Black Friday has evolved from a purely Western retail phenomenon into a global shopping event. India is no exception. While the country celebrates its own mega sale events like the Great Indian Festival and Big Billion Days, Black Friday has carved out its own space in the Indian retail calendar. E-commerce in India is expected to reach $325 billion by 2030, with festive shopping seasons driving significant portions of that growth.
So how did Indian retailers and brands navigate Black Friday 2025? At DataWeave, we analyzed pricing trends across four major categories: Consumer Electronics, Home & Furniture, Health & Beauty, and Apparel. Our AI-powered retail intelligence platform tracked nearly 128,000 SKUs across leading platforms including Amazon India, Flipkart, Myntra, and others, revealing how the Indian market approached discounting and brand visibility during this high-stakes shopping period.
Our Methodology
DataWeave monitored average discount percentages across major Indian e-commerce platforms during two distinct periods:
Pre-Black Friday: Up to November 23, 2025 – capturing early promotional activity and baseline pricing
Black Friday Week: November 24 – December 1,2025 – spanning Thanksgiving week through Black Friday (November 28) and Cyber Monday (December 1)
We analyzed top-ranked products across subcategories on major retail sites, alongside Share of Search data, a metric that measures brand visibility by tracking which brand names appear in the top 20 search results for high-intent keywords.
Here’s a quick look at the overall discounts this Black Friday in India:
Consumer Electronics
Consumer electronics remain a cornerstone of Black Friday shopping in India, with smartphones and laptops driving significant online sales. Our analysis of 20,439 SKUs reveals distinctive discount patterns across subcategories.
Subcategory Discount Analysis
The category averaged 23.2% pre-Black Friday discounts with an additional 0.5% during Black Friday Week. Drones and TVs led pre-sale discounting at 47.1% and 45.2% respectively, suggesting retailers wanted to clear high-ticket inventory ahead of the main event. Tablets (25.2%) also saw aggressive pre-sale pricing. During Black Friday Week, Earbuds saw the most substantial additional discounts at 1.1%, while categories like Laptops and Smartphones, already heavily discounted, had minimal incremental price cuts at 0.2%.
Share of Search: Brand Visibility Trends
The most notable is Cofendy, electronics accessories and speaker brand, that saw the share of search rise from 1.4% pre Black Friday to 8.2% during the event. Realme followed with a solid 2.3% gain, reinforcing its position as a rising smartphone brand. Smart gadget brand Hammer also saw visibility increase by 1.4% at par with OnePlus, Fastrack, HP and Asus.
Samsung saw the highest brand visibility with share of search at 9.6% pre-event and 9.5% during Black Friday week, despite seeing a small drop in visibility. Audio brand Boat saw a visibility drop by 0.8%, while Xiaomi saw the share of search drop by 0.1%. This shift suggests that Indian consumers were drawn to newer brands and compelling deals across computing and mobile devices during the sale period.
Health & Beauty
The Indian beauty and personal care market is experiencing rapid growth, expected to reach $30 billion by 2027. Black Friday has become an important sales window for beauty brands and retailers. Our analysis of 19,854 SKUs reveals distinct patterns.
Subcategory Discount Analysis
The category averaged 17% pre-Black Friday discounts with an additional 0.4% during Black Friday Week. Beard Care led early discounting at 21.3%, reflecting strong pre-sale positioning in men’s grooming. Conditioner (17.2%), Makeup (16.9%), and Moisturizer (16.9%) also saw solid baseline promotions. During Black Friday Week, Makeup and Sunscreen saw the highest additional discounts at 0.5% each, while Electric Toothbrush and Toothpaste maintained modest incremental discounts at 0.2%.
Share of Search: Brand Visibility Trends
Affordable and emerging beauty brands dominated during Black Friday in India. Kellsie (beauty tools brand) surged from 1.6% to 6.5%. Classic mass-market brands like Pond’s (+3.4%) and Parachute Advanced (+2.8%) also performed strongly, alongside men’s grooming favorite Beardo (+1.6%). Other popular brands like Maybelline, Tresemme, Vaseline, all saw share of search and visibility increase during Black Friday.
Skincare brand Minimalist made a notable entry, jumping from 0% to 2.0% visibility. The flip side? Premium international brand L’Oréal Paris dropped from 8.3% to 6.5%, losing visibility during Black Friday.
Apparel
Our analysis of 57,537 SKUs reveals interesting discount dynamics.
Subcategory Discount Analysis
The category averaged 14.3% pre-Black Friday discounts with an additional 0.1% during Black Friday Week. Men’s Swimwear and Men’s Athleisure led pre-sale promotions at 22.2% and 18.5% respectively, while Women’s Swimwear and Women’s Shoes also saw strong pre event discounts at 19.3% and 17.6%.
Black Friday Week saw minimal incremental discounting across all subcategories, with most adding just 0.1-0.3%. The relatively subdued incremental discounting indicates that early birds captured the best deals, or that margins were already stretched from pre-sale promotions.
Share of Search: Brand Visibility Trends
Pepe Jeans dominated visibility, surging from 7.4% to 24.6%, a staggering gain that represents the largest visibility increase across all categories. Jockey also performed strongly with a 6% gain, solidifying its position in innerwear.
On the flip side, athletic and footwear stalwarts faced headwinds: Speedo dropped 1.4% and Bata fell 1.1%. This data suggests that during Black Friday 2025 in India, denim and lifestyle fashion brands invested heavily in promotional visibility, capturing massive mindshare at the expense of traditional athletic and footwear brands.
For brands and retailers navigating India’s increasingly competitive e-commerce landscape, the 2025 Black Friday data reveals a critical insight: pre-sale positioning matters more than Black Friday Week discounting. Early promotional investment and visibility campaigns delivered far greater returns than late-stage price drops, fundamentally reshaping how brands should approach this shopping event.
Want to understand how DataWeave’s retail intelligence platform can help your business make data-driven decisions during peak sales events? Contact us to learn more about competitive insights, price intelligence, assortment analytics, content analytics, and digital shelf analytics.
Check out our analysis on Black Friday 2025 Pricing and Discount trends in the USA, Canada, UK, and Germany. Follow our blog for more insights on retail pricing trends, brand visibility analysis, and data-driven commerce intelligence.
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.
Every major shift in commerce has been driven by data. A century ago, shopkeepers relied on ledgers to track sales. In the supermarket era, loyalty cards and barcodes turned transactions into insights. With the rise of eCommerce, clickstream data and online analytics reshaped how products were merchandised and sold.
Now, we are entering the next chapter: agentic commerce.
In this new paradigm, autonomous AI agents will handle the tasks that once required teams of analysts, merchandisers, and pricing specialists. Imagine an agent that monitors competitor prices across dozens of retailers, recommends adjustments, and pushes updates to a dynamic pricing engine, all in real time. Picture a shopper’s digital assistant scanning marketplaces for the right mix of price, delivery time, and customer reviews before making a purchase on their behalf.
These aren’t distant scenarios. They’re unfolding now. Industry analysts estimate the enterprise AI market at $24 billion in 2024, projected to grow to $155 billion by 2030 at nearly 38% CAGR . Meanwhile, 65% of organizations already use web data for AI and machine learning projects, and 93% plan to increase their budgets for it in 2024. The trajectory is undeniable: the next era of commerce will be built on AI-driven decision-making.
And what fuels those AI-driven decisions? Data. Reliable, structured, timely, and compliant data.
The Data Problem No One Can Ignore
Here’s the paradox: just as data has become most critical, it has also become harder to acquire.
For data and engineering leaders, the challenges are painfully familiar:
Old school scrapers that collapse whenever a site changes its HTML or introduces new interactivity.
Constant maintenance cycles, with engineering teams spending 20-40 hours a week debugging, rerunning, and patching scripts.
Low success rates, with in-house approaches succeeding just 60-70% of the time.
Complex infrastructure, from managing proxies to retry logic, pulls attention away from higher-value work.
But the costs go far beyond engineering frustration.
For retailers, broken pipelines mean competitive blind spots. A pricing team without reliable visibility into competitor moves can’t respond fast enough, risking lost margin or missed sales. Merchandising teams trying to optimize assortments are left with incomplete data, making poor stocking decisions inevitable.
For brands, unreliable data disrupts visibility into the digital shelf. Products might be misplaced in search rankings, content could be outdated or incomplete, and reviews could signal issues, but without continuous monitoring, those signals are missed until it’s too late.
For AI and ML teams, poor-quality training data means underperforming models. Without clean, consistent, and large-scale inputs, even the most sophisticated algorithms produce flawed predictions.
Finally for consulting firms and research providers, fragile collection systems can compromise credibility. Clients expect robust, evidence-backed recommendations. Data gaps erode trust.
The reality is stark: fragile pipelines don’t just waste engineering hours. They undermine competitive agility, customer experience, and business growth.
Enter the Data Collection API
DataWeave’s Data Collection API is a self-serve, enterprise-scale platform designed to deliver the data foundation today’s enterprises need, and tomorrow’s agentic AI systems will demand.
At its core, the API replaces brittle scrapers and ad hoc tools with a resilient, adaptive, and compliant data acquisition layer. It combines enterprise reliability with retail-specific intelligence to ensure that structured data is always available, accurate, and ready to power critical workflows.
