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:
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
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:
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
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
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 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:
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
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:
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
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
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
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
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
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
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
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
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 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:
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
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:
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
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
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
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.
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
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
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
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
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.
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:
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
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:
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
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
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
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%).
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
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
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
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%.
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.
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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.
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.
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.
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.
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.
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.
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.
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.
It uses multimodal AI that combines text, image, and metadata analysis to identify matches even when products are described or displayed differently across retailers.
Domain heuristics apply retail-specific logic, recognizing that “Large” means something different in apparel than in beverages, and that seasonal items require unique treatment.
Knowledge graphs link products across brands, categories, and regions to reveal true relationships even when surface attributes vary.
Through continuous learning, every human correction improves future AI suggestions, making the system smarter and more accurate over time.
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.
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.
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%.
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%.
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.
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%.
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.
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%.
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.
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:
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
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?
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.
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:
Harnessing the data
Pattern recognition: Your solution should help you identify repeat pricing behaviors and competitor strategies
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
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.
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
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
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.
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.
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.
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.
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.
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:
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.
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.
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?
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.
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.
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.
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.
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
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.
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
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:
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.
Black Friday and Cyber Monday are major retail events in Canada, with 43% and 29% of the population making purchases during these sales respectively, according to a YouGov report. Consumer electronics continue to lead the Canadian retail market during these events, with 55% of surveyed shoppers choosing to buy tech products on Black Friday. Household appliances come in second, with 25% of shoppers opting for these items, while 18% prefer to shop for furniture deals.
These statistics highlight the importance of delivering value during the Thanksgiving sales week. Retailers must cater to shoppers’ expectations with competitive pricing, attractive deals, and a seamless shopping experience. So, what unique offerings did Canadian retailers present to shoppers this season?
To understand the pricing and discount dynamics during BFCM 2024 in Canada, DataWeave analyzed discounts across leading consumer electronics and home & furniture retailers. Using our AI-powered pricing intelligence platform, we analyzed 37,108 SKUs across these categories for major retailers including Amazon, Walmart, Best Buy, Home Depot, and Canadian Tire from the 10th to 29th November. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “sofa” and “wearables”.
In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Consumer electronics saw robust participation from major retailers, with Amazon, Best Buy, and Walmart leading the charge. Here’s how they stacked up in terms of discounts:
Best Buy emerged as the frontrunner in absolute discounts at 31.2%, while Amazon impressed with a notable 19.7% additional discount, indicating a strong Black Friday-specific markdown strategy.
Walmart offered steady competition, particularly in audio and video products, which reached an average absolute discount of 37.2%. However, it’s average additional discount was only 3.1%, indicating muted BFCM-specific price reductions in this category.
Subcategory Insights
Diving deeper into consumer electronics subcategories, we observed varied discounting strategies.
Audio & Video stood out as the most discounted subcategory, with Walmart leading at 37.2%.
In Wearables, Walmart again took the top spot with 36.4%, while Amazon offered higher additional discounts (22.4%).
Discounting for computers and gaming was less aggressive, highlighting strategic pricing to maintain profitability in these high-demand segments.
Brand Performance
Brand-level data highlighted how key players used Black Friday to drive visibility and sales.
Dell led in average absolute discounts (36.7%) followed by Samsung at 36.68%
Audio brand JBL offered significant absolute discounts at 35.9%.
Apple and Lenovo offered comparatively fewer discounts but maintained strong visibility, as seen in their increase in the Share of Search during the sale period.
MSI (laptop brand) and Bose (audio and earphone brand) experienced significant increases in visibility, with Share of Search increases of 5% and 3.6%, respectively.
Notably, HP faced a decline (-3.2%) in the Share of Search, suggesting missed opportunities to align promotions with consumer interest.
Home & Furniture
Retailers in Focus
The home and furniture category saw competitive discounting, with Walmart, Canadian Tire, and Home Depot vying for consumer attention.
Walmart took the lead with the highest absolute discounts at 36.8%. The retailer’s additional discounts were more conservative at 3.6%. This is similar to their discount levels in Consumer Electronics.
Canadian Tire offered stiff competition, providing 31.6% absolute discounts and 25% additional discounts.
Home Depot matched its absolute and additional discounts, maintaining consistency at 24.1%.
Subcategory Insights
Home and furniture subcategories revealed targeted discount strategies.
Bedding emerged as the most discounted subcategory at Walmart (50.6%) and Canadian Tire (35.3%).
Kitchenware saw competitive pricing, with Walmart leading at 42.9%, followed by Canadian Tire at 33.9%.
Canadian Tire focused on lighting, offering the highest absolute discounts in this subcategory (38.2%)
Brand Performance
Brand-level analysis revealed stark contrasts in discounting approaches.
Furniture brands Homcom led in absolute discounts (36.4%), while South Shore stood out with the highest additional discounts (30.2%).
Value-oriented brands like furnishings brand Mainstays and mattress and bedding brand Zinus offered more modest discounts, focusing on consistent affordability.
Zinus (mattresses and sofa brand) experienced a significant 7.9% increase in the Share of Search, driven by aggressive promotions.
Home furnishings brands like Costway and Safavieh faced declines, reflecting the importance of aligning promotional strategies with consumer expectations.
Insights for Retailers and Brands
This Black Friday, Canadian retailers effectively balanced deep discounts with category-specific strategies to maximize sales. However, the fluctuating Share of Search highlights the critical need for brands to align promotions with consumer interest.
For brands and retailers looking to stay ahead of the competition, DataWeave’s pricing intelligence platform offers unparalleled insights to refine discounting strategies and boost visibility. Contact us to learn how we can help you stay competitive in this dynamic retail landscape.
Today, the first name that comes to anybody’s mind when they hear about online shopping is Amazon. In the US alone, Amazon accounted for over 37.6 percent of total online retail sales in 2023 with the second place Walmart not even managing to win double-digit numbers on the same scale.
With such a phenomenal market share, it is not surprising that any retail brand would want to have their products listed on Amazon for sale. However, as enticing as the potential exposure could be, the overwhelming presence of brands selling similar products on Amazon is so huge that getting fair visibility for your products may require some heavy-lifting support.
Will the Same SEO You Use for Google Work with Amazon?
Unfortunately, no, as Google and Amazon have different objectives when it comes to search rankings on their respective customer platforms. Google makes the lion’s share of its revenue from search advertising, whereas Amazon makes money when customers buy products listed on its platform by sellers.
Relying on traditional search engine optimization (SEO) techniques may not get the desired results as they are more optimized for search engines like Google. Amazon embraces its unique DNA when it comes to product display rankings on its search option.
How Does SEO Work in Amazon?
Over the years, Amazon amassed data about shopping experiences that billions of customers globally had on its platform. With this data, they developed their custom search algorithm named A9. Contrary to the gazillion objectives that Google has for its intelligent search algorithms, Amazon has tasked A9 with just a simple straightforward target—when a customer keys in a search query, provide the best choice of products that they will most probably purchase, as search results.
A9 works to fulfill the mission of guiding shoppers to the right product without worrying about semantics, context, intent, mind mapping, etc. of the search query in contrast to what Google does. As with Google search, Amazon does have paid advertising and sponsored results options such as Amazon PPC, Headline ads, etc. but their SEO algorithms are aware of how to support and boost search rankings of genuine products and brands that have taken an effort to follow best practices in Amazon SEO as well as have a great offering with attractive prices.
