 3.2% to 6.6%. These substantial increases suggest strategic timing to capture consumer attention during peak shopping days.

    Share of Search: Brand Visibility Trends

    Share of search across home and furniture brands in Germany - Black Friday

    Mattress brand Slumber Solutions saw the highest share of search at 21.8% pre event and 26.1% during Black Friday week. Meanwhile, another mattress brand Swedrea led all brands with a remarkable 10.8% surge in visibility, jumping from 8.8% to 19.6%. Budget-friendly furniture brand Clickbin gained 9.2%, while window treatment brand Sun Zero increased 9% and home textiles brand Safavieh surged 8.6%.

    Premium and established brands also performed well: Caraway Home (cookware) rose 6.6%, mattress brand Serta gained 5.3%, and Slumber Solutions added 4.4%. Royal Gourmet, specializing in outdoor cooking equipment, gained 2.0%.

    However, several mid-tier brands faced steep declines. Addison Rugs dropped 6.9% from 8.5% to just 1.6%, while Sunlifer fell 6.8 % and Maxax declined 6.4 %.

    Health & Beauty

    Our analysis of 6,474 SKUs reveals subcategory-level insights and dramatic brand visibility shifts that highlight the evolving German beauty landscape.

    Subcategory Discount Analysis

    Discounts across key health and beauty categories in Germany - Black Friday 2025

    Pre-Black Friday discounts averaged 10%, with Black Friday Week adding 4.5% on average. Fragrance led pre-sale discounting at 11.6%, positioning perfumes and colognes as attractive early holiday purchases. However, Fragrance saw the smallest Black Friday Week addition at just 2.8%.

    Hair Care and Skincare both showed strong performance across both periods, with Hair Care at 11.1% pre-sale and 4.9% during Black Friday Week, while Skincare registered 10.9% and 5.1% respectively. Skincare’s 5.1% Black Friday Week discount was the highest in the category.

    Men’s Grooming stood apart with notably lower discounts at 3.3% pre-sale and 2.6% during Black Friday Week, suggesting either tighter margins in this subcategory or strategic positioning at different price points.

    Share of Search: Brand Visibility Trends

    Share of search across health and beauty brands in Germany - Black Friday

    L’Oréal Deutschland GmbH emerged as the most visible brand, surging from 37.7% to 51.9%, (+14.2). German pharmaceutical cosmetics company Medicos Kosmetik GmbH & Co. KG gained 4.4 %. Clinique gained 3.3 %, and French luxury brand Sisley added 3.1 %, German natural cosmetics brand Weleda rose 2.7 %, and Japanese prestige brand Shiseido gained 2.5 %.

    The flip side? K-beauty brands and some international names faced significant challenges. Viral make-up brand Tirtir experienced the steepest decline visibility, dropping by 9.2 %. Aveda fell 6.2 %, Elizabeth Arden dropped 5.8 %, and Beauty of Joseon declined 3.8 %. German pharmacy brand Mi.to. Pharm GmbH lost 4.3 %, while Israeli makeup brand GA-DE fell 3.3 %.

    The data reveals a clear German preference for European beauty heritage, pharmaceutical-grade cosmetics, and established prestige brands during Black Friday, rather than trendy K-beauty products or mid-tier international brands.

    Grocery

    While Grocery typically sees more modest discounts compared to discretionary categories, the German market showed interesting patterns with Black Friday Week discounts (3.5%) actually exceeding Pre-Black Friday levels (2.6%). Our analysis of 4,378 SKUs shows how German retailers approached promotions across essential and indulgent subcategories.

    Subcategory Discount Analysis

    Discounts across key FMCG categories in Germany - Black Friday 2025

    Pre-Black Friday discounts averaged just 2.6% in grocery. But Black Friday Week saw this surging to 3.5%, an increase that represents the highest proportional jump among all categories.

    Pet Products and Household Essentials led discounting, with Pet Products at 4.6% pre-sale and an additional 5.5% during Black Friday Week. Meanwhile Household Essentials registered 4.9% and 5.4% respectively.

    Beverages showed particularly strong Black Friday Week performance, jumping from 3.3% to 5.3%, while snacks doubled from 1.7% to 3.4%. Fresh categories maintained minimal promotional activity consistent with perishable inventory constraints: Meat & Seafood saw negligible discounts (0.6% pre-sale, 0.4% Black Friday Week), while Fresh Produce actually decreased from 2.4% to 2% during the main event.

    Share of Search: Brand Visibility Trends

    Share of search across FMCG brands in Germany - Black Friday

    365 by Whole Foods saw the highest share of search at 9% pre event and 12% during Black Friday week. But Sprite led all brands with a 5.1% surge in share of search. Meanwhile, sister brand Coca-Cola experienced one of the steepest declines in our analysis, with visibility plummeting from 6.0% to just 0.1%. Fanta maintained modest search growth at 0.5 %. This stark divergence between two brands from the same parent company suggests vastly different promotional strategies during Black Friday Week.

    Specialty and organic food brands performed well: Amy’s (organic/natural foods) gained 3.6 %, while spice brand McCormick surged 3.1 %. Pet food brand Nutro gained 1.7 %, while value-oriented Great Value added 0.8 %.

    Snack brand Planters gained 0.4 %. On the decline side, Organic Valley dropped 2.8 %, Kraft Mac & Cheese fell 2.4 %, and Swanson’s declined 1.1 %.

    Apparel

    Apparel in the German market showed modest promotional activity compared to other markets, with Pre-Black Friday discounts averaging 5.9% and Black Friday Week at 5.2%. Our analysis of 2,321 SKUs reveals interesting patterns across fashion segments in the German market.

    Subcategory Discount Analysis

    Discounts across key fashion and apparel categories in Germany - Black Friday 2025

    Footwear dominated discounting in both periods, with an impressive 23.3% pre-sale discount, and 15% additional discount during Black Friday Week. Men’s Clothing followed at 12.5% pre-sale, with additional 7.5% during Black Friday Week.

    Activewear presented a unique pattern, with discounts increasing from 7.2% pre-event to additional 9.1% during Black Friday Week: one of the few subcategories to show higher discounts during the main event. Plus Size Clothing saw minimal promotional activity at 0.5% pre-sale and 0.7% during Black Friday Week, the lowest discounts in the entire category.

    Share of Search: Brand Visibility Trends

    Share of search across fashion and apparel brands in Germany - Black Friday

    Danish fashion brand Jack & Jones experienced an unprecedented surge with a staggering 19.4% gain in visibility. Sister brand Vero Moda (also owned by Jack & Jones’ parent company Bestseller) maintained flat visibility at 7.1%. Footwear brand Chaoren gained 7%. Meanwhile activewear giant Under Armour rose by 5%.

    However, established sportswear giants suffered dramatic visibility losses. Adidas experienced an 11.6% decline in share of search. Dream Pairs saw visibility drop by 10.2%, Puma fell 8.4%, and plus-size brand Just My Size declined by 3.8 %.


    Want to understand how DataWeave’s pricing intelligence platform can help your business make data-driven decisions during peak sales events? Contact us to learn more about competitive insights, price intelligence, assortment analytics, content analytics, and digital shelf analytics.

    Check out our analysis on Black Friday Pricing and Discount trends in the USA and UK. Follow our blog for more insights on retail pricing trends, brand visibility analysis, and data-driven commerce intelligence.

  • Black Friday 2025 UK: In-Depth Analysis of Discount Strategies and Brand Performance Across Major Categories

    Black Friday 2025 UK: In-Depth Analysis of Discount Strategies and Brand Performance Across Major Categories

    Black Friday 2025 saw British shoppers spend an average of £430 each this year representing a £91 increase from last year and totaling over £10.2 billion across the sales period.

    The 2025 sales event arrived at a pivotal moment for UK retail. Retail sales fell 1.1% in October 2025 as consumers held back spending in anticipation of Black Friday promotions, according to the Office for National Statistics. Over 2 in 5 UK adults participated in Black Friday shopping, with 45% planning to pick up Christmas gifts at a discount, according to a Barclays study.

    Against this backdrop of cautious optimism and strategic consumer behavior, how did retailers and brands perform across key categories this Black Friday? At DataWeave, we conducted a comprehensive analysis of Black Friday 2025 discounting trends across five major categories in the UK market: Consumer Electronics, Home & Furniture, Health & Beauty, Grocery, and Apparel. Our AI-powered pricing intelligence platform monitored over 71K SKUs across leading UK retailers like Costco, Selfridges, Boots, Debenham’s, Carethy, Argos, John Lewis, Amazon, and more, revealing fascinating patterns in how retailers and brands approached this year’s sale season.

    Our Methodology

    For this analysis, DataWeave monitored average discounts across leading UK retailers during two distinct periods:

    • Pre-Black Friday: November 6 – November 23, 2025 – capturing early holiday deals and baseline pricing
    • Additional Discounts During Black Friday Week: November 24 – December 1, 2025 – covering Thanksgiving week through Black Friday (November 28) and Cyber Monday (December 1)

    Our sample encompassed the top-ranked products across subcategories on major retail sites. We also analyzed Share of Search data, which measures brand visibility by monitoring which brand names appear in the top 20 search results for critical keywords.

    Black Friday 2025 UK: The Big Picture

    Our analysis covered 71,642 SKUs across five major categories. Here’s the snapshot of average discounts during the Pre-Black Friday period compared to Black Friday Week:

    Snapshot of Black Friday Discounts across Key Categories in the UK

    Key Insight: Pre-Black Friday discounts were substantial across all categories. This suggests UK retailers front-loaded their promotions to capture early holiday shoppers, with additional discounts during Black Friday Week adding to the baseline discounts already in place. The extended promotional period transformed Black Friday from a single-day event into a month-long campaign, with 60% of UK shoppers beginning their deal searches as early as October.

    Consumer Electronics

    Consumer electronics remains a cornerstone of Black Friday shopping in the UK, with technology accounting for 48% of planned consumer spending during the sales period, according to PwC.

    AI is transforming how consumers shop for electronics, with AI-driven traffic to retail sites expected to rise 410% year-on-year during the 2025 holiday season. Younger shoppers particularly embraced AI tools, with 17% of Gen Z consumers using AI platforms like Chat GPT and Gemini to source and compare deals. Our analysis of 10,297 SKUs reveals how discounts varied across subcategories and which brands dominated search visibility.

    Subcategory Discount Analysis

    Discount analysis for key consumer electronics subcategories this Black Friday in the UK

    Pre-Black Friday discounts averaged 11.1% across subcategories, while Black Friday Week saw an additional 2.3% discount on average. Audio & Video products led the pre-sale discounting at 16.3%, indicating retailers were eager to move inventory early in the season.