Here’s what makes it different:
Enterprise-scale throughput: The API can process thousands of URLs in a single batch or handle continuous, high-frequency scrape. Whether you need daily pulses or near real-time monitoring, it scales with you.
Flexible access modes: Technical teams can integrate directly into internal workflows via API, while business users can configure jobs through a no-code interface. Everyone gets what they need without bottlenecks.
Adaptive resilience: As websites evolve, the API adapts automatically. No frantic patching, no firefighting.
Structured outputs, your way: Clean JSON, CSV, or WARC formats are delivered directly into your environment – AWS S3, Snowflake, GCP, or wherever your data stack lives.
Built-in monitoring and self-healing: Automated retries, real-time logs, and usage dashboards keep teams in control without manual oversight.
Compliance by design: WARC-based archiving and SOC2 alignment ensure data pipelines are auditable, trustworthy, and enterprise-ready.
This isn’t about scraping pages. It’s about creating a reliable data utility, a system that transforms raw web inputs into structured, actionable data streams that enterprises can trust and scale on.
Who It’s Built For (And How They Use It)
The Data Collection API isn’t limited to one role or industry. It’s been designed with multiple stakeholders in mind, each of whom can apply it to solve pressing challenges:
Retailers and Consumer Brands
Retailers live and die by competitive awareness. With the API, pricing teams can monitor SKU-level prices and promotions across channels, ensuring they don’t leave margin on the table. Merchandising leaders can track assortment coverage, identifying gaps relative to competitors. Digital shelf teams can measure search rankings, share of voice, and content completeness. The result is faster responses, stronger category performance, and fewer blind spots in shopper experience.
AI & Machine Learning Teams
AI teams depend on data at scale. Whether training a natural language model to understand product descriptions or a computer vision system to analyze images, the Data Collection API delivers the structured, high-quality inputs they need. Reviews, ratings, attributes, and product images can all be captured and delivered at scale. For teams building predictive models, from demand forecasting to personalization, the difference between mediocre and world-class often comes down to input quality. This API ensures AI systems are always learning from the best data available.
Retail Intelligence & Pricing Platforms
Technology providers serving retailers and brands face unforgiving client expectations. Missed SLAs on data delivery can mean churn. By using the Data Collection API as their acquisition layer, platform providers gain enterprise reliability without rebuilding infrastructure from scratch. They can scale seamlessly with client needs while maintaining the integrity of the insights their customers rely on.
Marketing & Advertising Teams
For marketing leaders, competition is visible every time a shopper searches. The API enables teams to track keyword rankings, ad placements, and competitor promotions with consistency. Instead of anecdotal data or partial coverage, marketers get a full picture of their brand’s digital presence and the strategies competitors are using to capture share of voice.
Consulting Firms & Research Providers
Consultancies and market research agencies deliver strategy. But a strategy without evidence is just opinion. The API allows these firms to back every recommendation with structured, large-scale data. Whether advising on pricing, benchmarking performance, or publishing analyst research, firms can deliver trustworthy insights without taking on the cost or distraction of building fragile data pipelines.
The diversity of these use cases demonstrates why the API is a platform for collaboration across industries, ensuring every stakeholder, from engineers to strategists, has the reliable data foundation they need.
Why DataWeave, Why It Matters
Many vendors claim to deliver web data. Few can deliver it at enterprise scale, with commerce-specific expertise, and with proven ROI.
What sets DataWeave apart isn’t just that we provide data; it’s the way we do it, and the outcomes we enable.
Commerce expertise baked in: With 14+ years of experience powering the world’s leading retailers and brands, DataWeave brings domain-specific intelligence that generic scraping vendors simply can’t. Our schemas are designed for commerce. Our defaults are smarter because they’re informed by retail realities.
Adaptability without firefighting: Most tools break when websites evolve. Our API adapts automatically, minimizing the need for engineering intervention. Teams stay focused on innovation, not maintenance.
Accessible to everyone: Whether you’re a senior data engineer automating workflows or a business analyst configuring a quick scrape, the API meets you where you are with both API and no-code interfaces.
Enterprise-grade trust: Reliability and compliance are built in, not bolted on. With SLA-backed delivery, SOC2 alignment, and audit-ready archiving, the API is trusted by enterprises that can’t afford uncertainty.
This combination makes the Data Collection API not just a technical solution but a strategic partner for enterprises preparing for the age of agentic commerce.
A Foundation for the Future
The Data Collection API is more than an answer to today’s frustrating data problems. It represents a strategic foundation for tomorrow’s growth, designed to scale alongside the increasingly complex demands of commerce in the AI era.
At the heart of DataWeave’s vision is the Unified Commerce Intelligence Cloud, a layered ecosystem that transforms raw digital signals into strategic insights. The Data Collection API is the entry point, the essential first layer that ensures enterprises have a reliable supply of the most important raw material of the digital economy: data.
Collection: Enterprise-grade acquisition of web data at scale. From product pages and search results to reviews and promotions, enterprises can finally count on continuous, structured inputs without worrying about fragility or failure.
Processing: Once collected, data is normalized, enriched, and matched across sources. What was once noisy and inconsistent becomes clean, comparable, and immediately actionable.
Intelligence: On top of this foundation sits advanced analytics, solutions for pricing optimization, assortment planning, promotion tracking, and digital shelf visibility, enabling sharper decisions at the speed of the market.
This progression means enterprises don’t have to transform overnight. Many start small, solving urgent challenges like competitive price tracking or digital shelf monitoring. From there, they can expand naturally into richer intelligence capabilities, knowing that their data foundation is already strong enough to support more ambitious use cases.
And as agentic AI systems begin to take on a larger share of decision-making, the importance of that foundation grows exponentially. These autonomous systems cannot operate effectively without clean, continuous, and contextual data. Without it, even the most sophisticated AI will falter, making poor predictions or incomplete recommendations. With it, they can operate at full capacity, powering dynamic pricing, real-time demand forecasting, and personalized shopping experiences at scale.
The Data Collection API isn’t just about reducing engineering pain today. It’s about preparing enterprises to compete and win in an AI-driven marketplace that never sleeps.
Getting Started
For teams tired of fragile scrapers, this is a chance to reset. For enterprises preparing for the next era of commerce, it’s a chance to build a foundation that can scale with them.
If your teams are still struggling with generic and inflexible data scrapers, request a demo now to see the DataWeave’s Data Collection API in action.
The retail landscape has reached a point where traditional strategies are no longer enough. Tariff shocks are driving up costs in categories like electronics and apparel, while freight disruptions are extending lead times. Retail executives are now operating in an environment of unprecedented complexity.
In response, many of the world’s largest retailers and brands are shifting critical operations to Global Capability Centers (GCCs) in regions such as India, East Asia, and Africa. Once focused on back-office support, GCCs are rapidly evolving into strategic intelligence hubs powering high-impact decisions on pricing, assortment, content, analytics, and more. These decisions consistently influence both top-line growth and bottom-line performance for multibillion-dollar enterprises.
At DataWeave, we’ve been working closely with GCCs to help them achieve technical, tactical, and strategic advantages through actionable market intelligence. To further engage with the community and exchange ideas, we recently hosted our first GCC VIP Roundtable in Bengaluru. Leaders from organizations including JC Penney, Lowe’s, Kenvue, and ARKO joined us for a series of candid and insightful discussions on retail’s most pressing challenges and the evolving role of GCCs in driving leadership amid disruption.
In this article, we share the key themes, challenges, and solutions that emerged from these conversations.
Where GCCs Are Facing the Biggest Challenges
The Adoption Lag Challenge
A recurring concern among GCC leaders at the roundtable was the delay in translating insights into action. As one leader noted, “We have the data and insights at our fingertips, but it can take our internal teams an entire quarter to respond.” Others agreed with this sentiment, recognizing that such adoption lags create a competitive risk.
The pattern is consistent across organizations, while GCCs excel at generating insights, real-time responsiveness at the store level remains aspirational due to change management challenges and operational inertia.
The Integration Imperative
Our discussion with GCC leaders coincided with creeping evidence for the impact of tariffs across retail categories. Managing competitive intelligence is a difficult enough challenge. Now, pricing strategies must account for not just competitive positioning, but also rapid cost structure changes that vary dramatically by product origin and category.
Home & Furniture categories are experiencing the steepest price inflation trajectory, with increases reaching 4.7% by mid-2025.
Toys and Electronics follow closely at 3.8% and 2.1% respectively, both heavily dependent on international supply chains.
The Strategic Intelligence Evolution
Leading GCCs are responding by reimagining their role around critical capabilities:
Beyond Traditional Competitive Intelligence
Pricing and content strategies now require integration of multiple variables:
Category-specific trends
Tariff impacts
Competitive positioning
Broader macroeconomic factors
Traditional pricing models that worked in stable environments are proving inadequate for this new reality.
Real-Time Responsiveness as a Competitive Edge
The shift from periodic reporting to always-on intelligence systems emerged as a critical theme. GCC leaders discussed the need for:
Technical Infrastructure: Moving from batch processing to streaming data architectures, handling millions of SKUs daily
Analytical Capability: AI-driven data refinement, including computer vision and natural language processing
Organizational Agility: Breaking down silos between marketing, merchandising, and operations
Regional Complexity Management
The group highlighted a key gap in the competitive intelligence data they receive. Insights often overlook region-specific nuances such as local competitive landscapes, regulatory requirements, and consumer preferences. They stressed that effective pricing strategies must go beyond base pricing to also factor in card-linked offers, loyalty programs, and delivery options.