As additional knowledge, Amazon also has clear guidelines on what it prioritizes for search rankings. Known in the SEO world as Amazon ranking signals, these are core factors that influence how a product is ranked for search queries. Some of the top Amazon ranking signals that carry heavy influence on search rankings include on-page signals, off-page signals, sales rank, best sellers rank, etc.
What Brands Need to Strategize to Master the Amazon SEO Algorithms
From a broad perspective, we can classify the actions brands need to take in this regard in 3 core stages:
Pre-Optimization
This deals with getting first-hand knowledge about both customers who are likely to purchase your product and the competitors who are vying for sales from these very same customers. Filtering your target customer or audience is essential to ensure that you get the most ROI from marketing initiatives and that sales cycles are accelerated. For example, if your product is a premium scented candle, there is no point in wasting advertising dollars trying to win attention from customers who are not likely to ever spend on luxury home décor items.
Knowing how your competitors are performing on Amazon search, the keywords, and SEO strategies they have adapted is critical to ensure that you stay one step ahead.
Product Listing Page Optimization
This includes strategies that a brand can adopt so that its product description page gets the much-needed content optimizations to sync with Amazon’s A9 algorithm. It has a mix of keyword-integrated content, relevant images, descriptions in easy-to-understand language, localized content flavors to resonate with target buyers, etc. For example, a kitchen tool like a grater might be used for different kinds of food preparation techniques in different regions of the same country.
The brand must ensure that the description adequately localizes the linguistic or usage preference representation of the target audience. If the grater is used for grating coconut shells to extract the fibrous pulp in the Midlands and for grating ginger skin in the Far East, both use cases should be part of the product description if the target customers are from both regions.
Sales Optimization
This deals with options that have more sales strategies integrated into their core. For example, blogs on popular websites with the Amazon purchase link embedded in the content, collaboration with social media influencers, paid advertising on Amazon itself as well as on search engines, video ads, banner and display ads, etc.
The key intent here is to drive organic and inorganic traffic to the Amazon product listing page and ultimately win sales.
How Can Your Products Rank High in Amazon Search Results? Top 10 Tactics
Now that you have a clear understanding of the strategies that help in mastering Amazon’s ranking algorithms, here are some great tips to help achieve higher search rankings for your products on Amazon search:
1. Target Relevant Keywords
You need to figure out the best keywords that match what customers put as queries into the Amazon search bar. Your brand needs to clearly understand customer behavior when they arrive on Amazon to search for a product or category of products. The best place to begin looking for the same would be on competitor pages on Amazon. The keywords that helped them rank well on Amazon can help you as well. Manually investigating such a large pool of competitors is nearly impossible but with the right tools, you can easily embrace capabilities to know which keywords can help you in mimicking the success of your competitors.
2. Focus on Product Titles
Every single part of the content in your brand’s Amazon storefront or product page needs dedicated focus. Beginning with the product titles, effort needs to be made to ensure that they include the brand name, key product category or features, and other relevant keyword information.
In other words, product titles must be optimized for searchability. This searchability for product titles needs to be optimized for both mobile and desktop screens.
3. Create Product Descriptions that Resonate with the Audience
For product descriptions on your Amazon webpage, you need to figure out the optimal quality levels needed for the intended audience. Effective content can help achieve better search ranking visibility and convince the incoming traffic of shoppers to make a purchase. It is important to periodically review and modify your page content to suit the interests of visitors from both web and mobile devices.
Leveraging solutions like DataWeave can help with regular content audits to ensure you are putting out the best product content that will delight shoppers and deliver on sales conversion targets.
4. Use High-Quality Media Assets like Images and Videos
Promoting your product doesn’t have to be restricted to just textual content in Amazon product description sections. You can use other multimedia assets of high quality. These include images, videos, brochure images, etc. Every content asset must aim to educate shoppers on why your product should be their number one choice. For example, look at this detailed product description for the viral K-Beauty product COSRX Mucin Essence.
Moreover, images can help attract more attention span from visitors, thereby increasing the probability of purchases.
5. Strengthen the Backend Keywords As Well
Amazon also supports hidden backend keywords that sellers add to their product listings. They help add more relevance to products similar to meta descriptions and titles in traditional SEO for search engines like Google. A typical backend keyword may comprise synonyms, misspelled keywords, textual variations, etc. However, knowing how to pick the right ones is crucial. By analyzing your keyword rankings against competitors and higher-ranking product results in search, the platform can help you consistently optimize your content backend to help grow visibility.
6. Focus on Reviews and Ratings
Reviews and ratings on product pages are key insights that help customers with their purchasing decisions. So, it is natural for brands to keep a close eye on how their products are faring in this regard. Reviews and ratings are a direct indication of the trustworthiness of your product. When previous buyers rate you high and leave favorable reviews on your product, it will directly promote trust and help you secure a better rapport with new customers.
This upfront advantage can help boost sales conversions better. Leveraging solutions like DataWeave can help you understand the sentiments that customers have for your products by intelligently analyzing reviews and ratings.
7. Implement Competitive Pricing Strategies
The goal of most customers when shopping online is to get their desired product at the most affordable prices. The eCommerce price wars every year are growing in scale today and getting your product pricing right is crucial for sales. However, there is a need to gain comprehensive insights into how your competitors are pricing their offerings and how the market responds to specific price ranges. Solutions like DataWeave help your brand access specific insights into pricing. By analyzing competitor pricing, you can create a winning price model that is sustainable for your brand and favorable for target customers.
8. Track Share of Search
Content and other SEO activities will help improve your search rankings on Amazon. However, it is equally important to know how well your products are performing periodically against your competitors for the same set of specific keyword searches. You need to understand the share of search that your products are achieving to formulate improvement strategies. DataWeave’s Digital Shelf Analytics solution provides share of search insights helping you uncover deep knowledge on your discoverability on Amazon (and other marketplaces) for your vital search keywords.
9. Ensure Stock Availability
To achieve better ranking results, brands need to ensure that the relevant products matching the search keywords are available for quick delivery at the desired ZIP codes where users are more likely to search and order them. Out-of-stock items seldom show up high on search results. Certain products, especially if they’re popular, can get stocked out frequently in certain locations. Keeping a close eye on your stock availability across the map can help minimize these scenarios.
10. Optimize Your Brand Presence
While optimizing content and other key areas within the Amazon webpage for your product is critical, there are other avenues to help boost search rankings. One such option includes registering in the Amazon Brand Registry, which provides more beneficial features like protection against counterfeits and ensuring that your brand page is optimized according to Amazon storefront standards.
The Bottom Line
Winning the top spot in Amazon search ranking is crucial for brands that aim to capitalize on online sales revenue to grow their business. Knowing your workaround for Amazon’s proprietary SEO frameworks and algorithms is the first step to succeeding. The key element of success is your ability to gain granular insights into the areas we covered in this blog post such as competitor prices, sentiments of customers, market preferences, and content optimization requirements.
This is where DataWeave’s Digital Shelf Analytics solution becomes the biggest asset for your eCommerce business. Contact us to explore how we can empower your business to build the most visible and discoverable Amazon storefront that guarantees higher search rankings and ultimately increased sales. Talk to us for a demo today.
Brands are investing millions of dollars in digital retail media to make their products stand out amid unrelenting competition.
The ad spend on digital retail media worldwide was estimated at USD 114.4 billion in 2022, and the current projections indicate that it will grow to USD 176 billion by 2028. This amounts to a 54% increase in just six years.
The current surge in digital retail media advertising has led brands to find an effective way to monitor the efficacy of their ad spend. While Share of Search has long been used to measure brand visibility effectively, the metrics often missed tracking ads on retail sites.