    During Black Friday Week, Home Automation saw the highest additional discount at 2.8%. Wearables and Computers both saw solid 2.5% additional discounts, making them attractive categories during the peak shopping period.

    Share of Search: Brand Visibility Trends

    Our Share of Search analysis reveals significant shifts in brand visibility during Black Friday Week:

    Brand visibility for consumer electronics during Black Friday week in the UK

    Bose dominated with its Share of Search more than doubling from 11.3% to 23.8%, a remarkable +12.5% gain. Lighting brand Philips Hue followed, gaining by +5.4% in share of search during Black Friday week. Notably, Meta, with it’s range of wearables, stands out for a +3.1% increase in visibility.

    Apple’s Share of Search jumped from 10.1% to 16.4%, a gain of 6.4%. At the same time, Fitbit’s share dropped nearly 11%, the steepest decline in the entire electronics category. Meanwhile, Samsung and HP also lost on visibility this Black Friday in the UK.

    Home & Furniture

    The Home & Furniture category continues to attract UK consumers during Black Friday, though with a more measured discounting approach compared to other categories. Our analysis of 16,487 SKUs shows how discounts were distributed across subcategories.

    Subcategory Discount Analysis

    Discount analysis for key home & furniture subcategories this Black Friday in the UK

    Home & Furniture saw Pre-Black Friday discounts averaging 9.1%, with Black Friday Week adding just 1% on average, the lowest additional discount among all categories. Furniture led pre-sale discounts at 13.7%, followed by Bedding at 11.9%. This suggests retailers aggressively promoted larger home goods early in the season to capture deal-seekers.

    During Black Friday Week, Kitchenware saw the highest additional discount at 1.9%, making it attractive for holiday cooking and gifting needs. Conversely, Lighting saw minimal additional promotion at just 0.5%.

    Share of Search: Brand Visibility Trends

    Brand visibility for home and furniture during Black Friday week in the UK

    Made.com, the contemporary furniture brand, saw visibility surge during Black Friday with an impressive 8.9% increase. Similarly, emerging mattress brand Vesgantti gained 5.4%. Rug specialist Gooch Oriental also made significant gains with a 4.9% increase.

    On the flip side, British heritage brands faced challenges. Both Laura Ashley and Julian Bowen saw share of search drop 2.2%. Premium mattress maker Vispring also declined 2.1%, while French cookware brand Le Creuset fell 1.9%.

    Health & Beauty

    Health & Beauty has emerged as a growth engine during Black Friday in the UK. The beauty industry is projected to grow 5% annually through 2030 according to a McKinsey survey. The category continues to demonstrate resilience even as consumers show caution in other discretionary categories. Our analysis of 15,816 SKUs reveals fascinating subcategory-level insights and dramatic brand visibility shifts that highlight evolving consumer preferences in the beauty space.

    Subcategory Discount Analysis

    Discount analysis for key health and beauty subcategories this Black Friday in the UK

    Health & Beauty presented a unique discounting pattern compared to other categories. Pre-Black Friday discounts averaged 14.4%, the second-highest among all categories. But Black Friday Week discounts were also robust at 6.1%, the highest additional discount increase.

    Hair Care led both periods with 16.4% pre-sale discounts and an additional 6.4% during Black Friday Week. Skincare saw the highest Black Friday Week discount at 6.9%, suggesting retailers strategically saved their best skincare promotions for the main event when consumers are actively seeking holiday gift sets. Men’s Grooming stood apart with strong pre-sale discounts of 12.5% but more modest Black Friday Week additions of just 2.3%.

    Share of Search: Brand Visibility Trends

    The UK Beauty category saw some of the most dramatic Share of Search swings in our analysis.

    Brand visibility for health and beauty during Black Friday week in the UK

    Himalaya, the Ayurvedic skincare brand, dominated the category with a stunning 16.8% gain. British cult favorite Dr. Pawpaw exploded from 3.7% to 11.8%, a gain that reflects the brand’s growing mainstream appeal. Face the Future, the skincare specialist, also gained significant ground with a 4.9% increase.

    Budget-friendly British brand Q+A continued its momentum, rising 4%, while prestige names like Guerlain and Tous each gained 3.8%.

    Italian natural beauty brand L’Erbolario saw the steepest decline, with visibility dropping by 9%. Haircare brand Noughty fell 6.1%, and eco-beauty brand So Eco declined by 5.1%.

    Grocery

    While Grocery typically sees more modest discounts compared to discretionary categories, the sector remains a critical part of Black Friday shopping in the UK, particularly as consumers prepare for holiday entertaining and gifting. Our analysis of 11,979 SKUs shows how UK retailers approached promotions across essential and indulgent subcategories.

    Subcategory Discount Analysis

    Discount analysis for key FMCG subcategories this Black Friday in the UK

    Grocery had the lowest discounts across all categories, reflecting the already-thin margins in food retail. Pre-Black Friday discounts averaged just 5.7%, with Black Friday Week adding only 1.2%. Pet Products led pre-sale discounts at an impressive 13%, significantly outpacing other grocery subcategories.

    Beverages and Household Essentials followed with 9.4% and 7.7% pre-sale discounts respectively, and maintained their lead during Black Friday Week with 1.7% and 1.5% additional discounts each. Fresh categories like Meat & Seafood (1.4% pre-sale, 0.6% Black Friday Week) and Frozen Foods (1.7% pre sale, 0.6% additional discounts during Black Friday week) saw minimal promotional activity, consistent with perishable inventory constraints and tight margins.

    Share of Search: Brand Visibility Trends

    Brand visibility for FMCG during Black Friday week in the UK

    Doritos led with a 7.3% surge in visibility. Pepsi delivered an equally impressive performance, rising from 3.6% pre Black Friday to 10.7% during Black Friday week. Fanta too saw 5.2% gain in share of search.

    Conversely, Coca-Cola gained more modestly at 1.6%, while its Sprite brand actually declined 0.9%.

    Apparel

    Apparel remains a Black Friday staple in the UK and is projected to deliver the strongest year-on-year growth of any UK retail segment this festive season. With clothing accounting for 39% of planned Black Friday purchases, the category represents one of the most hotly contested battlegrounds during the sales period.

    Our analysis of 17,063 SKUs, the largest category in our study, reveals interesting patterns across fashion segments that demonstrate both the opportunities and competitive intensity in UK apparel retail.

    Subcategory Discount Analysis

    Discount analysis for key Fashion and apparel subcategories this Black Friday in the UK

    Apparel showed the strongest discounting activity throughout the BFCM period among all categories. Pre-Black Friday discounts averaged 17.2%, with Black Friday Week adding 3.5%, making it one of the most heavily promoted categories.

    Activewear led pre-sale discounts at an impressive 26.7%, with an additional 4.5% discount during Black Friday week. Plus Size Clothing and Men’s Clothing tied for second place in pre-sale discounts at 21.9% each. Notably, Plus Size Clothing saw the highest Black Friday Week discount at 5.4%.

    Women’s Clothing saw robust discounts throughout, with 20.8% pre-sale and an additional 6% during Black Friday Week (the highest additional discount in the category).

    Share of Search: Brand Visibility Trends

    Brand visibility for apparel during Black Friday week in the UK

    White Stuff, the British lifestyle brand, saw a 7.2% surge in visibility during Black Friday. Y2K fashion made a statement as Juicy Couture jumped 5.8 %. Fast fashion player Pretty Little Thing gained in visibility by 5.2%. Comfort brands performed strongly, with Skechers gaining 4.5% and activewear specialist Sweaty Betty rising 3.4%. Even premium denim brand Levi’s gained ground, increasing share of search by 2.2%.

    However, retail giants faced significant visibility challenges. John Lewis saw the steepest decline in the Apparel category, with share of search dropping by 10.6% during Black Friday week. Fast fashion giant Boohoo declined 2.5%. Premium accessory brand Coach’s share fell 4.9%.

    The data suggests UK consumers gravitated toward distinctive brands with clear identities during Black Friday, whether heritage British labels, Y2K nostalgia, or comfort-focused specialists, rather than generalist retailers or fast fashion platforms.


    Want to understand how DataWeave’s pricing intelligence platform can help your business make data-driven decisions during peak sales events? Contact us to learn more about competitive insights, price intelligence, assortment analytics, content analytics, and digital shelf analytics.

    Check out our analysis on Black Friday 2025 Pricing and Discount trends in the USA.

    Follow our blog for more insights on retail pricing trends, brand visibility analysis, and data-driven commerce intelligence.

  • Black Friday U.S. 2025: Comprehensive Analysis of Deals, Pricing Trends, and Brand Visibility Across Key Categories

    Black Friday U.S. 2025: Comprehensive Analysis of Deals, Pricing Trends, and Brand Visibility Across Key Categories

    Black Friday 2025 shattered records once again. U.S. consumers spent a record $11.8 billion online on Black Friday, a 9.1% increase from 2024 and the first time online sales exceeded $11 billion. The National Retail Federation (NRF) projects holiday retail sales (November-December) to grow 3.7% to 4.2% over 2024, with total holiday spending expected to surpass $1 trillion for the first time ever. Against this backdrop of robust consumer spending, how did leading retailers and brands perform across key categories this Black Friday?

    At DataWeave, we conducted a detailed analysis of Black Friday 2025 pricing trends across five major categories: Consumer Electronics, Home & Furniture, Health & Beauty, Grocery, and Apparel. Our AI-powered pricing intelligence platform monitored nearly 80,000 SKUs across leading U.S. retailers like Amazon, Walmart, Target, Macy’s, Home Depot, Sephora, and more, revealing interesting patterns in how retailers and brands approached this year’s sale season.

    Our Methodology

    For this analysis, DataWeave monitored average discounts across leading U.S. retailers during two distinct periods:

    • Pre-Black Friday: Up to November 23, 2025 – capturing early holiday deals and baseline pricing
    • Additional Discounts During Black Friday Week: November 24 – December 1, 2025 – covering Thanksgiving week through Black Friday (November 28) and Cyber Monday (December 1)

    Our sample encompassed the top-ranked products across subcategories on major retail sites. We also analyzed Share of Search data, which measures brand visibility by monitoring which brand names appear in the top 20 search results for critical keywords.

    Black Friday 2025: The Big Picture

    Here’s the snapshot of average discounts during the Pre-Black Friday period compared to Black Friday Week:

    Black Friday snapshot of categories across US

    Key Insight: Pre-Black Friday discounts were significant across all categories. This suggests retailers front-loaded their promotions to capture early holiday shoppers, with additional discounts during Black Friday Week adding to the baseline discounts already in place.