Operational Challenges
Several technical and operational issues emerged during discussions:
Data Quality and Accessibility: Questions around whether platforms provide pre-refined data or raw dumps, and the availability of implementation layers for easy visualization
Change Management: The persistent challenge of translating insights into action at operational levels
The Path Forward: Building Intelligence-Driven GCCs
The most successful GCCs of the future will deliver what our attendees called “closed-loop intelligence.” These insights will not only inform decisions but also continuously improve through feedback and results tracking.
This is something that DataWeave excels in.
This requires investment in three core areas:
Data Acquisition: Comprehensive, timely product and pricing data collection across retail platforms
Intelligence Refinement: AI-powered transformation of raw data into meaningful relationships across retailers, brands, categories, and competitive landscapes
Insight Delivery: Flexible output capabilities serving everything from executive dashboards to automated pricing systems
Key Takeaways for GCC Leaders
Our roundtable revealed that successful GCCs share common characteristics:
Proactive Decision-Making: Moving beyond reactive responses to anticipate market changes
Integrated Intelligence Systems: Combining traditional competitive data with modern digital signals, including social media trends
Cross-Functional Impact: Establishing strategic partnerships with business units rather than transactional service relationships
Measurable ROI: Proving value through pricing strategies that demonstrably improve margins
The retail industry will likely become more complex, not less, in the coming years. The GCCs that invest in sophisticated competitive intelligence capabilities today will be the ones helping their organizations not just navigate this complexity, but thrive within it.
The depth of insight and openness in the discussions during the event underscored the value of bringing this community together. As we continue to strengthen our connections with GCC leaders, we look forward to hosting more such forums.
If you’d like to be part of the conversation, reach out to us today!
Tariffs can spike landed costs overnight, regulations rewrite labelling rules, and competitors slash prices before your team finishes its daily stand-up. And yet, some consumer brands thrive.
The winning brands see changes early, decide quickly, and execute flawlessly across the digital shelf. This post blends three decades of pricing and merchandising expertise with timely digital shelf insights from DataWeave, offering a clear path forward for brands navigating today’s volatile retail environment.
From Cost Shock to Chronic Uncertainty
Tariffs are no longer just one-off headlines; they’ve become an unpredictable, ongoing variable in the global marketplace. The true challenge isn’t always the duty rate itself, but the constant whiplash of not knowing if, when, or how much that duty will change. This pervasive uncertainty is having a tangible impact:
Unpredictable Competitive Response: Delaying price moves while watching competitors can erode margins as much as tariffs.
To stay ahead, pricing decisions must be stress-tested against multiple tariff scenarios and aligned with likely competitor reactions. Timing matters as much as accuracy, move too soon or too late, and margins suffer either way.
The Tariff Math No One Can Afford to Get Wrong
When it comes to tariff disruption, the difference between profit and loss often hinges on a precise understanding of a three-step process. Get any part of this chain wrong, and the financial ripple effect can undermine pricing and promotions. The duty you pay, therefore, is the direct result of the following three critical steps:
Step 1: Harmonized System (HS) Code
What it is: A six- to ten-digit classifier that drills down to product sub-types.
Why it matters: A single digit change can shift an item into a higher-tariff bracket.
Step 2: Country of Origin
What it is: The nation in which the imported item was made.
Why it matters: Mis-tagging the origin can lead to mis-pricing and inaccurate margin calculations.
Step 3: Trade-Agreement Overlay
What it is: Differentiation between the World Trade Organization (WTO) baseline tariffs and special trade agreements (e.g., USMCAUnited States-Mexico-Canada Agreement).
Why it matters: The same HS code can result in significantly different duties, up to a 10% swing, depending on the originating country (see the example below).
This isn’t just about paying the correct duty; it’s about safeguarding your bottom line in a global marketplace where every digit and every designation carries substantial weight.
The wrong origin, the wrong rule, the wrong margin.
Hard Numbers: Where Prices Are Already Climbing
DataWeave’s latest digital shelf analysis shows import-driven price inflation diverging sharply by source country.
The intricate dance of HS codes, country of origin, and trade agreements directly translates into the prices consumers see. And the data doesn’t lie. Below, we delve into the hard numbers: where prices are already climbing, as illuminated by DataWeave’s latest digital shelf monitoring, showing significant import-driven price inflation by source country.
China: Products sourced from China are up 5%. This is largely attributable to the numerous tariffs currently imposed on Chinese goods.
Mexico: Prices for products from Mexico have risen by 3%.
United States: Interestingly, even U.S.-sourced products show a 3% increase.
This rise in U.S. product prices might seem counterintuitive if tariffs are solely focused on imports. However, the reality lies in the global supply chain for many products.
Consider guacamole as an example: While the final product might be “Made in the USA,” its components often come from various international sources. Avocados might be imported from Mexico, lime juice from Central America, and seasonings from India or China. Even packaging could originate in Asia. Each of these imported components can be subject to tariffs. Therefore, even if an item is assembled in the U.S., the tariffs on its constituent parts contribute to an overall price increase, explaining the rising rates for U.S.-sourced goods.
Action step: Map tariff exposure at both finished-goods and component-level to avoid “Made in USA” blind spots.
Timing Is a Competitive Weapon
With duty tables and competitor reactions changing fast, the question is: move first or follow? Early movers recoup cost fastest but risk overshooting if tariffs ease; laggards may enjoy a brief price advantage but suffer sudden margin compression.
The Strategic Dilemma
The table below illustrates this strategic choice and its potential outcomes:
Shrinkflation: Margin Patch or Trust Erosion?
Beyond direct price adjustments, many brands are turning to shrinkflation to manage tariff-driven cost pressure, shaving net weight instead of hiking prices. DataWeave’s analysis reveals an average package reduction of 5 – 6%, with extreme cases reaching 15 – 25%, sometimes even coupled with a shelf-price increase.
While this can cushion immediate margin, it comes at a significant cost: brand credibility. Savvy shoppers quickly spot these changes, sharing “before-and-after” photos online and fueling consumer frustration. What begins as a margin patch can rapidly erode trust and damage long-term loyalty.
Ultimately, navigating this volatile environment requires dynamic intelligence and a holistic pricing strategy that balances profitability with market share and, crucially, consumer trust.
Price Hikes May be Inevitable, But You Can Still Run Your Digital Shelf
Tariff‑driven cost pressure can force list‑price increases, but it does not dictate how well your products show up, sell through, or satisfy shoppers online. Those outcomes still hinge on five levers that live entirely inside your control. Master them and you cushion margin hits while protecting (or even expanding) share.
The Five Levers of Digital‑Shelf Control
Inventory Depth – Maintain online in‑stock rates above 95 percent for high‑velocity SKUs and flag substitute logic when unavoidable out‑of‑stocks occur.
Content Quality & Accuracy – Keep titles keyword‑rich, imagery crisp, and attributes complete so search filters never bury you.
Ratings & Reviews Cadence – Proactively request fresh reviews to earn retailer search boosts and reassure value‑conscious shoppers.
Retail‑Media Precision – Bid where pages are healthy and in‑stock; pause spend on broken listings that leak conversion and ROAS.
Fulfillment Excellence – Monitor pick‑pack accuracy, on‑time delivery, and substitution rates; each one influences retailer algorithmic visibility.
Content Hygiene Keeps You Visible, Compliant, and Conversion-Ready
Missing or incorrect product attributes (e.g., “gluten-free,” “caffeine content”) can swiftly jeopardize both regulatory compliance and your product’s fundamental search visibility. Simply put, if it’s not labeled right, it won’t be found.
This impact plays out in two crucial areas:
Retailer Search Visibility: Filter logic on major e-commerce platforms like Target.com, Walmart.com, and Instacart is increasingly driven by precise attribute tags (e.g., “gluten-free,” “BPA-free,” “0g added sugar”). Fail to provide or correctly format these claims, and your product will simply never appear when shoppers apply these critical search filters. You become invisible to a motivated audience.
Regulatory Compliance: Global regulatory bodies, including the U.S. FDA and EU authorities, now treat online product detail pages as officially regulated labeling space. This means that a single missing allergen statement or an inaccurate nutritional claim can trigger severe consequences, from product takedowns and hefty fines to a devastating “straight-to-zero” share of search. Non-compliance isn’t just a legal risk; it’s a direct threat to your market presence (see example below).
The Hygiene Playbook: Audit → Score → Fix → Grow
Your Product Detail Pages (PDPs) are your digital storefronts, and they need to be impeccable. Modern content-intelligence tools are like vigilant auditors, constantly scanning, structuring, and scoring every PDP across your retail network.
Tools like DataWeave do the heavy lifting by:
Surfacing critical gaps: They’ll pinpoint issues like blurry images, inaccurate titles, or missing nutrition information.
Optimizing for search: They ensure your product attributes align with live search filters, turning claims into clicks.
Flagging compliance risks: You’ll know about potential issues before regulators or retail partners ever do.
Quantifying your impact: Get a clear Content Quality Score that your teams can own and improve, week after week.
When you execute this well, it’s not just about tidying up; it’s a powerful growth engine. This proactive approach fuels every step of the digital customer journey – from getting found, to winning the click, converting the cart, and ultimately, capturing reviews that boost your search rankings.