DataWeave’s Share of Media solution helps solve this problem.
What is the Share of Media?
At DataWeave, Share of Media is a metric used to measure a brand’s presence in sponsored listings and banner ads on eCommerce platforms. It captures how often a brand appears in paid promotions compared to competitors, offering insights into advertising visibility and effectiveness.
These days most marketplaces seamlessly blend banner ads and sponsored listings into organic search results. Let’s take a closer look.
Banner Advertising
Banner advertising strategically places creative banners across websites—often at the top, bottom, or sides. Some eCommerce platforms also integrate these banners into product search listings.
What makes banner ads so special is the unique ability to allow marketers to use various types of media in a single ad, such as images, auto-play videos, and animations. Brands can also present curated collections of products. This flexibility provides marketers with creative opportunities to differentiate from competitors, capture customer interest, and encourage conversions.
Sponsored Listings
Sponsored listings are paid placements within search engine results or eCommerce platforms. They are usually marked as ‘sponsored’ or ‘ad,’ and they often appear at the top of search results and alongside organic product listing results.
Unlike organic search results, sponsored listings are prioritized based on the advertiser’s bid amount and relevance to users’ search queries.
Sponsored listings offer a strategic advantage by enabling businesses to connect directly with consumers who are actively searching for their products. This targeted approach ensures that marketing efforts are focused on individuals with high intent of making a purchase, maximizing the potential return on investment.
The Power of Banner Ads and Sponsored Listings
Banner ads and sponsored listings are great choices for boosting customer engagement and product sales. Here are four key advantages they offer:
Enhanced Visibility: Digital retail media strategically places your brand where it will stand out—outshining competitors and grabbing the attention of high-purchase-intent consumers.
Precision in Reach: These ads target specific keywords or categories, allowing for highly focused advertising based on demographics and search intent.
Minimal Conversion Friction: Smooth transitions from ads to a brand’s native store or product listing on the marketplace keep conversion friction to a minimum.
Brand Awareness and Recall: Consistent exposure to your brand through banner ads and sponsored product listings can leave lasting impressions and build brand recognition.
The bottom line is that it’s increasingly important for brands to monitor their Share of Media.
How to Monitor Your Brand’s Share of Media
DataWeave’s Digital Shelf Analytics (DSA) platform extends beyond the traditional Share of Search metrics and provides robust support for monitoring the Share of Media.
DataWeave monitors the Share of Media in two ways: keywords and product categories. Users can view Share of Media insights through aggregated views, trend charts, and detailed tables. The views are designed to show brand visibility and the overall competitive landscape. For example, the screenshot below, taken from DataWeave’s dashboard, showcases the Share of Media across keywords, categories, and retailers.
Share of Media by Keyword
The Share of Media metric captures a brand’s advertising presence within search listings for a designated keyword. This provides a comprehensive view of a brand’s visibility and promotional efforts across retail platforms, helping brands validate and gauge the effectiveness of their ad spend.
For example, the screenshot below shows the trend of manufacturer’s Share of Media by keyword—‘baby food.’
Share of Media by Category
The Share of Media metric measures the presence of brands’ banner ads and sponsored listings across product categories on retail sites. This helps brands see which product categories require more investment, making it easier for them to spend their ad budget wisely.
The screenshot below illustrates manufacturers’ Share of Media by category across retailers.
Share of Media: An Essential Ecommerce Metric
As retail media continues to evolve, our analytics must follow—after all, knowledge is a competitive advantage. In the dynamic world of eCommerce, where competition is fierce and consumer attention is scarce, understanding your share of media is crucial.
Analyzing the Share of Media can give brands a competitive edge. By regularly monitoring and analyzing this metric, you can make data-driven decisions to improve your brand’s visibility, attract more customers, and ultimately drive sales growth. With a deeper understanding of their target audience and market dynamics, brands can refine promotional efforts to drive more effective results and optimize return on ad spend (ROAS).
For more information on how Digital Shelf Analytics can enhance your brand’s digital shelf presence, request a demo or contact us at contact@dataweave.com.
As summer winds down, families across the US have been gearing up for the annual back-to-school shopping season. The back-to-school season has always been a significant event in the retail calendar, but its importance has grown in recent years. With inflation still impacting many households, parents and guardians are more discerning than ever about their purchases, seeking the best value for their money.
The National Retail Federation has forecasted that this season could see one of the highest levels of spending in recent years, reaching up to $86.6 billion. As shoppers eagerly stock up on back-to-school and back-to-college essentials, it’s crucial for retailers and brands to refine their pricing strategies in order to capture a larger share of the market.
To understand how retailers are responding to the back-to-school rush this season, our proprietary analysis delves into pricing trends, discount strategies, and brand visibility across major US retailers, including Amazon, Walmart, Kroger, and Target. By examining 1000 exactly matching products in popular back-to-school categories, our analysis provides valuable insights into the pricing strategies adopted by leading retailers and brands this year.
Price Changes: A Tale of Moderation
The most notable trend in our analysis is the much smaller annual price increases this year, in contrast to last year’s sharp price hikes. This shift is a reaction to growing consumer frustration about rising prices. After enduring persistent inflation and steep price growth, which peaked last year, consumers have become increasingly frustrated. As a result, retailers have had to scale back and implement more moderate price increases this year.
Kroger led the pack with the highest price increases, showing a 5.3% increase this year, which follows a staggering 19.9% rise last year. Walmart’s dramatic price increase of 14.9% is now followed by a muted 3.1% hike. Amazon and Target demonstrated a similar pattern of slowing price hikes, with increases of 2.3% and 2.7% respectively in the latest period. This trend indicates that retailers are still adjusting to increased costs but are also mindful of maintaining customer loyalty in a competitive market.
When examining specific product categories, we observe diverse pricing trends. Electronics and apparel saw the largest price increases between 2022 and 2023, likely due to supply chain disruptions and volatile demand. However, the pace of these increases slowed in 2024, indicating a gradual return to more stable market conditions. Notably, backpacks remain an outlier, with prices continuing to rise sharply by 22%.
Interestingly, some categories, such as office organization and planners, experienced a price decline in 2024. This could signal an oversupply or shifting consumer preferences, presenting potential opportunities for both retailers and shoppers.
Brand Visibility: The Search for Prominence
In the digital age, a brand’s visibility in online searches can significantly impact its success during the back-to-school season. Our analysis of the share of search across major retailers provides valuable insights into brand prominence and marketing effectiveness.
Sharpie and Crayola emerged as the strongest performers overall, with particularly high visibility on Target. This suggests strong consumer recognition and demand for these traditional school supply brands. BIC showed strength on Amazon and Target but lagged on Kroger, while Pilot maintained a more balanced presence across most retailers.
The variation in brand visibility across retailers also hints at potential partnerships or targeted marketing strategies. For instance, Sharpie’s notably high visibility on Target (5.16% share of search) could indicate a specific partnership.
Talk to us to get more insights on the most prominent brands broken down by specific product categories.
Navigating the 2024 Back-to-School Landscape
As we look ahead to the 2024 back-to-school shopping season, several key takeaways emerge for retailers and brands:
Price sensitivity remains high, but the rate of increase is moderating. Retailers should carefully balance the need to cover costs with maintaining competitive pricing.
Strategic discounting can be a powerful tool, especially for lesser-known brands looking to gain market share. However, established brands would need to rely more on quality, visibility, and brand loyalty.