    Consumer Electronics

    Consumer electronics remain one of the most anticipated categories during Black Friday and Cyber Monday. Notably, AI traffic to retail websites grew 805% year-over-year, with consumers using AI tools most frequently for video games, appliances, and electronics categories. Our analysis of 10,356 SKUs reveals how discounts varied across subcategories.

    Subcategory Discount Analysis

    Consumer Electronics discount trends across sub categories this Black Friday

    In Consumer Electronics, Pre-Black Friday discounts averaged 14.6% across subcategories, while Black Friday Week saw an additional 2.6% discount on average. Audio & Video products led the pre-sale discounting at 17.5%, indicating retailers were eager to move inventory early. During Black Friday Week, Audio & Video, Accessories, and Wearables saw the highest additional discounts (2.9-3 %), while Computers and Storage had already been heavily discounted pre-sale, leaving minimal room for further reductions during the main event.

    Share of Search: Brand Visibility Trends

    Our Share of Search analysis reveals significant shifts in brand visibility during Black Friday Week:

    Share of Search shows brand visibility trend across consumer electronics brands in the US

    The Takeaway: Our analysis reveals significant shifts in brand visibility during Black Friday Week compared to the pre-sale period. Computing and mobile-focused brands like Apple and Asus gained substantial ground, while audio brands like Logitech, JBL, and Beats saw their visibility plummet. Apple’s Share of Search jumped from 1.58% to 6.2%, a gain of 4.6%, suggesting strong promotional activity or heightened consumer interest. Meanwhile, Logitech dropped nearly 8%, from 9.9% to just 1.2%.

    Home & Furniture

    The Home & Furniture category continues to be a consumer favorite during Black Friday. Our analysis of 12,610 SKUs shows how discounts were distributed across subcategories.

    Subcategory Discount Analysis

    Discount analysis for home and furniture subcategories this Black Friday

    Home & Furniture saw Pre-Black Friday discounts averaging 13.9%, with Black Friday Week adding just 1.7% on average, the second-lowest additional discount among all categories. Bedding led pre-sale discounts at an impressive 18.6%, followed by Furniture at 16.4% and Outdoor at 15.1%. This suggests retailers aggressively promoted home goods early in the season to capture deal-seekers.

    During Black Friday Week, Bedding maintained leadership with 2.4% additional discounts, while Kitchenware saw the smallest bump at just 0.9%, indicating early promotions had already captured most of the discount opportunity.

    Share of Search: Brand Visibility Trends

    Share of Search shows brand visibility trend across home and furniture brands in the US

    The Takeaway: The Home & Furniture category saw some of the most dramatic Share of Search swings in our entire analysis. Emerging and value-oriented brands dominated the gains, with Swedrea surging from 8.7% to 19.6%, a remarkable 10.8% increase. Similarly, Clickbin and Costway each gained over 9%. On the flip side, established premium brands like Beautyrest and Livabliss saw sharp visibility declines, dropping over 7-8%.

    Health & Beauty

    Health & Beauty has emerged as a growth engine during Black Friday. The beauty industry is projected to grow 5% annually through 2030 according to a McKinsey survey. Our analysis of 16,141 SKUs reveals subcategory-level insights.

    Subcategory Discount Analysis

    Discount analysis for health and beauty subcategories this Black Friday

    Notable finding: Health & Beauty presented a unique discounting pattern compared to other categories. Pre-Black Friday discounts averaged just 7.1%, the second-lowest among all categories. But Black Friday Week discounts were relatively strong at 4.8%. This indicates the Beauty category held back more discounts for the main event.

    Notably, Hair Care was the only subcategory across our entire analysis where Black Friday Week discounts (6%) exceeded Pre-Black Friday discounts (4.6%), suggesting retailers strategically saved their best hair care promotions for the big weekend. Fragrance led pre-sale discounts at 13%, making it an attractive early shopping category.

    Share of Search: Brand Visibility Trends

    Health & Beauty saw some of the most dramatic Share of Search swings in our analysis, driven largely by celebrity-backed brands. Rare Beauty by Selena Gomez exploded from just 1.9% to 13.7%, a whopping 11.8% gain that made it the biggest winner across all categories. Haus Labs by Lady Gaga also surged (+4.1%), while prestige brands like Dior (+2.6%) and Clinique (+2.4%) gained ground.

    The flip side? Retailer private labels took a hit: Beauty Finds by ULTA Beauty collapsed from 24% to 13.7% (-10.3%), and Sephora Collection dropped from 19.8% to 16.0% (-3.8%).

    Share of Search shows brand visibility trend across health and beauty brands in the US

    Key Takeaway: The Beauty category tells a compelling story about the power of celebrity brands during Black Friday. The Share of Search shifts appear to reflect how retailers and brands recalibrated their promotional focus for Black Friday. Celebrity-driven lines rose sharply in visibility, suggesting stronger placement, promotion, or search prioritization during the sale period. At the same time, private-label ranges from ULTA and Sephora lost ground, indicating a pivot away from house-brand visibility in favor of more spotlighted national and prestige brands throughout the event.

    Grocery

    While Grocery typically sees more modest discounts compared to discretionary categories, the sector remains a critical part of Thanksgiving weekend shopping. According to the National Retail Federation, grocery stores and supermarkets ranked as the third most popular shopping destination during Thanksgiving weekend, with 40% of consumers making purchases there. Our analysis of 18,823 SKUs shows how retailers approached promotions across essential and indulgent subcategories.

    Subcategory Discount Analysis

    Discount analysis for grocery subcategories this Black Friday

    Grocery had the lowest discounts across all categories, reflecting the already-thin margins in food retail. Pre-Black Friday discounts averaged just 5.2%, with Black Friday Week adding only 1.5%. Household Essentials and Beverages led pre-sale discounts at 6.9% and 6.7% respectively, and maintained their lead during Black Friday Week with 2.2% additional discounts each. Fresh categories like Meat & Seafood (1.4% pre-sale, 1% Black Friday Week) and Frozen Foods (3.3%, 1%) saw minimal promotional activity, consistent with perishable inventory constraints and tight margins.

    Key Takeaway: Grocery discounting remains conservative, with shelf-stable and household items seeing the most promotional activity. The Beverages and Household Essentials subcategories, which have longer shelf life and higher margins, were the primary battleground for grocery promotions during BFCM 2025.

    Share of Search: Brand Visibility Trends

    Share of Search shows brand visibility trend across CPG brands in the US

    The Grocery category saw some surprising Share of Search swings during Black Friday Week. Most notably, there was a dramatic divergence between beverage giants: Sprite surged from 1.7% to 6.8% (+5.1%), while Coca-Cola collapsed from 6% to just 0.1% (-5.9%). This stark contrast suggests vastly different promotional strategies or algorithmic visibility changes between the two brands. Private label 365 by Whole Foods Market continued its steady rise, gaining 3%, reflecting ongoing consumer interest in store brands as shoppers seek value.

    The Sprite vs. Coca-Cola divergence is one of the most striking findings in our analysis. Additionally, brands like Amy’s (organic/natural foods) and McCormick (spices/seasonings) gained significant visibility.

    Apparel

    Apparel remains a Black Friday staple and performed strongly this year. Our analysis of 21,749 SKUs (the largest category in our study) reveals interesting patterns.

    Subcategory Discount Analysis

    Discount analysis for apparel and fashion subcategories this Black Friday

    Apparel showed strong discounting activity throughout the BFCM period. Pre-Black Friday discounts averaged 13.8%, with Black Friday Week adding 3.8%, the highest additional discount among all five categories. Men’s Clothing and Women’s Clothing led pre-sale discounts at 17.7% and 17.5% respectively, reflecting aggressive early promotions on core apparel. Interestingly, Plus Size Clothing saw the highest Black Friday Week discount at 5.4%, suggesting retailers pushed harder during the main event to drive conversions in this segment. Kids’ Clothing also saw strong Black Friday Week discounts at 4.4%.

    Share of Search: Brand Visibility Trends

    Apparel saw dramatic Share of Search movements during Black Friday Week. Fashion-forward brands dominated the gains: Madewell surged from 6.9% to 16.1% (+9.2%), while Alice + Olivia jumped from 14.7% to 23.6% (+8.9%). Nike also performed strongly with a 7.1% gain. Conversely, outdoor and athletic brands faced steep declines: The North Face dropped from 18.4% to 5.9%, a massive 12.5% decline, the largest in our entire analysis. Adidas fell 7.7%, Beyond Yoga declined 5.3%, and luxury brand Coach by 5%.

    Share of Search shows brand visibility trend across apparel brands in the US

    Key Takeaway: The data suggests fashion-forward and lifestyle brands (Madewell, Alice + Olivia, Saint Laurent) gained visibility at the expense of outdoor/athletic brands (The North Face, Adidas, Beyond Yoga). This could indicate that fashion brands invested more heavily in promotional visibility during the sale period.


    Want to understand how DataWeave’s pricing intelligence platform can help your business make data-driven decisions during peak sales events? Contact us to learn more about competitive insights, price intelligence, assortment analytics, content analytics, and digital shelf analytics.

    Follow our blog for more insights on retail pricing trends, brand visibility analysis, and data-driven commerce intelligence.

  • Own Your Product Matches: Gain The Power of Accuracy and Control at Your Fingertips

    Own Your Product Matches: Gain The Power of Accuracy and Control at Your Fingertips

    AI-powered product matching is the backbone of competitive pricing intelligence. Accurate matches help you compare prices correctly, identify meaningful assortment gaps, and optimize product content. Inaccurate matches distort every one of these insights. In some categories, a single mismatch can cause millions of dollars of lost revenue.

    Retailers and brands know this problem well. Product catalogs are vast. Competitor assortments shift daily. Titles are inconsistent. Product codes are missing. Images vary by region or packaging. Basically, context matters, and AI alone often misses that context.

    This is why a human-in-the-loop approach is essential. It allows product matches to be verified consistently, at scale, and with the context that only people can provide. Many retailers have also told us they want to take this a step further. They want the ability to control and define their own product matches.

    Sometimes that is because they need to fix inevitable errors quickly. Other times, it is because their teams have deeper category knowledge and can make the right judgment calls when AI falls short.

    To make that possible, DataWeave introduced User-Led Match Management. It combines the scale of AI with the judgment of experts within retail organizations. The platform does not just suggest matches. It gives your teams the tools to approve, reject, or refine them. This ensures your competitive intelligence reflects both machine precision and your unique business logic.

    Why AI Matching Alone Falls Short

    AI has changed the speed and scale of product matching. Algorithms can process millions of SKUs quickly. They can detect similarities in text, images, and metadata. But in retail, the stakes are too high to rely on AI alone.