A Case Study: Bush’s Beans Converts Visibility into Revenue
Before Bush’s Beans achieved rapid success with their “audit → scorecard → rapid-fix” approach, they confronted a significant hurdle. Here’s how they overcame it to drive impressive revenue growth.
The Challenge
Bush’s Beans saw its e-commerce contribution stall at just 1.5 percent while competition in canned goods intensified. A quick audit revealed three root causes:
Dipping online sales that signalled slipping visibility and conversion.
Fragmented product content across major retailer sites as images, titles, and claims were inconsistent or missing altogether.
Heavier category competition making it harder to hold first-page search positions.
The Fix
The brand adopted DataWeave’s Digital Shelf Analytics to create a single source of truth for every PDP. A lean internal team then:
Ran content audits across priority retailers to surface incomplete or non-compliant attributes.
Prioritized quick wins focusing on high-velocity SKUs where simple edits (e.g., adding pack-size keywords or allergy statements) would unlock search filters.
Tracked progress weekly using an automated scorecard to keep everyone focused on the next set of fixes.
The Win
Twelve months later the numbers told the story:
Bush’s Beans transformed their product data into a strategic asset, significantly improving online visibility, safeguarding brand reputation, and driving sustained revenue growth. Accurate and complete product pages ensured compliance and boosted search rankings, directly increasing sales. While you can’t control external factors like tariffs, you can control the quality and compliance of your product pages and that control directly translates margin pressure into market share gains.
Unified Insight: Turning Signals into Sustained Advantage
Imagine one living dashboard where every digital shelf signal like timely price moves, share-of-search shifts, retail media spend, on-shelf availability gaps, compliance flags, MAP breaches, plus content and review health flows together. With that single lens, the “whose numbers are right?” debate disappears and cross-functional teams can act in minutes rather than days.
A consolidated feed lets you:
Build market awareness: Spot competitor price changes as they happen, understand who owns first-page search, and measure the true lift of retail media campaigns.
Mitigate emerging risks: Surface impending out-of-stocks before rank erodes, catch claim or label errors ahead of audits, and receive instant alerts when a seller breaks MAP.
Activate growth levers: Prioritize content edits that open search filters and use ratings and reviews trends to fine-tune messaging and assortment.
Brands that weave these signals into one workflow move faster than the disruption. That’s the connective tissue highlighted in our recent post on pairing Digital Shelf Analytics with Digital Shelf Impact Modelling: when granular shelf data sits beside strategic performance metrics, smarter decisions follow.
A platform like DataWeave brings the pieces together quietly ingesting millions of price checks, availability reads, and PDP audits each day, then presenting only the next best actions. The payoff is simple: sharper market awareness, lower operational risk, and growth that compounds with every iteration.
Keep Moving, Keep Winning
Tariffs, evolving regulations, and agile competitors are no longer storms; they are the climate. Brands that pair a clear, shared insight stream with rapid execution turn volatility into durable advantage. Keep your data united, keep iterating on the five digital-shelf levers, and every new headwind becomes another step ahead.
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.
Do you remember when the movie Super Size Me came out? If you missed it, it was about the harmful effects of eating fast food too often. One aspect of the film that stands out is McDonald’s clever use of pricing to encourage consumers to buy bigger—and therefore more expensive—meals.
Hungry patrons could upgrade their meal to a Super Size version for only a few cents more. In doing so, McDonald’s was able to capitalize on perceived value, i.e., getting more product for an apparent lower total price for the volume. It encouraged restaurant-goers to spend a little more while feeling like they got a great deal. It was a smart use of strategic pricing.
There are hundreds of pricing relationship types like this one that pricing leaders need to be aware of and can use to their advantage when creating their team’s pricing strategy and workflows. You need to maintain profitable and logical price relationships across your entire product portfolio while keeping up with the competition. After all, the gimmick to Super Size would never have worked if the upgrade had been of less value than just buying another burger, for example.
In this article, we’ll examine more real-world examples of pricing challenges so you can consider the best ways to manage complex price relationships. We’ll examine things like package sizes, brands, and product lines and how they’re intertwined in systematic price relationship management. Read on to learn how to prevent margin erosion, improve customer perception of your brand, and keep your pricing consistent and competitive.
The Price Relationship Challenge
Pricing is one of the most challenging aspects of managing a retail brand. This is especially true if you are dealing with a large assortment of products, including private label items, the same products of differing sizes, and hundreds, or even thousands, of competing products to link. Inconsistencies in your price relationship management can confuse customers, erode trust, and harm your bottom line.
Let’s take a look at a few common pitfalls in portfolio pricing that you might run into in real life to better understand the impact on customer perception, trust, and sales.
Private Label vs. Premium Product Pricing
Let’s consider a nuanced scenario where price relationships between a retailer’s private label and premium branded products create an unexpected customer perception. Imagine you’re in a supermarket, comparing prices on peanut butter. You’ve always opted for the store’s private-label brand, “Best Choice,” because it’s typically the more affordable option. Here’s what you find:
Best Choice (Private Label) 16 oz – $3.50
Jif (National Brand) 16 oz – $3.25
At first glance, this pricing feels off—shouldn’t the private label be the cheaper option? If a customer has been conditioned to expect savings with private-label products, seeing a national brand undercut that price could make them pause. This kind of pricing misalignment can erode trust in private-label value and even push customers toward the national brand. When price relationships don’t follow consumer expectations, they create friction in the shopping experience and may lead to lost sales for the retailer’s own brand.
Value Size Relationships
A strong value-size relationship ensures that customers receive logical pricing as they move between different sizes of the same product. When this relationship is misaligned, customers may feel confused or misled, which can lead to lost sales and eroded trust.
Let’s look at a real-world example using a well-known branded product—salad dressing. Imagine you’re shopping for Hidden Valley Ranch (HVR) dressing and see the following pricing on the shelf:
HVR 16 oz – $3.99
HVR 24 oz – $6.49
HVR 36 oz – $8.99
At first glance, you might assume that buying a larger size offers better value. However, a quick calculation shows that the price per ounce actually increases with size:
16 oz = $0.25 per ounce
24 oz = $0.27 per ounce
36 oz = $0.25 per ounce
Customers expecting a discount for buying in bulk may feel misled or frustrated when they realize the mid-size option (24 oz) is actually the most expensive per ounce. This mispricing could drive shoppers to purchase the smallest size instead of the intended larger, more profitable unit—or worse, lead them to a competitor with clearer pricing structures.
Retailers must maintain logical price progression by ensuring that price per unit decreases as the product size increases. This not only improves customer trust but also encourages higher-volume purchases, driving profitability while maintaining a fair value perception.
Price Link Relationships
A well-structured price link relationship ensures customers can easily compare similar offerings of the same product and size. When the pricing across different versions or variations of the same item isn’t clear or consistent, it can confuse customers and damage trust, ultimately leading to missed sales and a negative brand perception.
Let’s break this down with an example of a popular product—coffee. Imagine you’re shopping for a bag of Starbucks coffee and you see the following pricing on the shelf:
Starbucks Classic Coffee, 12 oz – $7.99
Starbucks Coffee, Mocha, 12 oz – $9.99
Starbucks Ground Coffee, Pumpkin Spice, 12 oz – $12.99
At first glance, the product is the same size (12 oz) across all options, but the prices vary significantly. One might assume that the price difference is due to differences in quality or features, but what if there’s no clear indication of why the different flavors are priced higher than the standard?
After investigating, you may realize that the only differences are related to different variants—like “Mocha” or “Pumpkin Spice” rather than any significant changes in the product’s core attributes. When customers realize they’re paying a premium for just different flavors, without any tangible difference in product quality, it can lead to feelings of confusion and frustration.
Retailers must ensure that price links between similar offerings are justifiable by clearly communicating what differentiates each product. This avoids the perception that customers are being charged extra for little added value, building trust and encouraging repeat purchases. By maintaining transparent price link relationships, businesses can foster customer loyalty, increase sales, and drive better overall satisfaction.
What is the Foundational Process to Tackle the Price Relationship Challenge?
Now that we’ve reviewed several challenges brands face when pricing their products, what can be done about them?
If you’re a pricing leader, you must create a robust pricing strategy that considers customer expectations, competitive data, sizing, and the overall value progressions of your product assortment. These are the three foundational steps to solve your price relationship challenges.
First, you need to group products together accurately.
Second, you need to establish price management rules around the group of related items.
Third, you should set in place a process to review your assortment each week to see if anything is out of tolerance.
This process is difficult, if not impossible, to manage manually. To effectively set up and execute these steps, you’ll need the help of an advanced pricing intelligence system.
Implementation Strategy
Want to know how to roll out a price relationship management strategy? Follow this implementation strategy for a practical way to get started.
Set up price relationship rules: Determine which of your products go together, such as same products with different sizes or color options. Assign different product assortment groups and determine tolerances for scaling prices based on volume or unit counts.
Monitoring and maintenance: Establish rules to alert the appropriate party when something is out of tolerance or a price change has been discovered with a competitive product.
Exception management: Only spend time actioning the exceptions instead of pouring through clean data each week, looking for discrepancies. This will save your team time and help address the most significant opportunities first.
Change management considerations: Think about the current processes you have in place. How will this affect the individuals on your team who have managed pricing operations? Establish a methodology for rolling this new strategy and technology out over select product assortments or brands one at a time to build trust with internal players.