Online visibility is crucial. Brands should invest in strong SEO and retail media strategies, tailored to different retail platforms.
Category-specific strategies are essential. What works for backpacks may not work for writing instruments, so a nuanced approach is key.
Retailers and brands should be prepared for potential shifts in consumer behavior, such as increased demand for value-priced items or changes in category preferences.
By staying attuned to these trends and remaining flexible in their strategies, businesses can position themselves for success in the competitive back-to-school retail landscape of 2024. As always, the key lies in understanding and responding to consumer needs while maintaining a keen eye on market dynamics.
Stay tuned to our blog to know more about how retailers can stay aware of changing pricing trends. Reach out to us today to learn more.
Our world is increasingly going online. We work online, socialize online, and shop online every day. As a consumer brand, you need to ensure complete awareness of your brand’s online presence across eCommerce platforms, search engines, and media.
Only by deeply understanding the customer journey can you ensure that your product is reaching your ideal customers and maximizing your brand’s market share. You need data to intrinsically understand your customer journey and make changes where you’re lacking.
As the old adage goes: ‘You can’t manage what you don’t measure.’
You need digital shelf metrics to measure and start benchmarking your buyer’s journey. To find several of these types of key performance indicators (KPIs), you need a digital shelf analytics solution. These platforms allow you to track various metrics along the path to purchase from the awareness stage to the post-purchase phase across the entire internet, helping to inform online and offline sales strategies.
Digital shelf analytics will help you gain insights into how your brand is doing versus the competition, which areas are lagging behind in historical performance, and what activities are driving sales. There are innumerable ways in which you can leverage these valuable insights. But how do you know which KPIs to start tracking with your digital shelf analytics solution?
Here, we’ve summarized the top metric types your peers report, track and base their decisions on.
With these KPIs in hand, consumer brands like yours can ensure that their products are consistently visible and appealing to their target audience across online marketplaces, ultimately enhancing conversion rates, market share, and profitability.
Read this guide to learn more about the top digital shelf metrics consumer brands are tracking and how to use them in your own strategy.
1. Share of Search
Share of Search (SoS) is a KPI in digital shelf analytics that measures how frequently a consumer brand’s products appear in search results on eCommerce platforms relative to the competition for specific keywords. A good digital shelf analytics solution will be able to show this metric across all the top marketplaces and retailers, such as Amazon and Walmart, but also more niche marketplaces for industry-specific selling.
This metric provides brands with a quantifiable way to measure how frequently their products are being “served up” to customers on online marketplaces. Essentially, it measures visibility and discoverability.
With Share of Search on DataWeave, you can slice and dice your data in innumerable ways. These are a few important views you can see:
Aggregated SoS
Organic and Sponsored SoS scores
SoS scores across brands, retailers, keywords, cities
Historical SoS score trends
Once you have benchmarked your SoS and category presence relative to your competition, you need to start interpreting the data. Here are some questions you can ask yourself to help interpret your findings:
Which of my key categories have the lowest SoS score?
Which products feature low on search results because they are out of stock?
Are my competitors’ products faring better due to sponsored searches?
Is my SoS low due to poor content quality?
With insights in hand, you will know which actions to take to drive the biggest impact. For example, you could increase sponsored search results or improve organic reach by optimizing product pages.
Understanding your SoS is essential to maximizing the awareness phase of your customer journey. It will help you improve your brand visibility and increase product conversions through better search and category presence.
2. Share of Media
Share of Media (SoM) is a KPI that is just as impactful, if not more so, than the SoS metric. However, only a limited number of brands track it or use it to drive strategic action. This makes it a perfect opportunity for brands looking to get an edge on the competition.
But what is SoM in digital shelf analytics? Essentially, it’s a way of measuring retail media advertising activities like brand-sponsored banners, listings, videos, ads, and promotions that sometimes blend into search results. The main types of retail media advertising exist in two categories: banner advertising and sponsored listings.
Banner advertising involves strategically placing designed banners within websites and search listings. These banners raise brand awareness and drive traffic to online storefronts.
Sponsored listings are paid placements within search results on search engines or eCommerce platforms. They are prioritized based on the total bid amount and the product’s relevance. These paid listings are marked with “sponsored” or “ad.”
These listings serve to enhance your brand’s overall visibility, help you gain more precise reach, increase conversions, and drive better brand awareness and recall with your customers.
These efforts aren’t free, however, so measuring their effectiveness is critical not only to gain all the listed benefits but to also not waste your valuable marketing budget. The SoM KPI can help a consumer brand answer questions like:
Where are the opportunities to increase paid ads?
Which categories could benefit from a promotional boost or a strategic and streamlined allocation of ad spend?
Which of my competitors have active banners and what is their share of media by keyword?
How has my ad spend trended historically in comparison to my competitor?
DataWeave’s digital shelf analytics (DSA) is among the first providers to offer Share of Media KPI tracking and analysis. This is because it requires advanced, multi-modal AI to gather, view, and aggregate listings that encompass text, images, and video. With Share of Media tracking facilitated by DataWeave, consumer brands can track and analyze the effectiveness of their own promotional investments as well as those of their competitors.
3. Content Quality
The content quality metric measures how well your product content adheres to the retailer’s specific guidelines, which are in place to steer traffic and sales on their sites.
With the help of a DSA platform’s AI and ML capabilities, you can measure different elements of your product detail pages (PDPs), such as titles, descriptions, images, videos, and even customer reviews. You need to know which elements are missing, where they are missing, and which ones are negatively affecting sales so you can take corrective action.
Did you know that the average cart abandonment rate is 69.99%? The quality of your content can significantly impact this number. Ensuring that your content is high-quality will help influence product discoverability, customer engagement, and conversion rates. It will also help position you ahead of the competition. If your content quality is poor, you may find yourself with lower search rankings, a higher return rate, and more abandoned carts.
Here are some questions you can answer with the help of the content quality digital shelf metric:
Is my product content at a retail site exactly what was syndicated?
Are there any retailer initiated changes to my product content?
Are my product content updates reflected on the retailer platforms?
How well does my product content comply with the retailer guidelines?
How do I optimize my product content for enhanced discoverability and conversion?
DataWeave’s content quality digital shelf analysis helps consumer brands ensure that product content on eCommerce platforms is high-quality and benchmark their product listings against the competition. It does this through a combination of AI-driven quality analysis and by presenting brands with actionable recommendations. These optimized suggestions are based on the top-performing products so you can focus your valuable time on the areas that will drive the biggest impact.
4. Pricing & Promotions
Your customers can easily shop around to find the best price for the product you’re selling. If your competitor is selling it cheaper, you’ll lose that sale.
That’s why it’s essential to understand the pricing and promotional landscape for each of your products and categories. This can be a challenge, especially if it’s a common product or comes in multiple pack sizes or variants.
It’s equally important to track pricing and promotions even at individual, physical stores. Doing so will allow you to remain competitive and responsive to local market dynamics by tailoring your pricing strategies based on regional competition. You don’t want your products to be overpriced (lost sales) or underpriced (lost profit) in specific markets.
Harmonizing insights when operating an omnichannel consumer brand is extremely difficult without the aid of a digital shelf analytics solution. Insights need to be aggregated between desktop sites, mobile sites, and mobile applications, as well as from physical storefronts.
Questions you can answer with the help of the pricing & promotions digital shelf metric include:
How do my product prices and promotions compare to my competitors?
How consistent is my product pricing across retail websites?
How does my product pricing vary across regions, ZIPs, and stores?
How do price changes influence my sales numbers?