    Here is where AI sometimes falls short:

    • Category complexity: Matching rules that work in electronics may fail in fashion or grocery. An electronics SKU may depend on a model number. A fashion SKU may depend on seasonality. A grocery SKU may depend on pack size or whether it is a private label.
    Product descriptions differ from region to region
    Product pack sizes may be listed differently across marketplaces, regions
    • Data inconsistency: Titles vary. Images differ across regions. These gaps, when large, trip up algorithms.
    • Business context: Should a premium product ever be compared against a budget line? Should seasonal products match year-round items? AI may not know these boundaries.
    • Scale vs. accuracy: Automated systems optimize for coverage. That speed often limits accuracy for a small set of SKUs. Even a 1% error rate across millions of SKUs creates thousands of bad comparisons.

    AI is critical for scale. But accuracy requires human input. DataWeave’s human-in-the-loop framework addresses this by allowing expert reviewers to validate and improve AI outputs. Our user-led match management takes this further by putting control directly into the hands of your business teams.

    What DataWeave’s User-Led Match Management Delivers

    With User-Led Match Management, your team is not a passive reviewer. They become active participants in shaping the accuracy of your competitive intelligence.

    DataWeave's User Led Match Management lets you own your product matches

    Your teams can:

    • Approve, reject, or flag AI-suggested matches. Every suggestion comes with full visibility into why it was made. Your team can validate matches quickly, fix errors, and improve the dataset in real time.
    Approve or reject product matches based on your criteria and business goals
    • Define what “similar” means for your business. A retailer may want to compare multipacks against single packs. A brand may only care about comparing premium products to other premium products. With User-Led Match Management, your team sets tolerance levels that match your strategy.
    • Manually add or refine matches. When AI misses edge cases, your team can add them. This ensures coverage is complete and reflects the true competitive landscape.

    This approach creates a loop where AI, complemented by DataWeave’s human-in-the-loop framework does the heavy lifting, and your teams can fine-tune the results. The outcome is both scale and accuracy.

    Key Features

    DataWeave designed User-Led Match Management to be simple, intuitive, and scalable:

    • Expert-Led Decision Making forms the heart of the system. Rather than trusting AI suggestions blindly, teams gain full visibility into matching logic and can leverage their contextual knowledge of products, categories, and retailers. When the system suggests matching a premium product against a basic alternative, human experts can reject the match and flag it for different criteria. This expertise is particularly valuable for new product launches, seasonal items, or products with complex positioning strategies.
    You can verify matches based on specific attributes like size, type, and more
    • Business Logic Integration: Teams can define matching parameters that reflect their specific strategic needs. A premium brand might establish rules that prevent matches against budget alternatives, while a value retailer might specifically seek those comparisons. Category managers can create different matching criteria for different product lines, ensuring that seasonal items, limited editions, and promotional products are handled appropriately.
    Ensure that your products are matched according to business goals for accurate competitive intelligence
    • Transparent Decision Making: Every match decision creates an audit trail capturing who made the decision, when it occurred, and the reasoning behind it. This transparency is crucial for enterprise environments where pricing decisions need to be defensible and strategies need to be consistent across teams and time periods.
    Review and audit actions to ensure transsparency
    • Scalable Validation: User-Led systems provide bulk operations for efficiency while maintaining oversight. Teams can upload thousands of matches for validation, use filtered views to focus on high-priority items, and leverage automated alerts for matches that fall outside established tolerance levels.
    Review product matches at scale across categories, subcategories.

    Each of these features reduces the friction between AI outputs and business-ready insights.

    Technical Foundation

    The AI foundation behind User-Led Match Management is built for precision and scale.

    1. It uses multimodal AI that combines text, image, and metadata analysis to identify matches even when products are described or displayed differently across retailers.
    2. Domain heuristics apply retail-specific logic, recognizing that “Large” means something different in apparel than in beverages, and that seasonal items require unique treatment.
    3. Knowledge graphs link products across brands, categories, and regions to reveal true relationships even when surface attributes vary.
    4. Through continuous learning, every human correction improves future AI suggestions, making the system smarter and more accurate over time.

    For more information, download our whitepaper here!

    Why This Matters

    Pricing Intelligence

    With DataWeave, accurate and reliable product matching is the standard. Advanced algorithms and built-in quality checks deliver consistently high accuracy, reducing the risk of mismatched products and unreliable insights.

    In the few cases where a match needs review, User-Led Match Management gives your team the ability to validate it quickly and easily. You get full visibility and control, while DataWeave ensures the integrity of the overall matching framework.

    The outcome is true apples-to-apples price comparisons that protect margins, strengthen pricing strategies, and build trust in every decision.

    Assortment Analytics

    Gaps and overlaps only matter when matches are accurate. To understand your true competitive landscape, you need to eliminate false gaps and phantom overlaps that distort assortment insights.

    DataWeave’s advanced Match Management ensures precise product alignment across retailers, categories, and regions, giving you a clear view of your position in the market. At the same time, user-led oversight adds transparent validation, allowing your teams to confirm or refine matches based on their category knowledge.

    The result is a complete and trustworthy view of category coverage that reflects reality, not noise. It helps you identify real opportunities to expand assortments, close gaps, and respond quickly to market changes.

    Content Optimization

    Digital shelf audits only deliver value when the comparisons are accurate. DataWeave ensures that every product is benchmarked against its true competitors so that your insights reflect the real dynamics of your category. For example, a luxury serum is never compared to a basic moisturizer, and a premium electronic device is never matched with an entry-level model.

    With user-led control, your teams have transparent oversight of every match. They can review, validate, or adjust comparisons to make sure each audit aligns with your business standards. The result is a more reliable and actionable view of your digital shelf performance, helping you fine-tune content, optimize visibility, and strengthen conversion across channels.

    Trust and Accountability

    Leadership teams need complete confidence in the data they use to make decisions. User-Led Match Management delivers that confidence by combining the scale of AI with the assurance of human validation. Every match decision is transparent and traceable, giving teams clear visibility into how and why a product was matched.

    This approach builds trust across departments, from analysts to executives. It ensures that every pricing, assortment, and content decision is backed by data that is both accurate and accountable.

    Your Market, Your Rules, Your Insights

    Retailers and brands today need more than fast data. They need data they can trust, shape, and act on with confidence. User-Led Match Management gives them that control. It turns product matching from a static, automated process into a dynamic, collaborative workflow that adapts to how real teams operate.

    Category managers can fine-tune match rules instead of waiting on system updates. Pricing teams can validate critical SKUs in minutes, not days. Digital shelf teams can ensure their audits reflect real competitors, not algorithmic guesses. Executives gain visibility into decisions they can stand behind, supported by transparent data trails and measurable accuracy.

    In short, User-Led Match Management puts control back where it belongs – in your hands. It helps every team move faster, compete smarter, and make decisions powered by data they can truly believe in.

    Reach out to us to learn more!

  • Fueling Agentic Commerce: Introducing DataWeave’s Data Collection API

    Fueling Agentic Commerce: Introducing DataWeave’s Data Collection API

    Commerce Is Entering Its Next Chapter

    Every major shift in commerce has been driven by data. A century ago, shopkeepers relied on ledgers to track sales. In the supermarket era, loyalty cards and barcodes turned transactions into insights. With the rise of eCommerce, clickstream data and online analytics reshaped how products were merchandised and sold.

    Now, we are entering the next chapter: agentic commerce.

    In this new paradigm, autonomous AI agents will handle the tasks that once required teams of analysts, merchandisers, and pricing specialists. Imagine an agent that monitors competitor prices across dozens of retailers, recommends adjustments, and pushes updates to a dynamic pricing engine, all in real time. Picture a shopper’s digital assistant scanning marketplaces for the right mix of price, delivery time, and customer reviews before making a purchase on their behalf.

    These aren’t distant scenarios. They’re unfolding now. Industry analysts estimate the enterprise AI market at $24 billion in 2024, projected to grow to $155 billion by 2030 at nearly 38% CAGR . Meanwhile, 65% of organizations already use web data for AI and machine learning projects, and 93% plan to increase their budgets for it in 2024. The trajectory is undeniable: the next era of commerce will be built on AI-driven decision-making.

    And what fuels those AI-driven decisions? Data. Reliable, structured, timely, and compliant data.

    The Data Problem No One Can Ignore

    Here’s the paradox: just as data has become most critical, it has also become harder to acquire.

    For data and engineering leaders, the challenges are painfully familiar:

    • Old school scrapers that collapse whenever a site changes its HTML or introduces new interactivity.
    • Constant maintenance cycles, with engineering teams spending 20-40 hours a week debugging, rerunning, and patching scripts.
    • Low success rates, with in-house approaches succeeding just 60-70% of the time.
    • Complex infrastructure, from managing proxies to retry logic, pulls attention away from higher-value work.

    But the costs go far beyond engineering frustration.

    For retailers, broken pipelines mean competitive blind spots. A pricing team without reliable visibility into competitor moves can’t respond fast enough, risking lost margin or missed sales. Merchandising teams trying to optimize assortments are left with incomplete data, making poor stocking decisions inevitable.

    For brands, unreliable data disrupts visibility into the digital shelf. Products might be misplaced in search rankings, content could be outdated or incomplete, and reviews could signal issues, but without continuous monitoring, those signals are missed until it’s too late.

    For AI and ML teams, poor-quality training data means underperforming models. Without clean, consistent, and large-scale inputs, even the most sophisticated algorithms produce flawed predictions.

    Finally for consulting firms and research providers, fragile collection systems can compromise credibility. Clients expect robust, evidence-backed recommendations. Data gaps erode trust.

    The reality is stark: fragile pipelines don’t just waste engineering hours. They undermine competitive agility, customer experience, and business growth.

    Enter the Data Collection API

    DataWeave’s Data Collection API is a self-serve, enterprise-scale platform designed to deliver the data foundation today’s enterprises need, and tomorrow’s agentic AI systems will demand.

    Data Collection API Dashboard_DataWeave

    At its core, the API replaces brittle scrapers and ad hoc tools with a resilient, adaptive, and compliant data acquisition layer. It combines enterprise reliability with retail-specific intelligence to ensure that structured data is always available, accurate, and ready to power critical workflows.