DataWeave offers features specifically built to help pricing teams manage pricing strategies. These applications can help you optimize profit margins and improve your overall market positioning for long-term success. A concerted effort to create brand hierarchies within your own product assortment from the get-go, followed by routine monitoring and real-time updates, can make all the difference in your pricing efforts.
Within DataWeave, you can create price links between your products (value sizing) and those of the competition. These will alert you to exceptions when discrepancies are discovered outside your established tolerance levels. If a linked set of your products in different sizes shows inconsistent pricing based on scaled volumes, your team can quickly know how to make changes. If a competitor’s price drops significantly, you can react to that change before you lose sales.
DataWeave even offers AI-driven similar product matching capabilities, which can help you manage pricing for private label products by finding and analyzing similar products across the market.
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.
It’s up to senior leadership, whether you’re a Chief Strategy Officer, Pricing Executive, or Commercial Director, to think big picture about your company’s competitive intelligence strategy. For more junior team members, it’s easy to get caught in the “this is how we’ve always done it” mindset and continue to go through the motions of price monitoring.
You don’t have that luxury—it’s up to you to find and implement new ways to move beyond basic price monitoring and usher your company into an era of achieving actionable insights through competitive intelligence. There is much more to gain from competitive data than simple price monitoring.
How can retailers leverage clean, competitive data to uncover strategic insights beyond basic price comparisons? This article will help you shift your mindset from tactical monitoring to strategic insight generation. We’ll see how sophisticated analysis of clean and refined competitive data can reveal competitor strategies, regional and geographic opportunities, and overall market trends.
It’s time to shift away from standard reporting, which should be left for your pricing owners and end users, and towards gaining competitive intelligence to shape your holistic company pricing strategy. With the right tools, you can make this shift a reality.
Regional Price Intelligence
One significant opportunity you should take advantage of is a greater understanding of regional price intelligence. Understanding the nuances that shape how products, categories, and other retailers’ prices according to geographical differences can set your company up to win customer trust and dollars at checkout.
Understanding geographic and regional pricing strategies
Geographic price intelligence helps leaders leverage market opportunities based on where sales are happening. Variations in how products and categories are priced across regions often reflect competitor tactics, local demand, and cost structures.
Let’s consider an example that impacts a broad geography, such as the entire continental United States – egg prices. Eggs are a staple grocery item and are frequently a loss leader in stores. This means they are products priced below their cost specifically to draw customers into stores.
However, Avian Flu outbreaks affecting millions of birds have become more common recently. These outbreaks drive the cost of eggs higher as flocks must be culled to prevent the spread of the disease. This means that retailers must act to maintain acceptable margins or losses without frightening away customers or losing their trust.
Avian Flu has been especially bad in Iowa and California. Retailers in these regions face tough decisions during outbreaks. They need to figure out how to balance the high prices required to cover the supply shortages with maintaining consumer trust that this staple product will not be perceived as ‘overpriced.’ Customers expect retailers to be fair even when supply chain issues make it challenging to keep prices stable.
Another example impacting the broader USA is credit card defaults. Credit card defaults are reaching levels unseen since the financial crisis of 2008. $46 billion worth of credit card balances were written off in the first nine months of 2024 alone. This unprecedented figure highlights the fact that many Americans are struggling financially. Higher-income earners continue to do ok, but lower-income families are feeling the pressure more than ever.
Understanding the differences between the geographies you sell in can help you construct your pricing strategies better. This is especially true as consumers brace themselves for more anticipated economic hardship.
Retailers must set realistic financial targets without overpricing their catalogs. Otherwise, they risk losing customers who would otherwise have bought their products. Competitive intelligence can help retailers understand how economic disparities impact core consumer bases.
Pricing leaders can leverage insights around geographic variations in supply, demand, and competitor pricing to help in situations like these. With how important eggs are, changes to their price can spill over into other categories. And with credit card defaults affecting hundreds of thousands of Americans, having a way to dive into these topics can help shape overarching strategies.
Customer perception is a recurring theme in competitive intelligence. It’s not only about the actual value your brand offers but the perceived value based on historical context, current events, and competition.
Leveraging Regional Price Differences for Strategic Advantage
On the topic of customer perception, there are strategic ways to use customer perception to your advantage. One of these is detecting cross-market arbitrage opportunities using competitive intelligence and actioning them.
But what is cross-market arbitrage? It’s the practice of exploiting the differences in price across different markets or regions. As a retailer, you can use cross-market arbitrage to your advantage by finding disparities in market conditions and strategically pricing your products to entice customers or offer more value. These opportunities can be in demand, supply, or competitive pricing. Acting quickly in markets where frequent disruptions happen can drive your business forward.
DataWeave’s advanced competitive intelligence tools can analyze regional market trends to help you respond to real-time local demand fluctuations or cost pressures.
Local Market Dynamics
Pricing isn’t a one-size-fits-all operation. Where and what you’re pricing truly matters. Pricing teams should take price zones into account when constructing pricing strategies. This is because pricing isn’t equivalent across locations; it’s localized. Understanding this fact is critical for category-specific considerations at the macro and micro levels.
This map shows a retailer’s regional price differentials on a breakfast basket. With the ability to access and refine your data, you can better construct maps like this one to track local market dynamics. Determine where you need to differentiate prices based on locality, understand the strategic stance of your competitors, and plan if you start to see changes in competitive price zones.
Competitor Strategy Detection
As a retailer, you should continuously monitor your competitors, whether they’re succeeding or stagnating. One example of a major retailer that is seeing growth even during this challenging economic time is Costco. Costco is an interesting case because they do not have stores in every major city or even in every state.
Costco has its brand strategy down, and it is tied to the pricing strategy. Costco has committed to its customers to provide quality items at competitive prices, and they’ve delivered even in a volatile economy. Costco has managed to maintain competitive prices on core merchandise and make strategic pricing adjustments on items that matter most to members. Their private label brand, Kirkland Signature, highlights their value-first approach. They continue to lead with price reductions like:
Organic Peanut Butter: $11.49 → $9.99
Chicken Stock: $9.99 → $8.99
Sauvignon Blanc: $7.49 → $6.99
Costco has figured out how to balance premium offerings for cost-conscious consumers with standardly priced items filling the shopper’s basket. This demonstrates that they have a pricing strategy that relies on competitive intelligence and market trends.
With the correct data and tools, any retailer can conduct research to discover more about their competitors and gain usable insights into their implemented pricing strategies. Once established, this can act as an early warning signal so you can take action.
For example, understanding whether a retailer operates with a stable Everyday Low Price (EDLP) strategy or a more dynamic High/Low pricing approach is crucial when building and maintaining the integrity of your pricing strategy.
Data should be able to show you things like:
When holiday price decreases start to accelerate
How quickly a retailer responds to cost increases (especially at the category or item level)
The cadence of seasonal campaigns and their impact on pricing behavior
When we move beyond the numbers, these patterns tell a story about how to win in today’s competitive retail landscape. After all, pricing isn’t just a standard reporting tactic. In its truest form, it’s a strategy rooted in understanding the bigger picture of your consumers, competition, and the economy.
Actionable Intelligence Framework
With a practical system to turn insights into action, your company’s pricing strategy is much more likely to drive actual results. Merely collecting data and churning out out-of-date reports won’t cut it. Instead, begin to identify patterns and insights for accurate competitive intelligence. Use this simple framework to start setting up a comprehensive competitive intelligence process.
Setting up monitoring systems: Leverage technology to monitor and aggregate data on your competition, market trends, and consumer behavior. Ensure the system chosen can clean and refine the data along the way so it’s ready to be analyzed.
Creating action triggers: Define clear thresholds and triggers based on key insights. These can be things like price changes of a certain amount, competitor moves, assortment changes, or regional and geographic trends. These triggers should prompt specific, pre-planned actions for your team to capitalize on opportunities.
Response protocol development: Change management is easier with a plan. Work on building out and training your teams on protocols for specific triggers. When something arises, they know the protocol to take advantage of the opportunity or mitigate the challenge effectively.
Performance measurement: Measure the impact of your team’s protocol-based actions with the help of pre-determined KPIs and then hone your approach to competitive intelligence based on the results.
Competitive Intelligence at Your Fingertips
Shifting from a latent standard reporting and price monitoring mindset to a growth mindset centered around competitive intelligence doesn’t need to be a struggle. The key is to lean on the tools that will accelerate your company’s journey to finding the right insights and putting action behind them quickly.
Start by conducting a competitive intelligence maturity assessment to evaluate your organization’s current state and identify areas for improvement. Then, create a capability development roadmap for your company to track efficacy and progress toward your goal.
Poor retail data is the bane of Chief Commercial Officers and VPs of Pricing. If you don’t have the correct inputs or enough of them in real time, you can’t make data-driven business decisions regarding pricing.
Retail data isn’t limited to your product assortment. Price data from your competition is as important as understanding your brand hierarchies and value size progressions. However, the vast and expanding nature of e-commerce means new competitors are around every corner, creating more raw data for your teams.
Think of competitive price data like crude oil. Crude or unrefined oil is an extremely valuable and sought-after commodity. But in its raw form, crude oil is relatively useless. Simply having it doesn’t benefit the owner. It must be transformed into refined oil before it can be used as fuel. This is the same for competitive data that hasn’t been transformed. Your competitive data needs to be refined into an accurate, consistent, and actionable form to power strategic insights.