Are there regional differences in pricing and promotion effectiveness?
DataWeave’s digital shelf analytics platform stands out with its sophisticated location-aware capabilities, which enable the aggregation and analysis of localized pricing and promotions. The platform defines locations based on a range of identifiers, such as latitudes and longitudes, regions, states, ZIP codes, or specific store numbers.
The platform can also extract promotional information, such as credit card-based or volume-based promotions. You can see variances across retailers, split by price groups, brands, and competitors. DataWeave specializes in enabling brands to conduct in-depth analyses across a wide array of attributes so you can answer just about any pricing or promotional question you have.
5. Availability
The availability KPI in digital shelf analytics measures the in-stock and availability rates for a brand’s products across eCommerce and physical locations. Similar to the pricing and promotions metric, it relies heavily on location awareness, down to individual stores. Measuring both online availability and offline in-stock rates will help you understand the big picture and take more informed replenishment action.
When you start leveraging the availability KPI with the help of digital shelf analytics, you can improve inventory management, boost product discoverability, increase the frequency with which your online product listings convert, and generally drive more sales. This KPI is essential for ensuring your customers can always find and buy the products they want.
With the availability KPI, you can start answering questions like:
What is my overall in-stock rate?
Which of my products frequently go out of stock?
How does product availability vary across different regions and stores?
What is the impact of availability on my conversion rates?
Are there any seasonal trends in product availability that I need to address?
How quickly are we resolving stockout issues across different locations?
What are my biggest opportunities to reduce stockouts?
DataWeave enables consumer brands to track their product availability metric through automated data collection from various eCommerce platforms in conjunction with physical in-stock rates. The platform provides granular, store-level insights so you can understand regional stock variations and optimize inventory distribution. By tracking historical availability data, you can identify seasonal patterns and predict future demand to pre-empt stockout issues. All of this can be configured with automatic notifications to alert you when there has been a stockout event or when a low stock threshold has been passed, facilitating timely replenishment.
6. Ratings & Reviews
The final KPI in our guide is the ratings & reviews digital shelf metric. Consumers rely heavily on genuine feedback from their peers and refer to star ratings, posted comments, and uploaded pictures to inform their buying decisions. This KPI analyzes the impact of customer feedback and reviews on your products’ performance across eCommerce platforms so you can measure overall brand perception and isolate areas of opportunity.
This metric does something other digital shelf metrics don’t; it can inform your product strategy. It can help you identify repeat complaints that your product team can address with the manufacturer or use for the design of future products.
Some questions you can answer with this powerful KPI include:
What is the overall customer sentiment towards my products based on ratings and reviews?
Which product features are frequently mentioned positively or negatively by customers?
How do my product ratings and reviews compare to those of my competitors?
Are there common issues or complaints that need to be addressed to improve customer satisfaction?
Which products have the highest and lowest ratings, and why?
With DataWeave’s digital ratings and reviews feature, you can keep a pulse on customer sentiment to take short-term action as well as decide long-term strategy. You can leverage reviews to influence product perception, refine products, and enhance overall customer satisfaction.
DataWeave’s Digital Shelf Metrics
Each one of these metrics is interconnected and collectively influences a brand’s success. For instance, improving content quality and earning higher ratings can significantly enhance your product’s visibility in search results, thereby boosting the Share of Search digital shelf metric. By focusing on a comprehensive approach that integrates these metrics, brands can ensure their products are consistently visible, competitively priced, well-reviewed, and readily available.
DataWeave gives consumer brands the means to execute a holistic digital shelf strategy. From a single portal, track and improve digital shelf metrics like Share of Search, Share of Media, Pricing and promotions, Availability, and Ratings and Reviews.
Our solutions help audit and optimize the most critical KPIs that drive sales and market share for brands so you can stay competitive in a dynamic digital landscape and foster long-term customer satisfaction.
As consumer goods brands increasingly turn to eCommerce marketplaces as a source of profitable growth, it becomes harder for teams to grapple with the complexity of revenue growth management.
This complexity emerges from multiple fonts: there are hundreds, and even thousands, of competitors to consider when formulating strategies for managing pricing, promotion, and assortment changes. The world is currently experiencing a period of unprecedented supply chain instability, shifting more consumers away from traditional retail and into eCommerce shopping. And finally, consumer buying patterns, preferences, and trends are constantly shifting.
Revenue growth management (RGM) and net revenue management (NRM) were once less complex processes; but that is no longer the case. Now, some 80% of consumer brand CEOs report that they “aren’t satisfied with their RGM results.”
Gathering data, analyzing it, and acting on it quickly stand out as major challenges that businesses must overcome to grow their market share, earn more profits, and capitalize on market shifts in real time. In this article, we’ll dive into RGM and NRM, the obstacles business teams face, and explore how using technology for digital shelf analytics can help bridge the gap.
What is Net Revenue Management (NRM) or Revenue Growth Management (RGM)?
Every consumer goods company aims to increase profits and grow market share. This requires a concerted effort in RGM and net revenue management (NRM) strategy. Whether a company has a specific team dedicated to this task or relies on the abilities of business analysts or merchandisers, this function is crucial.
It’s worth mentioning that though the terms NRM and RGM are often used interchangeably, there are subtle differences. While both net revenue management and revenue growth management focus on maximizing overall revenue for the brand, NRM typically has a narrower focus and is specific to optimizing profitability through product pricing, promotion, product mix, and cost management. RGM strategies are a bit broader and tend to look at the top line to grow market share and expand the customer base.
The Challenges Revenue Teams Face
Differentiating between ‘good growth’ and ‘bad growth’ is central to NRM and RGM. Net revenue management and revenue growth management teams need the data and tools in place to determine if growth in one area is coming at the expense of another so as not to cannibalize business. Tracking and analyzing extensive data to successfully take action on opportunities and determine whether strategies are working as intended consumes a tremendous amount of mental bandwidth. The fact that these decisions are incredibly time-sensitive only compounds the issue.
To cope, many teams in charge of NRM or RGM employ digital shelf analytics strategies to help speed up data aggregation and analysis to make sure they’re capitalizing on potential opportunities.
eCommerce has added a whole new layer of complexity to consumer goods sales. Instead of a few relatively stable prices at big-box stores, a single item for sale may experience high price volatility, with dozens of minute pricing changes occurring online each day. In some cases, consumers become blind to price volatility, letting brands increase prices, but consumer sentiment, the overall price elasticity of the product, and dozens of other factors go into determining the final price of an online product. Net revenue teams need to modernize and adapt to changing eCommerce environments to competitively price, promote, and grow their revenue.
Here are the top three challenges standing in the way of net revenue management and revenue growth management teams and solutions to address these issues.
Challenge 1: Incomplete or Inaccurate Data
Incomplete and inaccurate data are critical for Net Revenue Management and Revenue Growth Management teams to get under control when attempting to modernize in a digital-centric selling environment. As more competitors enter the market, many brands find it hard to make strategic decisions without the complete picture.
Data may be incomplete or inaccurate because a brand is analyzing only part of the market, such as Amazon or another enterprise-scale eCommerce marketplace. Additionally, they might not be analyzing all types of online media, such as branded ads, sponsored search listings, or sponsored category listings.
Most importantly, another pitfall is the lack of hyperlocal data. Generalized data across regions, states, ZIPs, and stores can skew the decision-making process and result in poor outcomes.