    Here’s what makes it different:

    • Enterprise-scale throughput: The API can process thousands of URLs in a single batch or handle continuous, high-frequency scrape. Whether you need daily pulses or near real-time monitoring, it scales with you.
    • Flexible access modes: Technical teams can integrate directly into internal workflows via API, while business users can configure jobs through a no-code interface. Everyone gets what they need without bottlenecks.
    • Adaptive resilience: As websites evolve, the API adapts automatically. No frantic patching, no firefighting.
    • Structured outputs, your way: Clean JSON, CSV, or WARC formats are delivered directly into your environment – AWS S3, Snowflake, GCP, or wherever your data stack lives.
    DataWeave's Data Collection API provides output in your preferred format
    • Built-in monitoring and self-healing: Automated retries, real-time logs, and usage dashboards keep teams in control without manual oversight.
    • Compliance by design: WARC-based archiving and SOC2 alignment ensure data pipelines are auditable, trustworthy, and enterprise-ready.

    This isn’t about scraping pages. It’s about creating a reliable data utility, a system that transforms raw web inputs into structured, actionable data streams that enterprises can trust and scale on.

    Who It’s Built For (And How They Use It)

    The Data Collection API isn’t limited to one role or industry. It’s been designed with multiple stakeholders in mind, each of whom can apply it to solve pressing challenges:

    Retailers and Consumer Brands

    Retailers live and die by competitive awareness. With the API, pricing teams can monitor SKU-level prices and promotions across channels, ensuring they don’t leave margin on the table. Merchandising leaders can track assortment coverage, identifying gaps relative to competitors. Digital shelf teams can measure search rankings, share of voice, and content completeness. The result is faster responses, stronger category performance, and fewer blind spots in shopper experience.

    Data Collection can be customised and scaled with our API

    AI & Machine Learning Teams

    AI teams depend on data at scale. Whether training a natural language model to understand product descriptions or a computer vision system to analyze images, the Data Collection API delivers the structured, high-quality inputs they need. Reviews, ratings, attributes, and product images can all be captured and delivered at scale. For teams building predictive models, from demand forecasting to personalization, the difference between mediocre and world-class often comes down to input quality. This API ensures AI systems are always learning from the best data available.

    Receive updates for your data collections

    Retail Intelligence & Pricing Platforms

    Technology providers serving retailers and brands face unforgiving client expectations. Missed SLAs on data delivery can mean churn. By using the Data Collection API as their acquisition layer, platform providers gain enterprise reliability without rebuilding infrastructure from scratch. They can scale seamlessly with client needs while maintaining the integrity of the insights their customers rely on.

    Marketing & Advertising Teams

    For marketing leaders, competition is visible every time a shopper searches. The API enables teams to track keyword rankings, ad placements, and competitor promotions with consistency. Instead of anecdotal data or partial coverage, marketers get a full picture of their brand’s digital presence and the strategies competitors are using to capture share of voice.

    Consulting Firms & Research Providers

    Consultancies and market research agencies deliver strategy. But a strategy without evidence is just opinion. The API allows these firms to back every recommendation with structured, large-scale data. Whether advising on pricing, benchmarking performance, or publishing analyst research, firms can deliver trustworthy insights without taking on the cost or distraction of building fragile data pipelines.

    The diversity of these use cases demonstrates why the API is a platform for collaboration across industries, ensuring every stakeholder, from engineers to strategists, has the reliable data foundation they need.

    Why DataWeave, Why It Matters

    Many vendors claim to deliver web data. Few can deliver it at enterprise scale, with commerce-specific expertise, and with proven ROI.

    What sets DataWeave apart isn’t just that we provide data; it’s the way we do it, and the outcomes we enable.

    • Commerce expertise baked in: With 14+ years of experience powering the world’s leading retailers and brands, DataWeave brings domain-specific intelligence that generic scraping vendors simply can’t. Our schemas are designed for commerce. Our defaults are smarter because they’re informed by retail realities.
    • Adaptability without firefighting: Most tools break when websites evolve. Our API adapts automatically, minimizing the need for engineering intervention. Teams stay focused on innovation, not maintenance.
    • Accessible to everyone: Whether you’re a senior data engineer automating workflows or a business analyst configuring a quick scrape, the API meets you where you are with both API and no-code interfaces.
    • Enterprise-grade trust: Reliability and compliance are built in, not bolted on. With SLA-backed delivery, SOC2 alignment, and audit-ready archiving, the API is trusted by enterprises that can’t afford uncertainty.

    This combination makes the Data Collection API not just a technical solution but a strategic partner for enterprises preparing for the age of agentic commerce.

    A Foundation for the Future

    The Data Collection API is more than an answer to today’s frustrating data problems. It represents a strategic foundation for tomorrow’s growth, designed to scale alongside the increasingly complex demands of commerce in the AI era.

    At the heart of DataWeave’s vision is the Unified Commerce Intelligence Cloud, a layered ecosystem that transforms raw digital signals into strategic insights. The Data Collection API is the entry point, the essential first layer that ensures enterprises have a reliable supply of the most important raw material of the digital economy: data.

    • Collection: Enterprise-grade acquisition of web data at scale. From product pages and search results to reviews and promotions, enterprises can finally count on continuous, structured inputs without worrying about fragility or failure.
    • Processing: Once collected, data is normalized, enriched, and matched across sources. What was once noisy and inconsistent becomes clean, comparable, and immediately actionable.
    • Intelligence: On top of this foundation sits advanced analytics, solutions for pricing optimization, assortment planning, promotion tracking, and digital shelf visibility, enabling sharper decisions at the speed of the market.

    This progression means enterprises don’t have to transform overnight. Many start small, solving urgent challenges like competitive price tracking or digital shelf monitoring. From there, they can expand naturally into richer intelligence capabilities, knowing that their data foundation is already strong enough to support more ambitious use cases.

    And as agentic AI systems begin to take on a larger share of decision-making, the importance of that foundation grows exponentially. These autonomous systems cannot operate effectively without clean, continuous, and contextual data. Without it, even the most sophisticated AI will falter, making poor predictions or incomplete recommendations. With it, they can operate at full capacity, powering dynamic pricing, real-time demand forecasting, and personalized shopping experiences at scale.

    The Data Collection API isn’t just about reducing engineering pain today. It’s about preparing enterprises to compete and win in an AI-driven marketplace that never sleeps.

    Getting Started

    For teams tired of fragile scrapers, this is a chance to reset. For enterprises preparing for the next era of commerce, it’s a chance to build a foundation that can scale with them.

    If your teams are still struggling with generic and inflexible data scrapers, request a demo now to see the DataWeave’s Data Collection API in action.

  • From the Leaders’ Table: Key Insights from DataWeave’s GCC Executive Roundtable

    From the Leaders’ Table: Key Insights from DataWeave’s GCC Executive Roundtable

    The retail landscape has reached a point where traditional strategies are no longer enough. Tariff shocks are driving up costs in categories like electronics and apparel, while freight disruptions are extending lead times. Retail executives are now operating in an environment of unprecedented complexity.

    In response, many of the world’s largest retailers and brands are shifting critical operations to Global Capability Centers (GCCs) in regions such as India, East Asia, and Africa. Once focused on back-office support, GCCs are rapidly evolving into strategic intelligence hubs powering high-impact decisions on pricing, assortment, content, analytics, and more. These decisions consistently influence both top-line growth and bottom-line performance for multibillion-dollar enterprises.

    At DataWeave, we’ve been working closely with GCCs to help them achieve technical, tactical, and strategic advantages through actionable market intelligence. To further engage with the community and exchange ideas, we recently hosted our first GCC VIP Roundtable in Bengaluru. Leaders from organizations including JC Penney, Lowe’s, Kenvue, and ARKO joined us for a series of candid and insightful discussions on retail’s most pressing challenges and the evolving role of GCCs in driving leadership amid disruption.

    In this article, we share the key themes, challenges, and solutions that emerged from these conversations.

    Where GCCs Are Facing the Biggest Challenges

    The Adoption Lag Challenge

    A recurring concern among GCC leaders at the roundtable was the delay in translating insights into action. As one leader noted, “We have the data and insights at our fingertips, but it can take our internal teams an entire quarter to respond.” Others agreed with this sentiment, recognizing that such adoption lags create a competitive risk.

    The pattern is consistent across organizations, while GCCs excel at generating insights, real-time responsiveness at the store level remains aspirational due to change management challenges and operational inertia.

    The Integration Imperative

    Our discussion with GCC leaders coincided with creeping evidence for the impact of tariffs across retail categories. Managing competitive intelligence is a difficult enough challenge. Now, pricing strategies must account for not just competitive positioning, but also rapid cost structure changes that vary dramatically by product origin and category.

    Price Increases across key Categories 2024 Vs. 2025_Price Inflation
    • Home & Furniture categories are experiencing the steepest price inflation trajectory, with increases reaching 4.7% by mid-2025.
    • Toys and Electronics follow closely at 3.8% and 2.1% respectively, both heavily dependent on international supply chains.

    The Strategic Intelligence Evolution

    Leading GCCs are responding by reimagining their role around critical capabilities:

    Beyond Traditional Competitive Intelligence

    Pricing and content strategies now require integration of multiple variables:

    • Category-specific trends
    • Tariff impacts
    • Competitive positioning
    • Broader macroeconomic factors

    Traditional pricing models that worked in stable environments are proving inadequate for this new reality.

    Real-Time Responsiveness as a Competitive Edge

    The shift from periodic reporting to always-on intelligence systems emerged as a critical theme. GCC leaders discussed the need for:

    • Technical Infrastructure: Moving from batch processing to streaming data architectures, handling millions of SKUs daily
    • Analytical Capability: AI-driven data refinement, including computer vision and natural language processing
    • Organizational Agility: Breaking down silos between marketing, merchandising, and operations

    Regional Complexity Management

    The group highlighted a key gap in the competitive intelligence data they receive. Insights often overlook region-specific nuances such as local competitive landscapes, regulatory requirements, and consumer preferences. They stressed that effective pricing strategies must go beyond base pricing to also factor in card-linked offers, loyalty programs, and delivery options.

    Operational Challenges

    Several technical and operational issues emerged during discussions:

    • Data Quality and Accessibility: Questions around whether platforms provide pre-refined data or raw dumps, and the availability of implementation layers for easy visualization
    • Change Management: The persistent challenge of translating insights into action at operational levels

    The Path Forward: Building Intelligence-Driven GCCs

    The most successful GCCs of the future will deliver what our attendees called “closed-loop intelligence.” These insights will not only inform decisions but also continuously improve through feedback and results tracking.

    This is something that DataWeave excels in.