So, how can retailers transform vast amounts of competitive pricing data into actionable business intelligence? Read this article to find out.
Poor Data Refinement vs. Good Refinement
Let’s consider a new product launch as an example of poor price data refinement vs. good data refinement, which affects most sellers across industries.
Retailer A
Imagine you’re launching a limited-edition sneaker. Sneakerheads online have highly anticipated the launch, and you know your competitors are watching you closely as go-live looms.
Now, imagine that your pricing data is outdated and unrefined when you go to price your new sneakers. You base your pricing assumptions on last year’s historical data and don’t have a way to account for real-time competitor movements. You price your new product the same as last year’s limited-edition sneaker.
Your competitor, having learned from last year, anticipates your new product’s price and has a sale lined up to go live mid-launch that undercuts you. Your team discovers this a week later and reacts with a markdown on the new product, fearing demand will lessen without action.
Customers who have already bought the much-anticipated sneakers feel like they’ve been overcharged now, and backlash on social media is swift. New buyers see the price reduction as proof that your sneakers aren’t popular, and demand decreases. This hurts your brand’s reputation, and the product launch is not deemed a success.
Retailer B
Imagine your company had refined competitive data to work with before launch. Your team can see trends in competitors’ promotional activity and can see that a line of sneakers at a major competitor is overdue for sale based on trends. Your team can anticipate that the competitor is planning to lower prices during your launch week in the hope of undercutting you.
Instead of needing to react retroactively with a markdown, your team comes up with clever ways to bundle accessories with a ‘deal’ during launch week to create value beyond just the price. During launch week, your competitor’s sneakers look like the lesser option while your new sneakers look like the premium choice while still being a good value. Customer loyalty improves, and buzz on social media is positive.
Here, we can see that refined data drives better decision-making and competitive advantage. It is the missing link in retail price intelligence and can set you ahead of the competition. However, turning raw competitive data into strategic insights is easier said than done. To achieve intelligence from truly refined competitive pricing data, pricing teams need to rely on technology.
The Hidden Cost of Unrefined Data
Technology is advancing rapidly, and more sellers are leveraging competitive pricing intelligence tools to make strategic pricing decisions. Retailers that continue to rely on old, manual pricing methods will soon be left behind.
You might consider your competitive data process to be quite extensive. Perhaps you are successfully gathering vast data about your competitors. But simply having the raw data is just as ineffective as having access to crude oil and making no plan to refine it. Collection alone isn’t enough—you need to transform it into a usable state.
Attempting to harmonize data using spreadsheets will waste time and give you only limited insights, which are often out of date by the time they’re discovered. Trying to crunch inflexible data will set your team up for failure and impact business decision quality.
The Two Pillars of Data Refinement
There are two foundational pillars in data refinement. Neither can truly be achieved manually, even with great effort.
Competitive Matches
There are always new sellers and new products being launched in the market. Competitive matching is the process of finding all these equivalent products across the web and tying them together with your products. It goes beyond matching UPCs to link identical products together. Instead, it involves matching products with similar features and characteristics, just as a shopper might decide to compare two similar products on the shelf. For instance private label brands are compared to legacy brands when consumers shop to discern value.
A retailer using refined competitive matches can quickly and confidently adjust its prices during a promotional event, know where to increase prices in response to demand and availability and stay attractive to sensitive shoppers without undercutting margins.
Internal Portfolio Matches
Product matching is a combination of algorithmic and manual techniques that work to recognize and link identical products. This can even be done internally across your product portfolio. Retailers selling thousands or even hundreds of thousands of products know the challenge of consistently pricing items with varying levels of similarity or uniformity. If you must sell a 12oz bottle of shampoo for $3.00 based on its costs, then a 16oz bottle of the same product should not sell for $2.75, even if that aligns with the competition.
Establishing a process for internal portfolio matching helps to eliminate inefficiencies caused by duplicated or misaligned product data. Instead of discovering discrepancies and having to fire-fight them one by one, an internal portfolio matching feature can help teams preempt this issue.
Leveraging AI for Enhanced Match Rates
As product SKUs proliferate and new sellers seem to enter the market at lightning speed, scaling is essential without hiring dozens more pricing experts. That’s where AI comes in. Not only can AI do the job of dozens of experts, but it also does it in a fraction of the time and at an improved match accuracy rate.
DataWeave’s AI-powered pricing intelligence and price monitoring offerings help retailers uncover gaps and opportunities to stay competitive in the dynamic world of e-commerce. It can gather competitive data from across the market and accurately match competitor products with internal catalogs. It can also internally match your product portfolio, identifying product family trees and setting tolerances to avoid pricing mismatches. The AI synthesizes all this data and links products into a usable format. Teams can easily access reports and dashboards to get their questions answered without manually attempting to refine the data first.
From Refinement to Business Value
Refined competitive price data is your team’s foundation to execute these essential pricing functions: price management, price reporting, and competitive intelligence.
Price Management
Refined data is the core of accurate price management and product portfolio optimization. Imagine you’re an electronics seller offering a range of laptops and personal computing devices marketed toward college students. Without refined competitive data, you might fail to account for pricing differences based on regionality for similar products. Demand might be greater in one city than in another. By monitoring your competition, you can match your forecasted demand assumptions with competitor pricing trends to better manage your prices and even offer a greater assortment where there is more demand.
Price Reporting
Leadership is always looking for new and better market positioning opportunities. This often revolves around how products are priced, whether you’re making a profit, and where. To effectively communicate across departments and with leadership, pricing teams need a convenient way to report on pricing and make changes or updates as new ad hoc requests come through. Spending hours constructing a report on static data will feel like a waste when the C-Suite asks for it again next week but with current metrics. Refined, constantly updated price data nips this problem in the bud.
Competitive Intelligence
Unrefined data can’t be used to discover competitive intelligence accurately. You might miss a new player, fail to account for a new competitive product line, or be unable to extract insights quickly enough to be helpful. This can lead to missed opportunities and misinformed strategies. As a seller, your competitive intelligence should be able to fuel predictive scenario modeling. For example, you should be able to anticipate competitor price changes based on seasonal trends. Your outputs will be wrong without the correct inputs.
Implementation Framework
As a pricing leader, you can take these steps to begin evaluating your current process and improve your strategy.
Assess your current data quality: Determine whether your team is aggregating data across the entire competitive landscape. Ask yourself if all attributes, features, regionality, and other metrics are captured in a single usable format for your analysts to leverage.
Setting refinement objectives: If your competitive data isn’t refined, what are your objectives? Do you want to be able to match similar products or product families within your product portfolio?
Measuring success through KPIs: Establish a set of KPIs to keep you on track. Measure things like match rate accuracy, how quickly you can react to price changes, assortment overlaps, and price parity.
Building cross-functional alignment: Create dashboards and establish methods to build ad hoc reports for external departments. Start the conversation with data to build trust across teams and improve the business.
What’s Next?
The time is now to start evaluating your current data refinement process to improve your ability to capture and leverage competitive intelligence. Work with a specialized partner like DataWeave to refine your competitive pricing data using AI and dedicated human-in-the-loop support.
Want help getting started refining your data fast? Talk to us to get a demo today!
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.
At DataWeave, data security isn’t just about compliance—it’s about enabling peace of mind and better decision-making for our customers. Our customers rely on us not just for competitive and market intelligence but also for the seamless integration of critical data sources into their decision-making frameworks. To achieve this, we have built a security-first infrastructure that ensures organizations can confidently leverage both external and internal data without compromising privacy or protection.
Secure Data Integration: The Foundation of Smarter Decisions
Effective decision-making in today’s digital commerce landscape depends on combining multiple data sources—including first-party customer data, pricing intelligence, and business rules—into a unified framework. However, without the right security measures in place, businesses often struggle to operationalize this data effectively.
At DataWeave, we eliminate this challenge by offering:
Integration with Leading Data Storage Solutions: Our platform seamlessly connects with data lakes and warehouses like AWS S3 and Snowflake, ensuring that businesses can easily ingest and analyze our data in real time.
Support for Sandboxed Environments & Data Clean Rooms: Organizations can securely merge internal and external datasets without compromising confidentiality, unlocking deeper insights for pricing and business strategies.
Automated Data Ingestion & Management: We simplify the process of integrating first-party data alongside competitive insights, allowing customers to focus on execution rather than infrastructure management.
Our Purpose-Built Security Framework
Handling millions of data points daily demands a security framework that is not only robust but also scalable and adaptable to evolving threats. DataWeave’s multi-tenant architecture ensures seamless data security without compromising operational efficiency.
Multi-Tenant Architecture: Our system allows multiple customers to share the same application infrastructure while maintaining complete data isolation and security.
Tenants share infrastructure and computing resources but remain logically isolated.
Application-level controls ensure privacy while maximizing cost efficiency.
Centralized updates, maintenance, and easy scalability for new tenants.
End-to-End Encryption & Access Controls: Every piece of data is encrypted both in transit and at rest. Role-based access controls (RBAC) restrict visibility to only authorized personnel, ensuring minimal risk of unauthorized data access.
Active Monitoring & Automated Compliance Management: We leverage automated access controls that adjust permissions dynamically as organizational roles evolve, ensuring that compliance is continuously maintained.