Overcoming Incomplete or Inaccurate Data
In order to get the full picture, consumer brands need to ensure they have a view of the entire competitive landscape across their channels. This includes gathering data down to the case pack, the unique product identifier, and the geography, including ZIP and store. They also need the respective MSRP by SKU, the unit normalized price, and the selling price at a specific moment in time. This is done by aggregating brick-and-mortar store information available online, such as when stores list curbside pickup SKUs and pricing online.
Individual teams cannot manually gather all this detailed data. The growth in eCommerce means there is simply too much data to find and aggregate. Instead, they can employ digital shelf technology to get more data from more sites. Teams can leverage AI to better match product listings, ads, and even visuals to avoid missing data on listings that lack common attributes, such as UPCs for normalization.
To add to this, advanced pricing intelligence systems can cache URLs to help teams audit and verify their data, avoiding delays and confusion when ad hoc requests arise.
Challenge 2: Difficulty in Making Sense of the Competitive Landscape
Once net revenue management and revenue growth management teams have gathered all of the available data, it’s time to make sense of it. This is a monumental challenge, and ends up being the stage where most NRM and RGM teams flounder. Disparate marketplaces include different product attributes and images. This makes it extremely complicated to sync competitors’ data to ready it for analysis, especially if this analysis is carried out manually in Excel. These are some of the attributes that teams need to harmonize in order to make sense of the competitive landscape:
Product identifiers (UPC, SKU, Internal Code)
Size, case, pack, volume, bundled offerings
Language
Currency
Stock Status (Whether the product is available or not)
Platform-specific attributes such as ‘Amazon’s Choice,’ ‘Best Seller,’ etc.
Teams also need to group and classify various categories of promotions. These can include sponsored listings, banner ads, coupons, bank offers, and others. Each of these categories needs to be tracked separately. This vast array of data points across hundreds of sites creates a big data problem for teams.
Making Sense of the Competitive Landscape
The best way to overcome this challenge is to task a digital shelf analytics system with gathering and harmonizing data automatically across the consumer goods competitive landscape. Competitive and market intelligence tools can help break down an overwhelming amount of data, matching similar products across competing brands and analyzing their various strengths and weaknesses. Once the technology matches complex product attributes and identifiers, it becomes easier for teams to gain insights and exploit findings. In a sense, the data needs to be cleaned before analysis can occur.
Technology can gather data in multiple ways, and the best systems employ several methods to get the best matches. Data consumption modes include API integrations, CSV and Excel file uploads, and proprietary scrapers that view websites independently of direct inputs. Having all the data in a single place helps net revenue management and revenue growth management teams gain indicative insights on product popularity, pricing, and sales, on their own and competitor products.
Challenge 3: Lack of Timely Visibility
The final challenge that many net revenue management and revenue growth management teams face is something of a ‘silent killer’ — timeliness. Even if they successfully gather data across the entire competitive landscape and harmonize that data into a format for easy analysis, a lack of timeliness can render even the best actions irrelevant.
Speed is of the utmost importance when there are market changes. If a product goes viral and competitors raise prices in response to increased demand, without timely visibility, the trend may be over before a consumer goods brand can successfully increase its prices for the duration of the trend. This can mean lost margins.
Another example is analyzing data and incorporating lagging promotional and sales data into analyses. This can skew pricing strategies because timely data is not accessible to inform decision-making. Many teams waste time firefighting due to a lack of timely pricing and promotional intelligence data.
Get Near Real-Time Insights for Faster Decision Making
Using technology that allows for net revenue management and revenue growth management teams at consumer goods brands to establish update frequencies can be a game changer. Teams can set update frequencies based on their need. They can set up the system to check a fast-moving product daily, while a slow-moving item might only need to be checked weekly, monthly, or even quarterly. This allows teams to focus on the highest-impact products first and address the largest exceptions before they lose out on an opportunity. Managing exceptions with a digital shelf analytics platform saves teams significant time instead of poring over low-impact changes in the data.
Digital Shelf Analytics for Net Revenue Management
Modernizing a consumer goods brand’s net revenue management or revenue growth management processes requires advanced digital shelf analytics. DataWeave provides consumer goods companies with the technology they need for quick and accurate pricing, promotional, and assortment intelligence. By tracking over 200 million products each day, users can be sure they get the widest and most timely view of the competitive landscape. DataWeave’s deep industry knowledge is baked into every aspect of its platform.
Picture this: You wake up one morning to discover that your top competitor reduced their prices overnight. And now your shopper traffic has tanked and your sales have taken a hit.
Unfortunately, this is a common scenario because your customers can compare prices online in seconds—and loyalty lies with the budget.
So, how can you protect your business? Price monitoring.
Price monitoring solutions can help you keep abreast of competitor price changes—which, of course, will help you improve your pricing strategies, retain your customers, and maximize your profits.
How? In this article, we’ll explore:
What is price monitoring
The key benefits of price monitoring for retailers and brands
What a capable price monitoring solution can do
What Is Price Monitoring?
Price monitoring is the process of tracking and analyzing your competitor’s prices across various online and offline platforms. By monitoring competitors’ prices, you can understand market price trends and adjust your prices strategically—which, in turn, helps you remain competitive, increase margins, and improve customer retention.
5 Benefits of Price Monitoring
Competitor price monitoring can help you:
Gain a competitive edge: Competitor price tracking allows you to adjust your prices to remain attractive to consumers.
Maximize revenue: With timely pricing data, you’re empowered to identify optimum price points that strike a delicate balance between maximizing revenue and maintaining customer loyalty.
Retain customers: Consumers are looking for the most value for their dollar, so maintaining consistently competitive pricing is crucial for retaining loyal customers.
Understand promotional effectiveness: Price monitoring helps businesses evaluate the effectiveness of their promotions and discounts. By comparing the impact of different pricing strategies, businesses can refine their promotional tactics to maximize sales and customer engagement.
Understand market movements: By analyzing historical pricing data, you’re better positioned to anticipate future pricing changes — and adjust your strategies accordingly.
4 Essential Capabilities of Price Monitoring Software
Here are four capabilities to look for when choosing a price monitoring system.
1. AI-Driven Product Matching
Product matching is the process of identifying identical or similar products across different platforms to ensure accurate price comparisons.
If your price monitoring solution can’t reliably match your products with competitors’ across various sales channels at scale, you’ll end up with poor data. Inaccurate data will then lead you to make misinformed pricing decisions.
Product matching needs to be accurate and comprehensive, covering a wide range of products and product variations—even for including private label products.
For example, AI-driven product matching can recognize a specific brand and model of sneakers across multiple online stores—even if product descriptions and images differ. Here’s how it works in a nutshell:
Sophisticated algorithms and deep learning architecture enable AI to identify and match products that aren’t identical but share key characteristics and features.
Using unified systems for text and image recognition, the AI matches similar SKUs across hundreds of eCommerce stores and millions of products. The AI zeroes in on critical product elements in images, like a t-shirt’s shape, sleeve length, and color.
The AI also extracts unique signatures from photos for rapid, efficient identification and grouping across billions of indexed items.
DataWeave’s AI algorithm can initially match products with 80–90% accuracy. Then, humans can bring contextual judgement and make nuanced decisions that the AI might miss to correct errors quickly and push for accuracy closer to 100%. By integrating AI automation with human validation, you can achieve accurate and reliable product-matching coverage at scale.
2. Accurate and Comprehensive Data Collection and Aggregation
The insights you derive are only as good as the data you collect. However, capturing comprehensive pricing data is tough when your competitors operate on multiple platforms.