    AI-POWERED COMMERCE INTELLIGENCE FOR END-TO-END ECOMMERCE OPTIMIZATION

    This requires investment in three core areas:

    1. Data Acquisition: Comprehensive, timely product and pricing data collection across retail platforms
    2. Intelligence Refinement: AI-powered transformation of raw data into meaningful relationships across retailers, brands, categories, and competitive landscapes
    3. Insight Delivery: Flexible output capabilities serving everything from executive dashboards to automated pricing systems

    Key Takeaways for GCC Leaders

    Our roundtable revealed that successful GCCs share common characteristics:

    • Proactive Decision-Making: Moving beyond reactive responses to anticipate market changes
    • Integrated Intelligence Systems: Combining traditional competitive data with modern digital signals, including social media trends
    • Cross-Functional Impact: Establishing strategic partnerships with business units rather than transactional service relationships
    • Measurable ROI: Proving value through pricing strategies that demonstrably improve margins

    The retail industry will likely become more complex, not less, in the coming years. The GCCs that invest in sophisticated competitive intelligence capabilities today will be the ones helping their organizations not just navigate this complexity, but thrive within it.

    The depth of insight and openness in the discussions during the event underscored the value of bringing this community together. As we continue to strengthen our connections with GCC leaders, we look forward to hosting more such forums.

    If you’d like to be part of the conversation, reach out to us today!

  • Prime Day 2025: Shadow of Rising Prices Over Record Sales

    Prime Day 2025: Shadow of Rising Prices Over Record Sales

    Amazon Prime Day 2025 generated a record-breaking $24.1 billion in US online sales during its extended four-day run (July 8–11, 2025). While the expanded format helped broaden participation, it also diluted the urgency and daily peaks that typically define Prime Day.

    Beneath this record-setting performance lies a more complex reality. Persistent inflation, shifting consumer behavior, and rising pricing pressures created a retail environment very different from previous years, one where higher baseline prices often replaced the deep discounts shoppers expected.

    To understand these dynamics, DataWeave analyzed pricing and visibility trends across 11,495 products using our proprietary AI platform. The study focused on four major categories – Consumer Electronics, Apparel, Home & Furniture, and Health & Beauty – comparing identical SKUs from Prime Day 2024 and 2025, and tracking changes in both organic and sponsored share of search for leading brands.

    The results reveal clear year-over-year price increases: Apparel led with a 9.5% rise, followed by Health & Beauty (7.9%), Consumer Electronics (5.1%), and Home & Furniture (3.9%). In total, 47% of tracked products saw higher prices, indicating that this year’s record sales were achieved in an environment of elevated base pricing rather than deeper discounts.

    Category-wise year-on-year price surge 2024 Vs. 2025_Amazon Prime Day

    Multiple converging forces shaped the retail landscape leading into Prime Day 2025, pushing baseline prices higher even before promotions began.

    • Supply Chain Pressures: Ongoing disruptions and elevated shipping and production costs continue to shape the cost structure across categories.
    • Trade Policy Factors: Recent tariff measures and trade regulations may be contributing to upward pricing trends in certain categories, particularly those with high import dependence such as electronics and home goods.
    • Labor and Operating Costs: Rising wages, transportation expenses, and general operating overhead are placing additional pressure on retailer margins and influencing pricing decisions.
    • Currency Fluctuations: Shifts in exchange rates continue to add variability to the cost of imported goods, especially in globally sourced categories like electronics and apparel.

    These combined pressures created a pricing environment where brands had less room for deep discounting, shaping not just how products were priced, but also how aggressively they were promoted.

    To better understand the impact, we compared Prime Day 2025 prices to those from Prime Day 2024 for the same SKUs across major categories. This year-over-year view highlights how elevated baseline prices, driven by the factors outlined above, shaped the shopping experience and promotional strategies.

    Consumer Electronics

    • JBL prices increased 24% year-over-year, the highest among major electronics brands.
    • Amazon’s own brand saw prices increase by 22%.
    • Beats saw a significant 9% increase, while Sony and Samsung both experienced 8% price increases. Apple prices went up by 6%, and Google saw a 7% increase.
    • Meanwhile, other established brands like LG and Motorola maintained minimal increases at 1%, Lenovo at 2%, Soundcore at 3%, and Bose and Hisense both at 5%.
    Consumer Electronics Price Change_Share of Search_Prime Day 2025

    Apple dominated visibility gains, jumping to 17.2% share of search during Prime Day with a 10.7% growth, likely driven by promotional focus on premium devices. Soundcore also saw significant gains of 10.3%, reaching 15.8% share of search.

    LG and Amazon Basics both achieved strong 5.1% growth. Hisense gained 4.7% share with 5% price increases. Samsung and Amazon maintained strong positions with modest gains of 1.9% and 1.3% respectively.

    However, several brands lost ground, with Sony declining most significantly by 1.8% share despite its strong market position, followed by Google (-0.5%) and Motorola (-0.4%).

    Apparel

    • Party Pants showed the highest price increases at 18% year-over-year, followed by casual wear brand Dokotoo at 13%.
    • Athleisure brand CRZ Yoga saw prices increasing by 11%, while Under Armour saw prices rise by 10%.
    • While Reebok experienced a significant 12% increase, Adidas saw 5% price increase.
    • Meanwhile, innerwear brands like Hanes and Cupshe saw minimal price increases at 1%, while Coofandy also saw minimal increases of 1%.
    • Amazon’s own Amazon Essentials maintained minimal price increases of 2%, and Jockey saw modest increases of 4%.
    Apparel Price Change_Share of Search_Prime Day 2025

    T-shirt brand Gildan led share of search gains with 4.0% growth along with a 5% price increase. Party Pants achieved 3.3% growth with 18% price increases, while CRZ Yoga gained 2.8% share and 11% price increases.

    Amazon Essentials and Mens’ apparel brand Coofandy both improved share by 2.1%, with Amazon Essentials keeping price increases to just 2% and Coofandy at 1%. Under Armour gained 2.3% share with 10% price increase.

    However, several brands lost ground, with Hanes declining significantly by 4.4% while keeping price increases to just 1%, followed by Cupshe (-1.4%) and Blooming Jelly (-0.7%).

    Home & Furniture

    • Mattress brand Best Price Mattress increased prices 23% year-over-year, the highest in the category.
    • Amazon Basics showed 12% price increases, demonstrating strategic private label pricing.
    • Home Improvement and Appliances brand Black+Decker saw a pricing increase of 11%.
    • Better Homes saw an 8% increase, while most other brands saw price increases between 4-7%.
    Home & Furniture Price Change_Share of Search_Prime Day 2025

    Appliance brand Acekool led visibility gains with 3.7% growth along with a 5% price increase. Amazon Basics improved significantly with a 3.6% share growth alongside its 12% price increases. Better Homes achieved 3.1% gains with 8% price increases.

    Ironck gained 2.6% share with 7% price increases, and furniture brand Furinno improved by 1.5% with 5% price increases. However, several brands lost ground, with Samsonite declining most significantly by 4.4%, Best Price Mattress lost 2.1% share with its massive 23% price increase, and Allewie declined by 1.7% with 4% price increases.

    Home goods face elevated pressure from expanded steel and aluminium tariffs increased to 50%.

    Health & Beauty

    • Minimalist saw prices increase 25% year-over-year, the highest in the category, followed by Tresemme at 20% and Oral-B at 17%.
    • Neutrogena increased pricing by 14%, while Sun Bum rose 13% and Viking Revolution 12%.
    • Nyx Professional Makeup saw price increases of 10%, Dove and L’Oréal Paris both at 8%, and Maybelline at 7%.
    • Value-positioned brands saw modest price increases, with Philips Sonicare (6%), OGX and Banana Boat (both 5%), e.l.f. (4%), and Garnier (3%).
    • Notably, Cetaphil, Colgate, and Sensodyne all kept increases to just 1%.
    Health & Beauty Price Change_Share of Search_Prime Day 2025

    Dove led visibility gains with 9.3% growth along with 8% price increases. Minimalist achieved remarkable 8.1% growth even with the category’s highest 25% price increases, and Maybelline gained 4.9% share with 7% price increases.

    Cetaphil improved by 4.8% with minimal 1% price increases, while e.l.f. gained 4.1% share with 4% price increases. However, several established brands lost share, with Oral-B declining most significantly by 9.3% along with 17% price increases, followed by OGX (-7.9%) with 5% price increases, Viking Revolution (-6.5%) with 12% increases, and Philips Sonicare (-5.5%) with 6% price increases.

    In Conclusion

    Prime Day 2025 underscores the shifting realities of retail, where persistent pricing pressures, evolving consumer behavior, and complex market forces are redefining how promotions are planned and executed. In this environment, success hinges on having the right intelligence at the right time, empowering brands to target promotions strategically, protect margins, and maintain visibility in a crowded marketplace.

    As competition intensifies, the ability to anticipate trends and respond with precision will separate market leaders from the rest. At DataWeave, we equip retailers and brands with the insights needed to navigate these changes and make data-backed decisions that drive sustainable growth.

    Stay connected to our blog for ongoing analysis of pricing, promotion, and visibility trends or reach out to us today to learn more.

  • Turning Headwinds Into Wins: How Brands Can Navigate Price, Share, and Visibility Amid Tariff Disruption

    Turning Headwinds Into Wins: How Brands Can Navigate Price, Share, and Visibility Amid Tariff Disruption

    Disruption Is Now the Baseline

    Tariffs can spike landed costs overnight, regulations rewrite labelling rules, and competitors slash prices before your team finishes its daily stand-up. And yet, some consumer brands thrive.

    The winning brands see changes early, decide quickly, and execute flawlessly across the digital shelf. This post blends three decades of pricing and merchandising expertise with timely digital shelf insights from DataWeave, offering a clear path forward for brands navigating today’s volatile retail environment.

    From Cost Shock to Chronic Uncertainty

    Tariffs are no longer just one-off headlines; they’ve become an unpredictable, ongoing variable in the global marketplace. The true challenge isn’t always the duty rate itself, but the constant whiplash of not knowing if, when, or how much that duty will change. This pervasive uncertainty is having a tangible impact:

    • Market Uncertainty: Tariff talk alone disrupts planning and fuels market instability.
    • Operational cost inflation: Shifting trade rules raise expenses across sourcing, freight, and distribution.
    • Compromised SKU-level Margin: The profitability of individual products is under constant threat.
    • Shrinkflation: Brands shrink product quantities to mask rising costs, risking consumer trust.

    Unpredictable Competitive Response: Delaying price moves while watching competitors can erode margins as much as tariffs.

    To stay ahead, pricing decisions must be stress-tested against multiple tariff scenarios and aligned with likely competitor reactions. Timing matters as much as accuracy, move too soon or too late, and margins suffer either way.