Certifications That Inspire Confidence
Data security is at the core of everything we do. Our compliance with the highest industry standards ensures that businesses can trust us with their sensitive data.
SOC 2 Type II Certification: DataWeave’s SOC 2 compliance is a testament to our commitment to stringent security protocols. This certification guarantees that we adhere to strict standards in data protection, availability, and confidentiality.
We implement a phased approach to security improvement:
Prioritizing Critical Systems: To maximize impact, we prioritized systems that had the highest data security relevance and expanded the coverage thereafter. By addressing these priority areas, we were able to make meaningful security improvements early in the process.
Automating Monitoring and Compliance: Partnering with Sprinto streamlined the compliance journey by automating key processes. This included real-time monitoring of our cloud environments, automated generation of audit-ready evidence, and integration with critical systems like AWS, Bitbucket, and Jira. These enhancements ensured efficient management of compliance requirements while reducing the burden on our teams.
Fostering a Culture of Shared Responsibility: We conducted organization-wide training sessions to embed compliance as a shared responsibility across all teams. By educating employees on the importance of security practices and providing them with the tools to manage compliance autonomously, we established a security-first mindset throughout the company.
This systematic method allowed us to deliver immediate improvements while aligning long-term practices with industry’s best standards.
What This Means for Our Customers
By combining robust security with seamless data integration, DataWeave empowers businesses to:
Optimize Price Management & Modelling: With secure access to real-time data, organizations can make informed pricing decisions that enhance profitability and market competitiveness.
Run Advanced Simulations & Testing: Reliable, secure data enables businesses to model various pricing and assortment strategies before implementation, reducing risks and maximizing returns.
Uncompromised Data Security: SOC 2 Type II compliance ensures stringent protocols to protect your data at every stage.
Simplified Vendor Processes: Verified security certifications reduce friction during due diligence and onboarding, making it easier to partner with us.
Aligned Standards: Our adherence to industry benchmarks reflects our commitment to meeting your expectations as a trusted technology partner.
Scalable Operations: Expand across regions while maintaining full confidence in data privacy and security.
Secure Collaboration: Share insights across teams with tools designed to protect sensitive information.
Our customers are increasingly looking to integrate their internal datasets with the external competitive intelligence provided by DataWeave. This can be a complex and risky process without the right security measures in place. We remove these roadblocks by providing a secure, scalable infrastructure designed to help businesses unify data without security concerns.
By ensuring seamless compatibility with key data storage platforms, such as Snowflake and AWS S3, we enable organizations to consolidate valuable first-party data with timely market insights. This integration empowers businesses to refine their pricing, assortment, and digital shelf strategies, thereby driving superior customer experiences—without the headaches of data security risks.
Security remains a top priority in everything we do. Our SOC 2 Type II-certified framework enforces rigorous encryption, access controls, and real-time compliance monitoring. We take on the burden of data security so our customers can focus on innovation and growth.
With DataWeave, businesses can confidently leverage secure data-driven decision-making to unlock new opportunities, optimize operations, and scale without compromise.
In online retail, success hinges on more than just offering quality products at competitive prices. As eCommerce catalogs expand and consumer expectations soar, businesses face an increasingly complex challenge: How do you effectively organize, categorize, and present your vast product assortments in a way that enhances discoverability and drives sales?
Having complete and correct product catalog data is key. Effective product attribute tagging—a crucial yet frequently undervalued capability—helps in achieving this accuracy and completeness in product catalog data. While traditional methods of tagging product attributes have long struggled with issues of scalability, consistency, accuracy, and speed, a new thinking and fundamentally new ways of addressing these challenges are getting established. These follow from the revolution brought in Large Language Models but they fashion themselves as Small Language Models (SLM) or more precisely as Domain Specific Language Models. These can be potentially considered foundational models as they solve a wide variety of downstream tasks albeit within specific domains. They are a lot more efficient and do a much better job in those tasks compared to an LLM. .
Retail Domain Language Models (RLMs) have the potential to transform the eCommerce customer journey. As always, it’s never a binary choice. In fact, LLMs can be a great starting point since they provide an enhanced semantic understanding of the world at large: they can be used to mine structured information (e.g., product attributes and values) out of unstructured data (e.g., product descriptions), create baseline domain knowledge (e.g, manufacturer-brand mappings), augment information (e.g., image to prompt), and create first cut training datasets.
Powered by cutting-edge Generative AI and RLMs, next-generation attribute tagging solutions are transforming how online retailers manage their product catalog data, optimize their assortment, and deliver superior shopping experiences. As a new paradigm in search emerges – based more on intent and outcome, powered by natural language queries and GenAI based Search Agents – the capability to create complete catalog information and rich semantics becomes increasingly critical.
In this post, we’ll explore the crucial role of attribute tagging in eCommerce, delve into the limitations of conventional tagging methods, and unveil how DataWeave’s innovative AI-driven approach is helping businesses stay ahead in the competitive digital marketplace.
Why Product Attribute Tagging is Important in eCommerce
As the eCommerce landscape continues to evolve, the importance of attribute tagging will only grow, making it a pertinent focus for forward-thinking online retailers. By investing in robust attribute tagging systems, businesses can gain a competitive edge through improved product comparisons, more accurate matching, understanding intent, and enhanced customer search experiences.
Taxonomy Comparison and Assortment Gap Analysis
Products are categorized and organized differently on different retail websites. Comparing taxonomies helps in understanding focus categories and potential gaps in assortment breadth in relation to one’s competitors: missing product categories, sizes, variants or brands. It also gives insights into the navigation patterns and information architecture of one’s competitors. This can help in making search and navigation experience more efficient by fine tuning product descriptions to include more attributes and/or adding additional relevant filters to category listing pages.
For instance, check out the different Backpack categories on Amazon and Staples in the images below.
Or look at the nomenclature of categories for “Pens” on Amazon (left side of the image) and Staples (right side of the image) in the image below.
Assortment Depth Analysis
Another big challenge in eCommerce is the lack of standardization in retailer taxonomy. This inconsistency makes it difficult to compare the depth of product assortments across different platforms effectively. For instance, to categorize smartphones,
Retailer A might organize it under “Electronics > Mobile Phones > Smartphones”
Retailer B could use “Technology > Phones & Accessories > Cell Phones”
Retailer C might opt for “Consumer Electronics > Smartphones & Tablets”
Inconsistent nomenclature and grouping create a significant hurdle for businesses trying to gain a competitive edge through assortment analysis. The challenge is exacerbated if you want to do an in-depth assortment depth analysis for one or more product attributes. For instance, look at the image below to get an idea of the several attribute variations for “Desks” on Amazon and Staples.
Custom categorization through attribute tagging is essential for conducting granular assortment comparisons, allowing companies to accurately assess their product offerings against those of competitors.
Enhancing Product Matching Capabilities
Accurate product matching across different websites is fundamental for competitive pricing intelligence, especially when matching similar and substitute products. Attribute tagging and extraction play a crucial role in this process by narrowing down potential matches more effectively, enabling matching for both exact and similar products, and tagging attributes such as brand, model, color, size, and technical specifications.
For instance, when choosing to match similar products in the Sofa category for 2-3 seater sofas from Wayfair and Overstock, tagging attributes like brand, color, size, and more is a must for accurate comparisons.
Taking a granular approach not only improves pricing strategies but also helps identify gaps in product offerings and opportunities for expansion.
Fix Content Gaps and improve Product Detail Page (PDP) Content
Attribute tagging plays a vital role in enhancing PDP content by ensuring adherence to brand integrity standards and content compliance guidelines across retail platforms. Tagging attributes allows for benchmarking against competitor content, identifying catalog gaps, and enriching listings with precise details.
This strategic tagging process can highlight missing or incomplete information, enabling targeted optimizations or even complete rewrites of PDP content to improve discoverability and drive conversions. With accurate attribute tagging, businesses can ensure each product page is fully optimized to capture consumer attention and meet retail standards.
Elevating the Search Experience
In today’s online retail marketplace, a superior search experience can be the difference between a sale and a lost customer. Through in-depth attribute tagging, vendors can enable more accurate filtering to improve search result relevance and facilitate easier product discovery for consumers.
By integrating rich product attributes extracted by AI into an in-house search platform, retailers can empower customers with refined and user-friendly search functionality. Enhanced search capabilities not only boost customer satisfaction but also increase the likelihood of conversions by helping shoppers find exactly what they’re looking for more quickly and with minimal effort.
Pitfalls of Conventional Product Tagging Methods
Traditional methods of attribute tagging, such as manual and rule-based systems, have been significantly enhanced by the advent of machine learning. While these approaches may have sufficed in the past, they are increasingly proving inadequate in the face of today’s dynamic and expansive online marketplaces.
Scalability
As eCommerce catalogs expand to include thousands or even millions of products, the limitations of machine learning and rule-based tagging become glaringly apparent. As new product categories emerge, these systems struggle to keep pace, often requiring extensive revisions to existing tagging structures.
Inconsistencies and Errors
Not only is reliance on an entirely human-driven tagging process expensive, but it also introduces a significant margin for error. While machine learning can automate the tagging process, it’s not without its limitations. Errors can occur, particularly when dealing with large and diverse product catalogs.