For truly effective price monitoring insights, you need consistent, comprehensive, and highly accurate data. This means your chosen price monitoring system should:
Scrape data from various sources, such as desktop and mobile sites and mobile applications.
Pull data from various online platforms like aggregators, omnichannel retailers, delivery intermediaries, online marketplaces, and more.
Handle data from different regions and languages.
Collect data at regular intervals to ensure timeliness.
DataWeave’s online price monitoring software covers all of these bases and more with a fast, automated data source configuration system. It also allows you to painlessly add new data sources to scrape.
Instead of incomplete or inaccurate data, you’ll have comprehensive and up-to-date data, allowing you to respond quickly to market changes with confidence.
3. Seamless Normalization of Product Measurement Units
You can’t compare apples to oranges—or price-per-kilogram to price-per-pound.
For price monitoring to be accurate, there must be a way to normalize measurement units—so that we’re always comparing price-per-gram to price-per-gram. If we compare prices without taking into account measurement units, our data will be misleading at best.
Let’s take a closer look. Say that your top competitor sells 12oz cans of beans for $3, and you sell 15oz cans for $3.20. At first glance, your larger cans of beans will appear more expensive—but that’s not true. If we normalize the measurement unit—in this example, an oz—the larger can of beans offers more value to customers.
Unit of measure normalization facilitates sound price adjustments based on accurate and reliable data. For this reason, every business needs a price tracking tool that can guarantee accurate comparisons by normalizing unit measurements—including weight, volume, and quantity.
4. Actionable Data and an Intuitive User Experience
Knowledge is only powerful when applied—and price monitoring insights are only useful when they’re accessible and actionable.
For this reason, the best price monitoring software doesn’t just provide insights based on accurate and comprehensive data, but it also provides several ways to understand and deploy those insights.
Ideal price monitoring solutions provide customized pricing alerts, intuitive dashboards, detailed reports, and visuals that are easy to interpret—all tailored to each particular team or a team member’s needs. These features should make it easy for team members to compare prices against those of competitors in specific categories and product groupings.
Your price tracking tool should also permit flexible API integrations and offer straightforward data export options. This way, you can integrate competitive pricing data with your pricing software, Business Intelligence (BI) tools, or Enterprise Resource Planning (ERP) system.
4 Ways Retailers Can Leverage Price Monitoring
Retailers can use price monitoring tools to remain competitive without compromising profitability—here’s how:
1. Track Competitors’ Prices
Competitor price monitoring helps you avoid being undercut—and, as a result, maintain market share. By tracking competitor prices in real-time, you can adjust prices to remain competitive, especially in dynamic markets. Ideally, you should monitor both direct competitors selling the same products and indirect competitors selling similar or alternative products. This way, you’ll have a complete picture of market prices and can make more informed pricing adjustments.
2. Understand Historical and Seasonal Price Trends
As a retailer, you may want to analyze historical data to identify price patterns and predict future price movements—especially in relation to holidays and seasonal products. Knowing what’s coming, you’re better positioned to plan for pricing changes and promotional campaigns.
3. Implement Dynamic Pricing
Dynamic pricing is the process of adjusting prices based on real-time market conditions, product demand, and competitors’ prices—allowing you to respond faster to market changes to maintain optimized prices.
4. Optimize Promotional Strategies
Price monitoring tools can track retail promotions across numerous online and offline sales avenues, providing insight into the nature and timing of competitors’ promotions. This data can help you determine which promotions are most effective—and which aren’t—allowing you to improve your own promotions and discounts, and allocate marketing resources where it matters most. This is especially beneficial during peak sales periods.
3 Ways Brands Can Employ Price Monitoring
Here are three ways brands can use price monitoring to remain profitable, protect brand equity, and gain a competitive edge.
1. Maintain Consistent Retail Prices
Minimum advertised price (MAP) policies are designed to prevent retailers from devaluing a brand while ensuring fair competition among retailers. Price monitoring applications allow your brand to track retailers’ prices to detect MAP policy violations. Data in hand, you can maintain consistent pricing across online sales channels, physical stores, and retail stores’ digital shelves — and, critically, protect your brand equity.
2. Improve Product and Brand Positioning
When you understand how your products’ prices compare to those of competitors, you can set prices to improve brand positioning. For example, if you want to position your brand as luxurious and high-quality, you need to set higher product prices than budget-friendly alternative products.
3. Ensure Product Availability
You can use a price monitoring solution to track product availability to ensure products are always in stock, even across different physical stores and online marketplaces. If a product is frequently sold out, you can adjust production levels or help retailers to improve their inventory management.
Key Takeaways: E-commerce Price Monitoring
Price monitoring software allows you to compare your products’ prices with competitors. This valuable data can help you:
Optimize revenue through timely price changes and dynamic pricing Avoid being undercut by competitors
Improve pricing strategies and promotions to increase sales and retain customers
As the dust settles on Amazon’s 8th Prime Day extravaganza in India, held on July 20-21, 2024, the eCommerce giant has once again shattered records. This year’s event saw unprecedented engagement, with 24% more Prime members participating compared to 2023, marking the highest-ever Prime member involvement in the sale’s history.
At DataWeave, we’ve conducted an extensive analysis of this landmark event, examining over 47,000 SKUs across major categories for Flipkart and Amazon to uncover compelling insights into pricing strategies, competitive positioning, and emerging trends in the Indian eCommerce space. This follows our comprehensive analysis of Prime Day in the USA across categories and brands, which readers can explore for a global perspective on Amazon’s strategies.
Key Highlights:
Tier-2 Cities Drive Growth: This year’s exponential growth was notably fueled by orders from tier-2 cities, highlighting the expanding reach of eCommerce in India.
Swift Deliveries: Most Prime members’ orders from metros were delivered the same day or the next, while tier-2 cities saw deliveries in less than 2 days, highlighting the importance of delivery time when it comes to purchase decisions.
Brand Participation: Over 450 top Indian and global brands participated, including Intel, Samsung, OnePlus, and Puma, alongside 3,200 new product launches from small and medium Indian businesses. This highlights Amazon’s policy to promote small, local businesses globally in 2024.
Premium Products in Demand: Despite modest discounts across electronics and similar offerings, consumers chose to purchase premium products during Prime Day. With financial flexibility and no-cost EMI options, Indian consumers, especially Gen Z and millennials, are now ready to invest in premium products and experiences.
Our analysis focused on both the Additional Discounts offered during Prime Day (calculated relative to prices the week prior to the event), highlighting the event’s genuine benefits, and the Absolute Discounts offered during Prime Day, representing the total markdown relative to the MRP.
Cross-Category Discount Strategy
Our analysis this year reveals that the Consumer Electronics category saw the highest Absolute Discounts with an average of 48.9%, closely followed by Apparel at 46.9%. The Health & Beauty category had the lowest Absolute Discount at 32.1%.
However, Amazon offered the highest Additional Discount in Health & Beauty at 9.3%. Electronics and Apparel had additional discounts of 8.6% and 7.7% respectively.
Overall, Flipkart chose to not compete aggressively with Amazon during Prime Day this year. Only in Electronics did Flipkart offer an additional discount of 3.2% during the sale. Price changes in Apparel and Health & Beauty were negligible.
Category Deep Dive
Consumer Electronics
In Electronics, Earbuds (14.7%) and Wireless Headphones (12.5%) saw the highest Additional Discounts on Amazon, likely due to high demand for personal audio devices. Smartwatches followed closely at 12.5%, indicating a strong push in the wearable technology sector.
Larger electronic appliances like TVs saw more modest Additional Discounts (5.2%), despite a significant Absolute Discount (42.3%).