    The Tariff Math No One Can Afford to Get Wrong

    When it comes to tariff disruption, the difference between profit and loss often hinges on a precise understanding of a three-step process. Get any part of this chain wrong, and the financial ripple effect can undermine pricing and promotions. The duty you pay, therefore, is the direct result of the following three critical steps:

    Step 1: Harmonized System (HS) Code

    • What it is: A six- to ten-digit classifier that drills down to product sub-types.
    • Why it matters: A single digit change can shift an item into a higher-tariff bracket.

    Step 2: Country of Origin

    • What it is: The nation in which the imported item was made.
    • Why it matters: Mis-tagging the origin can lead to mis-pricing and inaccurate margin calculations.

    Step 3: Trade-Agreement Overlay

    • What it is: Differentiation between the World Trade Organization (WTO) baseline tariffs and special trade agreements (e.g., USMCAUnited States-Mexico-Canada Agreement).
    • Why it matters: The same HS code can result in significantly different duties, up to a 10% swing, depending on the originating country (see the example below).

    This isn’t just about paying the correct duty; it’s about safeguarding your bottom line in a global marketplace where every digit and every designation carries substantial weight.

    The wrong origin, the wrong rule, the wrong margin.

    Hard Numbers: Where Prices Are Already Climbing

    DataWeave’s latest digital shelf analysis shows import-driven price inflation diverging sharply by source country.

    The intricate dance of HS codes, country of origin, and trade agreements directly translates into the prices consumers see. And the data doesn’t lie. Below, we delve into the hard numbers: where prices are already climbing, as illuminated by DataWeave’s latest digital shelf monitoring, showing significant import-driven price inflation by source country.

    • China: Products sourced from China are up 5%. This is largely attributable to the numerous tariffs currently imposed on Chinese goods.
    • Mexico: Prices for products from Mexico have risen by 3%.
    • United States: Interestingly, even U.S.-sourced products show a 3% increase.
    tariff price increases

    This rise in U.S. product prices might seem counterintuitive if tariffs are solely focused on imports. However, the reality lies in the global supply chain for many products.

    Consider guacamole as an example: While the final product might be “Made in the USA,” its components often come from various international sources. Avocados might be imported from Mexico, lime juice from Central America, and seasonings from India or China. Even packaging could originate in Asia. Each of these imported components can be subject to tariffs. Therefore, even if an item is assembled in the U.S., the tariffs on its constituent parts contribute to an overall price increase, explaining the rising rates for U.S.-sourced goods.

    Action step: Map tariff exposure at both finished-goods and component-level to avoid “Made in USA” blind spots.

    Timing Is a Competitive Weapon

    With duty tables and competitor reactions changing fast, the question is: move first or follow? Early movers recoup cost fastest but risk overshooting if tariffs ease; laggards may enjoy a brief price advantage but suffer sudden margin compression.

    The Strategic Dilemma

    The table below illustrates this strategic choice and its potential outcomes:

    Shrinkflation: Margin Patch or Trust Erosion?

    Beyond direct price adjustments, many brands are turning to shrinkflation to manage tariff-driven cost pressure, shaving net weight instead of hiking prices. DataWeave’s analysis reveals an average package reduction of 5 – 6%, with extreme cases reaching 15 – 25%, sometimes even coupled with a shelf-price increase.

    While this can cushion immediate margin, it comes at a significant cost: brand credibility. Savvy shoppers quickly spot these changes, sharing “before-and-after” photos online and fueling consumer frustration. What begins as a margin patch can rapidly erode trust and damage long-term loyalty.

    Ultimately, navigating this volatile environment requires dynamic intelligence and a holistic pricing strategy that balances profitability with market share and, crucially, consumer trust.

    Price Hikes May be Inevitable, But You Can Still Run Your Digital Shelf

    Tariff‑driven cost pressure can force list‑price increases, but it does not dictate how well your products show up, sell through, or satisfy shoppers online. Those outcomes still hinge on five levers that live entirely inside your control. Master them and you cushion margin hits while protecting (or even expanding) share.

    The Five Levers of Digital‑Shelf Control

    • Inventory Depth – Maintain online in‑stock rates above 95 percent for high‑velocity SKUs and flag substitute logic when unavoidable out‑of‑stocks occur.
    • Content Quality & Accuracy – Keep titles keyword‑rich, imagery crisp, and attributes complete so search filters never bury you.
    • Ratings & Reviews Cadence – Proactively request fresh reviews to earn retailer search boosts and reassure value‑conscious shoppers.
    • Retail‑Media Precision – Bid where pages are healthy and in‑stock; pause spend on broken listings that leak conversion and ROAS.
    • Fulfillment Excellence – Monitor pick‑pack accuracy, on‑time delivery, and substitution rates; each one influences retailer algorithmic visibility.

    Content Hygiene Keeps You Visible, Compliant, and Conversion-Ready

    Missing or incorrect product attributes (e.g., “gluten-free,” “caffeine content”) can swiftly jeopardize both regulatory compliance and your product’s fundamental search visibility. Simply put, if it’s not labeled right, it won’t be found.

    This impact plays out in two crucial areas:

    1. Retailer Search Visibility: Filter logic on major e-commerce platforms like Target.com, Walmart.com, and Instacart is increasingly driven by precise attribute tags (e.g., “gluten-free,” “BPA-free,” “0g added sugar”). Fail to provide or correctly format these claims, and your product will simply never appear when shoppers apply these critical search filters. You become invisible to a motivated audience.
    2. Regulatory Compliance: Global regulatory bodies, including the U.S. FDA and EU authorities, now treat online product detail pages as officially regulated labeling space. This means that a single missing allergen statement or an inaccurate nutritional claim can trigger severe consequences, from product takedowns and hefty fines to a devastating “straight-to-zero” share of search. Non-compliance isn’t just a legal risk; it’s a direct threat to your market presence (see example below).

    The Hygiene Playbook: Audit → Score → Fix → Grow

    Your Product Detail Pages (PDPs) are your digital storefronts, and they need to be impeccable. Modern content-intelligence tools are like vigilant auditors, constantly scanning, structuring, and scoring every PDP across your retail network.

    Tools like DataWeave do the heavy lifting by:

    • Surfacing critical gaps: They’ll pinpoint issues like blurry images, inaccurate titles, or missing nutrition information.
    • Optimizing for search: They ensure your product attributes align with live search filters, turning claims into clicks.
    • Flagging compliance risks: You’ll know about potential issues before regulators or retail partners ever do.
    • Quantifying your impact: Get a clear Content Quality Score that your teams can own and improve, week after week.

    When you execute this well, it’s not just about tidying up; it’s a powerful growth engine. This proactive approach fuels every step of the digital customer journey – from getting found, to winning the click, converting the cart, and ultimately, capturing reviews that boost your search rankings.

    A Case Study: Bush’s Beans Converts Visibility into Revenue

    Before Bush’s Beans achieved rapid success with their “audit → scorecard → rapid-fix” approach, they confronted a significant hurdle. Here’s how they overcame it to drive impressive revenue growth.

    The Challenge

    Bush’s Beans saw its e-commerce contribution stall at just 1.5 percent while competition in canned goods intensified. A quick audit revealed three root causes:

    1. Dipping online sales that signalled slipping visibility and conversion.
    2. Fragmented product content across major retailer sites as images, titles, and claims were inconsistent or missing altogether.
    3. Heavier category competition  making it harder to hold first-page search positions.

    The Fix

    The brand adopted DataWeave’s Digital Shelf Analytics to create a single source of truth for every PDP. A lean internal team then:

    • Ran content audits across priority retailers to surface incomplete or non-compliant attributes.
    • Prioritized quick wins focusing on high-velocity SKUs where simple edits (e.g., adding pack-size keywords or allergy statements) would unlock search filters.
    • Tracked progress weekly using an automated scorecard to keep everyone focused on the next set of fixes.

    The Win

    Twelve months later the numbers told the story:

    Bush’s Beans transformed their product data into a strategic asset, significantly improving online visibility, safeguarding brand reputation, and driving sustained revenue growth. Accurate and complete product pages ensured compliance and boosted search rankings, directly increasing sales. While you can’t control external factors like tariffs, you can control the quality and compliance of your product pages and that control directly translates margin pressure into market share gains.

    Unified Insight: Turning Signals into Sustained Advantage

    Imagine one living dashboard where every digital shelf signal like timely price moves, share-of-search shifts, retail media spend, on-shelf availability gaps, compliance flags, MAP breaches, plus content and review health flows together. With that single lens, the “whose numbers are right?” debate disappears and cross-functional teams can act in minutes rather than days.

    A consolidated feed lets you:

    • Build market awareness: Spot competitor price changes as they happen, understand who owns first-page search, and measure the true lift of retail media campaigns.
    • Mitigate emerging risks: Surface impending out-of-stocks before rank erodes, catch claim or label errors ahead of audits, and receive instant alerts when a seller breaks MAP.
    • Activate growth levers: Prioritize content edits that open search filters and use ratings and reviews trends to fine-tune messaging and assortment.

    Brands that weave these signals into one workflow move faster than the disruption. That’s the connective tissue highlighted in our recent post on pairing Digital Shelf Analytics with Digital Shelf Impact Modelling: when granular shelf data sits beside strategic performance metrics, smarter decisions follow.

    A platform like DataWeave brings the pieces together quietly ingesting millions of price checks, availability reads, and PDP audits each day, then presenting only the next best actions. The payoff is simple: sharper market awareness, lower operational risk, and growth that compounds with every iteration.

    Keep Moving, Keep Winning

    Tariffs, evolving regulations, and agile competitors are no longer storms; they are the climate. Brands that pair a clear, shared insight stream with rapid execution turn volatility into durable advantage. Keep your data united, keep iterating on the five digital-shelf levers, and every new headwind becomes another step ahead.

  • Bridging the Gap: How Digital Shelf Impact Modeling Empowers Smarter Marketing Investments

    Bridging the Gap: How Digital Shelf Impact Modeling Empowers Smarter Marketing Investments

    Marketing analytics has evolved dramatically over the past decade, yet many brands still struggle to connect their marketing investments to real business outcomes. While traditional analytics platforms provide valuable historical insights, they often miss the critical external factors that drive consumer behavior in today’s fast-moving digital marketplace.

    The challenge isn’t just about measuring what happened. It’s about understanding why it happened and predicting what comes next. This is where Digital Shelf Impact Modeling becomes essential for smarter marketing investments.

    The Critical Data Gap In Marketing Analytics

    Traditional marketing analytics expose brands to considerable risk, especially in the CPG and retail space. The fundamental challenge lies in their reliance on lagging indicators for essential metrics like historical sales and ad spend. Data inputs may be months or quarters old before they’re used for strategic decision-making.

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

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

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

    In fact, opaque data integration and siloed insights remain substantial barriers to actionable intelligence from marketing analytics tools. Most critically, old school approaches often miss vital such variables influencing consumer behavior.

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

    How Digital Shelf Impact Modeling Completes The Picture

    This is where Digital Shelf Impact Modeling plays a complementary role. Brands leveraging digital shelf analytics gain insights into actual market dynamics that traditional analytics alone cannot provide. However, brands using digital shelf insights in isolation often struggle to quantify how digital shelf improvements directly impact revenue. Answering questions like “Did better product content drive sales, or was it the influencer campaign?” remains challenging.

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

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

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

    When merged with established analytics capabilities, digital shelf impact modeling creates a complete picture that fills the blind spots holding marketing teams back from maximizing ROI.

    The Digital Shelf Advantage in Retail Media

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

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

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

    How Digital Shelf Impact Modeling Enhances Retail Media Optimization

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

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

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

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

    How to Integrate Digital Shelf Impact Modeling: A 3-Step Framework

    Digital Shelf Impact Modeling for Marketing Investments

    Here’s how to integrate Digital Shelf Impact Modeling into your marketing strategy to start making better data-driven decisions for your brand.

    Step 1: Map Digital Shelf Variables to Analytics Inputs

    Begin by mapping specific digital shelf variables to your existing analytics inputs. Ensure that competitors are properly configured for monitoring in your digital shelf platform and that timely metrics like price changes and search ranking positions are linked with your marketing measurement systems.

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

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

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

    Step 2: Feed High-Quality Digital Shelf Data into Analytics Platforms

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

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

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

    Step 3: Validate and Iterate

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

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

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

    Executive Decision Support in Uncertain Times

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

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

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

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

    Future-Proofing Your Marketing Strategy with Digital Shelf Impact Modeling

    Several emerging trends highlight the growing importance of digital shelf-enhanced marketing analytics:

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

    DataWeave’s Unique Advantage

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

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

    Conclusion – From Hindsight to Foresight

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

    DataWeave serves as the essential bridge between traditional analytics systems and real-time, comprehensive market intelligence. When digital shelf analytics and marketing measurement work together, brands gain a complete picture: traditional analytics show precisely what happened, while Digital Shelf Impact Modeling explains why it happened. Together, they reveal what’s coming next.

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

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

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

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

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

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

    Understanding the Distinction

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

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

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

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

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

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

    Characteristics of Competitive Intelligence

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

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

    Here are some of the essential elements of competitive intelligence:

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

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

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

    Different Audiences, Different Needs

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

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

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

    Price Reporting for Operational Teams

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

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

    Key functions of standard price reporting include:

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

    Competitive Intelligence for Leadership

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

    Core strategic functions of competitive intelligence include:

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

    From Data to Strategy – Transforming Basic Price Data

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

    Transforming from basic price data to competitive intelligence involves:

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

    Leveraging Technology for Competitive Intelligence

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

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

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

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

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

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

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

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

    Understanding Tariff Impact

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

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

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

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

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

    Preparation Strategies

    Strategies to battle disruption in retail

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

    Cost Monitoring

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

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

    Competition Tracking

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

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

    Consumer Impact Assessment

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

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

    Response Framework

    Tariff response action plan for retailers

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

    Price Adjustment Strategies

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

    Promotion Planning

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

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

    Alternative Sourcing

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

    Forward Buying

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

    Market Intelligence Requirements

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

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

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

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

  • Beyond MAP Pricing: Strategic Approaches for Brands and Retailers

    Beyond MAP Pricing: Strategic Approaches for Brands and Retailers

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

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

    Understanding MAP Fundamentals

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

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

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

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

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

    Brand Perspective

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

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

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

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

    Retailer Strategies

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

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

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

    Digital Implementation for MAP Compliance

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

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

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

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

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

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

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

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

    Make MAP Compliance a Strategic Advantage

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

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

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

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

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

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

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

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

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

    The Price Relationship Challenge

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

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

    Pricing Relationship Challenges Retailers Need to Account For

    Private Label vs. Premium Product Pricing

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

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

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

    Value Size Relationships

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

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

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

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

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

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

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

    Price Link Relationships

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

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

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

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

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

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

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

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

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

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

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

    Implementation Strategy

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

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

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

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

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

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

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

    Maximizing Competitive Match Rates: The Foundation of Effective Price Intelligence

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

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

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

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

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

    The Match Rate Challenge

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

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

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

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

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

    Why Match Rates Matter

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

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

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

    The Business Impact

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

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

    Current Industry Challenges

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

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

    The Matching Hierarchy

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

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

    The AI Advantage

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

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

    Implementation Strategy

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

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

    Future-Proofing Match Rates

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

    Key Takeaways

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

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

  • Beyond Basic Price Monitoring: Advanced Applications of Competitive Intelligence

    Beyond Basic Price Monitoring: Advanced Applications of Competitive Intelligence

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

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

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

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

    Regional Price Intelligence

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

    Understanding geographic and regional pricing strategies

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

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

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

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

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

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

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

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

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

    Leveraging Regional Price Differences for Strategic Advantage

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

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

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

    Local Market Dynamics

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

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

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

    Competitor Strategy Detection

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

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

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

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

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

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

    Data should be able to show you things like:

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

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

    Actionable Intelligence Framework

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

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

    Competitive Intelligence at Your Fingertips

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

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

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

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

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

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

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

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

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

    Poor Data Refinement vs. Good Refinement

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

    Retailer A

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

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

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

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

    Retailer B

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

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

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

    The Hidden Cost of Unrefined Data

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

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

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

    The Two Pillars of Data Refinement

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

    Competitive Matches

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

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

    Internal Portfolio Matches

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

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

    Leveraging AI for Enhanced Match Rates

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

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

    How AI helps convert raw data to pricing and assortment intelligence

    From Refinement to Business Value

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

    Price Management

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

    Price Reporting

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

    Competitive Intelligence

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

    Implementation Framework

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

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

    What’s Next?

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

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

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

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

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

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

    The Data Quality Challenge for Retailers and Brands

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

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

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

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

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

    Improving the Accuracy of Product Matching

    Image Matching for Data Quality

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

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

    How ‘Embeddings’ Enhance Scoring

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

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

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

    Vector Databases for Enhanced Accuracy

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

    Evolution of Embeddings and Scoring: A Multimodal Perspective

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

    DataWeave’s AI framework can:

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

    Quantified Improvements: Model Accuracy and Stats

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

    Business Use Case: Multimodal Matching for Price Leadership

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

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

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

    DataWeave’s AI-Driven Data Quality Framework

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

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

    Scoring Data Quality

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

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

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

    DataWeave's Data Quality Check framework

    Statistical Process Control

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

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

    Transparent Quality Assurance

    The platform provides complete visibility into data quality through:

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

    Human-in-the-Loop Validation (Véracité)

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

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

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

    In Conclusion

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

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

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

  • Black Friday vs Boxing Day: Which Sale Event Offered Better Deals?

    Black Friday vs Boxing Day: Which Sale Event Offered Better Deals?

    When it comes to shopping events, Black Friday stands out as one of the most anticipated dates for scoring deals. Typically occurring the day after Thanksgiving, the weekend kicks off the holiday shopping season with a frenzy of discounts. But Boxing Day, celebrated on December 26, is also well-known for its post-Christmas clearance sales.

    This Black Friday, US eCommerce sales increased by a hefty 14.6% in 2024, according to Mastercard SpendingPulse. While Black Friday leads in overall revenue generation for retailers, Boxing Day presents unique opportunities for clearing post-holiday inventory.

    For a consumer, which sale event is likely to offer the most attractive deals?

    At DataWeave, we analyzed discounts across retailers and categories to uncover the answer.

    Our Methodology

    For this analysis, we tracked pricing data across major retailers for Black Friday and Boxing Day. To provide a comprehensive analysis of Black Friday pricing strategies, we explored a matched products dataset, comparing identical 14,000+ SKUs across retailers within key categories.

    • Categories included: Consumer Electronics, Home & Furniture, Apparel, Health & Beauty, Grocery
    • Retailers included: Amazon, Target, Walmart, Sephora, Ulta Beauty, Overstock, Home Depot, Best Buy, Saks Fifth Ave, Nordstrom, Macy’s, Bloomingdale’s, Neiman Marcus
    • Timeline: November 26 (Black Friday), December 26 (Boxing Day)

    Average Discounts: Black Friday vs Boxing Day

    Our analysis reveals that Black Friday generally offered steeper discounts across most categories, although Boxing Day wasn’t far behind. Here’s a breakdown:

    Boxing Day Vs. Black Friday - Discounts Across Categories

    While Black Friday led in most categories, Apparel saw a slight edge on Boxing Day, with discounts averaging 40.22% compared to 37.67% on Black Friday. Electronics, Beauty, and Home, however, remained more lucrative during Black Friday.

    Top 5 Products Higher Discounts on Black Friday

    Diving deeper into specific products, here are our top 5 picks offering better discounts during Black Friday.

    Top 5 Products With Higher Discounts on Black Friday
    • Appliances like an Immersion blender set offering a discount of 88.29%, significantly higher than its Boxing Day offer of 86.62%. 
    • High-end electronics like the Microsoft Surface Pro 4 also saw substantial markdowns at 84.60%. 
    • In beauty and fashion, both La Roche Posay’s retinol serum and Vera Bradley’s satchel offered discounts above 80%. 
    • Even everyday essentials like paper towels enjoyed generous discounts, with markdowns reaching 82.35% during Black Friday compared to 76.47% on Boxing Day.

    Top 5 Products With Higher Discounts on Boxing Day

    Boxing Day revealed some remarkable deals across diverse categories, with certain products offering significantly better value than their Black Friday counterparts.

    Top 5 Products With Higher Discounts on Boxing Day
    • The JBL Go 2 portable speaker emerged as the standout, with an extraordinary 82% Boxing Day discount compared to just 20% on Black Friday—a dramatic 62% difference.
    • Home furnishings showed strong Boxing Day performance, with the Costway accent armchair set reaching 80.30% off.

    In Conclusion

    Black Friday reigns supreme in driving early holiday sales, offering deeper discounts and drawing larger crowds. However, Boxing Day remains critical for retailers to offload surplus inventory and attract post-holiday shoppers.

    By combining insights from both events, retailers can refine their strategies to maximize revenue and enhance customer satisfaction. For shoppers, the decision comes down to timing—shop early for better deals or wait to capitalize on clearance markdowns. The products and categories with more attractive offers tend to vary between these two sale events. Hence, as a shopper, it’s a good idea to keep track of prices all through the holiday season to take advantage of the best deals.

    Check out our comprehensive coverage of Black Friday 2024 deals and discounts across categories.

    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!