Smartphones, interestingly, had the lowest Additional Discount (2.8%) among the subcategories, potentially due to their already competitive pricing or brand-specific strategies.
Brand-level Insights in Consumer Electronics
During the sale event, Soundcore (headphone and earphone brand from Anker) led with the highest Additional Discount of 10.3%, followed closely by OnePlus at 9.3%. These brands clearly leveraged Prime Day to drive sales and potentially gain market share.
Sennheiser and Sony also offered significant additional discounts, indicating aggressive promotional strategies during the event.
Notably, some well-known brands like Apple and HP offered more conservative discounts, possibly due to their established market positions or differing promotional strategies.
Despite modest discounts though, Amazon revealed that the iPhone 13 and OnePlus 12R were among the top premium smartphones customers bought this Prime Day. Prime members shopped from top brands such as LG, IFB, Bosch, and Haier and upgraded to premium appliances through affordability options of no cost EMI, Bank discounts and exchange offers, claims Amazon.
These learnings offer valuable insight into the mindset of the modern Indian consumer, who is ready to purchase premium products. Brands can leverage more detailed market intelligence to drive their positioning in India.
Share of Search Analysis
The Share of Search (SoS) for a brand represents the number of its products in the top 20 search results for relevant search keywords on Amazon, relative to that of its competitors. It provides insights into a brand’s visibility during the sale event.
HP saw the most significant improvement in its Share of Search during Prime Day, gaining 9.2%, with strong growth in both organic (7.5%) and sponsored (1.7%) listings. LG also made substantial gains of 8.2% and 5.3% respectively. Both brands offered significant markdowns on laptops and accessories.
Surprisingly, some popular brands like Apple (-0.6%) and Acer (-1.0%) experienced slight decreases in their SoS. Lenovo notably lost 7.2% of its visibility share, with a significant decline in organic listings (-7.6%). Samsung maintained its strong position with an increase in SoS (3.7%).
Key Takeaways:
While some brands like Soundcore and OnePlus used aggressive discounting to drive sales, others like HP and LG focused on improving their search visibility. The data suggests that a combination of attractive discounts and improved search visibility was key to success during the event. Brands that managed to balance these factors effectively, like HP and Samsung, seemed to perform particularly well. Interestingly, some brands with lower discounts (like HP) still managed to significantly increase their SoS, indicating that factors beyond pricing played a role in visibility during the event.
Apparel
Within the subcategories analyzed, Women’s Shoes (13.4%) and Men’s Shoes (11.6%) saw the highest Additional Discounts on Amazon, indicating a strong focus on footwear during the event. Men’s Shirts followed closely at 11.1%
Women’s Tops had a more modest Additional Discount of 7.9% despite a high Absolute Discount before the event. Similarly, Women’s Dresses showed substantial Additional Discounts (10.4%).
Swimwear for both men and women saw low Additional Discounts (3.7% and 3.0% respectively). Innerwear categories for both men and women had more conservative discounts, with Men’s Innerwear showing the lowest Absolute Discount (19.9%) among all subcategories.
Brand-level Insights in Apparel
During the sale event, Sangani (innerwear brand) led with the highest Additional Discount of 41%, followed by Louis Philippe at 25%. These brands clearly leveraged Prime Day to drive sales and gain market share.
Puma offered the highest Absolute Discount at 60% with a significant Additional Discount of 20%, indicating a consistently aggressive promotional strategy. Chromozome (men’s innerwear brand) and Bacca Bucci (homegrown shoe brand of Shark Tank India fame) also offered substantial Absolute Discounts (57% and 56% respectively), but their Additional Discounts varied significantly (16% and 6% respectively).
Again, some well-known brands like Adidas, and Pepe Jeans offered more moderate Additional Discounts, possibly due to their established market positions.
Share of Search Analysis
Biba (women’s ethnic wear brand) saw the most significant improvement in its SoS, gaining 3.60%, entirely through investments in sponsored listings. Allen Solly and New Balance (shoe brand) also made substantial gains of 3.3% and 3.2% respectively.
Surprisingly, some popular brands like Louis Philippe (-2.00%) and Sangani (-2.10%) experienced decreases in their SoS. Louis Philippe lost its share due to a roll back of its sponsored listings, while Sangini lost ground organically.
Key Takeaways:
While some brands like Sangani and Louis Philippe used aggressive discounting to drive sales, others like Biba and Allen Solly focused on improving their search visibility through sponsored listings.
Some brands with high discounts (like Sangani) saw a decrease in SoS, while others with more modest discounts (like Biba) significantly increased their visibility. This indicates that factors beyond pricing played a crucial role in brand performance during the event.
The significant use of sponsored listings by some brands highlights the importance of paid advertising in gaining visibility during competitive events like Prime Day.
Health & Beauty
Health & Beauty emerged as a standout category during Amazon Prime Day India 2024, with significant discounts and competitive strategies at play.
Within the subcategories we analyzed, Moisturizer (11.9%) and Sunscreen (11.8%) saw the highest additional price reductions on Amazon, likely due to seasonal demand during Prime Days. Make-up followed closely at 10.6%.
Electric Toothbrushes had the highest Absolute Discount at 47.1%, but a lower Additional Discount of 6.0%, highlighting that these products were already significantly discounted before the event. Similarly, Beard Care products showed high Absolute Discounts (43.7%) but lower Additional Discounts (5.7%) during Prime Day.
Everyday essentials like Toothpaste saw more modest discounts (19.9% Absolute, 8.4% Additional), reflecting steady demand for these products.
Brand-level Insights in Health & Beauty
Vaseline led with the highest Additional Discount of 13.1%, followed by Maybelline at 9.5%. These brands clearly leveraged Prime Day to drive sales and potentially gain market share.
Honest Amish (beard care brand) and Nivea also offered substantial Absolute Discounts (15.6% and 8.2% respectively), but their Additional Discounts were lower, indicating pre-existing promotional strategies.
Once again, some well-known brands like Colgate, Dove, and Neutrogena offered more conservative discounts, possibly due to their established market positions or differing promotional strategies.
Share of Search Analysis
Maybelline saw the most significant improvement, gaining 5.6% in SoS, all through organic listings. The brand has been aggressively increasing market share with several new product launches and influencer campaigns. Nivea and Vaseline also made substantial gains of 5.2% and 3.8% respectively.
In this category as well, some popular brands like Dove (-0.4%), Pond’s (-0.8%), and Honest Amish (-0.8%) experienced slight decreases in their SoS. Parodontax (toothpaste brand) notably lost 1.3% SoS, with most of the decline in sponsored listings. Colgate maintained its strong position with a slight increase in SoS (0.2%), reflecting its dominant market presence.
Key Takeaways:
While some brands like Vaseline and Maybelline used aggressive discounting and improved visibility to drive sales, others like Colgate and Dove relied more on their established market positions.
None of the brands made any significant strides in sponsored listings, revealing that brands in this category are not yet fully leveraging retail media to help manage and boost their visibility on online marketplaces.
Final Thoughts
Amazon Prime Day 2024 has redefined the eCommerce landscape in India, yet again, showcasing diverse strategies employed by brands to maximize their impact. By understanding and leveraging these insights, brands and retailers can better navigate future sales events, optimizing their eCommerce strategies to achieve sustained growth and success.
Stay tuned to our blog for more in-depth analyses of brand and retailer performance across various retail events.
Reach out to us today to learn how you can leverage data-driven insights to optimize your eCommerce strategy.