As inventories grow more complex to handle diverse product ranges, the likelihood of conflicting or erroneous rules increases. These inconsistencies can result in poor search functionality, inaccurate product matching, and ultimately, a frustrating experience for customers, drawing away the benefits of tagging in the first place.
Speed
When product information changes or new attributes need to be added, manually updating tags across a large catalog is a time-consuming process. Slow tagging processes make it difficult for businesses to quickly adapt to emerging market trends causing significant delays in listing new products, potentially missing crucial market opportunities.
How DataWeave’s Advanced AI Capabilities Revolutionize Product Tagging
Advanced solutions leveraging RLMs and Generative AI offer promising alternatives capable of overcoming these challenges and unlocking new levels of efficiency and accuracy in product tagging.
DataWeave automates product tagging to address many of the pitfalls of other conventional methods. We offer a powerful suite of capabilities that empower businesses to take their product tagging to new heights of accuracy and scalability with our unparalleled expertise.
Our sophisticated AI system brings an advanced level of intelligence to the tagging process.
RLMs for Enhanced Semantic Understanding
Semantic Understanding of Product Descriptions
RLMs analyze the meaning and context of product descriptions rather than relying on keyword matching. Example: “Smartphone with a 6.5-inch display” and “Phone with a 6.5-inch screen” are semantically similar, though phrased differently.
Attribute Extraction
RLMs can identify important product attributes (e.g., brand, size, color, model) even from noisy or unstructured data. Example: Extracting “Apple” as a brand, “128GB” as storage, and “Pink” as the color from a mixed description.
Identifying Implicit Relationships
RLMs find implicit relationships between products that traditional rule-based systems miss. Example: Recognizing that “iPhone 12 Pro” and “Apple iPhone 12” are part of the same product family.
Synonym Recognition in Product Descriptions
Synonym Matching with Context
RLMs identify when different words or phrases describe the same product. Examples: “Sneakers” = “Running Shoes”, “Memory” = “RAM” (in electronics) Even subtle differences in wording, like “rose gold” vs “pink” are interpreted correctly.
Overcoming Brand-Specific Terminology
Some brands use their own terminologies (e.g., “Retina Display” for Apple). RLMs can map proprietary terms to more generic ones (e.g., Retina Display = High-Resolution Display).
Dealing with Ambiguities
RLMs analyze surrounding text to resolve ambiguities in product descriptions. Example: Resolving “charger” to mean a “phone charger” when matched with mobile phones.
Contextual Understanding for Improved Accuracy and Precision
By leveraging advanced natural language processing (NLP), DataWeave’s AI can process and understand the context of lengthy product descriptions and customer reviews, minimizing errors that often arise at human touch points. The solution processes and interprets information to extract key information to dramatically improve the overall accuracy of product tags.
It excels at grasping the subtle differences between similar products, sizes, colors and identifying and tagging minute differences between items, ensuring that each product is uniquely and accurately represented in a retailer’s catalog.
This has a major impact on product and similarity-based matching that can even help optimize similar and substitute product matching to enhance consumer search. At the same time, our AI can understand that the same term might have different meanings in various product categories, adapting its tagging approach based on the specific context of each item.
This deep comprehension ensures that even nuanced product attributes are accurately captured and tagged for easy discoverability by consumers.
Case Study: Niche Jewelry Attributes
DataWeave’s advanced AI can assist in labeling the subtle attributes of jewelry by analyzing product images and generating prompts to describe the image. In this example, our AI identifies the unique shapes and materials of each item in the prompts.
The RLM can then extract key attributes from the prompt to generate tags. This assists in accurate product matching for searches as well as enhanced product recommendations based on similarities.
This multi-model approach provides the flexibility to adapt as product catalogs expand while remaining consistent with tagging to yield more robust results for consumers.
Unparalleled Scalability
DataWeave can rapidly scale tagging for new categories. The solution is built to handle the demands of even the largest eCommerce catalogs enabling:
Effortless management of extensive product catalogs: We can process and tag millions of products without compromising on speed or accuracy, allowing businesses to scale without limitations.
Automated bulk tagging: New product lines or entire categories can be tagged automatically, significantly reducing the time and resources required for catalog expansion.
Normalizing Size and Color in Fashion
Style, color, and size are the core attributes in the fashion and apparel categories. Style attributes, which include design, appearance, and overall aesthetics, can be highly specific to individual product categories.
Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification. By leveraging advanced technology to identify and normalize identical and similar products from competitors, you can optimize your pricing strategy and product assortment to remain competitive. Using Generative AI in normalizing color and size in fashion is key to powering competitive pricing intelligence at DataWeave.
Continuous Adaptation and Learning
Our solution evolves with your business, improving continuously through feedback and customization for retailers’ specific product categories. The system can be fine-tuned to understand and apply specialized tagging for niche or industry-specific product categories. This ensures that tags remain relevant and accurate across diverse catalogs and as trends emerge.
The AI in our platform also continuously learns from user interactions and feedback, refining its tagging algorithms to improve accuracy over time.
Stay Ahead of the Competition With Accurate Attribute Tagging
In the current landscape, the ability to accurately and consistently tag product attributes is no longer a luxury—it’s essential for staying competitive. With advancements in Generative AI, companies like DataWeave are revolutionizing the way product tagging is handled, ensuring that every item in a retailer’s catalog is presented with precision and depth. As shoppers demand a more intuitive, seamless experience, next-generation tagging solutions are empowering businesses to meet these expectations head-on.
DataWeave’s innovative approach to attribute tagging is more than just a technical improvement; it’s a strategic advantage in an increasingly competitive market. By leveraging AI to scale and automate tagging processes, online retailers can keep pace with expansive product assortments, manage content more effectively, and adapt swiftly to changes in consumer behavior. In doing so, they can maintain a competitive edge.
When egg prices surged 70% during the 2023 avian flu outbreak, grocery retailers faced a critical dilemma: maintain margins and risk losing customers, or absorb costs and watch profits evaporate. Similarly, rising olive oil and chocolate prices also had domino effects, cascading down from retailers to consumers. In each of these scenarios, those with sophisticated pricing intelligence systems adapted swiftly, finding the sweet spot between competitiveness and profitability. Others weren’t so fortunate.
This scenario continues to play out daily across thousands of products in the grocery sector. From breakfast cereals to fresh produce to bottled water, retailers must orchestrate pricing across a variety of categories – each with its own competitive dynamics, margin requirements, and price sensitivity patterns.
The Evolution of Grocery Pricing Intelligence
Imagine these scenarios in the grocery industry:
Milk prices spike during a supply shortage.
Your competitor drops egg prices by 20%.
Fresh produce costs fluctuate with an unseasonable frost.
For grocery retailers, these aren’t occasional challenges—they’re Tuesday. Reacting to each pricing crisis as it comes isn’t just exhausting—it’s a recipe for shrinking margins and missed opportunities.
Think of it this way: If you’re constantly playing defense with your pricing strategy, you’re already two steps behind. Commoditized items like milk and eggs face intense price competition, while seasonal products and fresh produce demand constant attention. Simply matching competitor prices or adjusting for cost changes isn’t enough anymore. What’s needed is a proactive approach that anticipates market shifts before they happen and turns pricing challenges into competitive advantages. This is where price management comes in.
Price management has transformed from simple competitor checks into a strategic power play that can make or break a retailer’s market position. Weekly manual adjustments have given way to a long-term strategic view, driven by data analytics and market intelligence. Here are the basics of how price management in grocery retail works today.
Three Pillars of Grocery Price Management
1. Smart Data Collection: Building Your Foundation
The journey begins with comprehensive data collection and storage across your entire product ecosystem. This means:
Complete Coverage Of All SKUs Across All Stores: Tracking prices for all SKUs across all stores, with particular attention to high-velocity items and volatile categories.
Dynamic Monitoring: Tracking prices across different time frequencies as grocery prices are highly volatile for different categories. So daily tracking for volatile items like dairy and produce, and weekly for more stable categories may be needed.
Competitive Intelligence: Gathering data not just on prices, but on promotions, pack sizes, and private label alternatives.
Infrastructure to Support Large Volumes of Data: Partnering with external data and analytics providers to bridge the gap when retailers struggle with the scale of digital infrastructure these data sets require.
2. Intelligent Data Refinement: Making Sense of the Numbers
Raw data alone isn’t enough—it needs context and structure to become actionable intelligence. This is called Data Refinement—the process of establishing meaningful relationships within the data to facilitate the extraction of valuable insights. This refinement stage is closely tied to the data collection strategy, as the quality and depth of the insights derived depend on the accuracy and coverage of the collected data.
Data refinement includes several key processes:
Advanced Product Matching
Picture this: You’re tracking a competitor’s pricing on organic apples. Simple, right? Not quite. Yes, Universal Product Codes (UPCs) and Price Lookup Codes (PLUs) are present in Grocery to standardize product identification across different retailers—unlike the fashion industry’s endless style variations. Still, product matching isn’t as straightforward as scanning barcodes.
Here’s the catch: many retailer websites don’t display them. Then there’s the private label puzzle—your “Store’s Best” organic apples need to match against competitors’ house brands, each with their own unique UPC. Throw in different sizes (4 Apples vs. 1Kg of Apples), regional product names (fancy naming for plain old arugula), and international brand variations (like the name for Sprite in the USA and China), and you’ve got yourself a complex matching challenge that would make conventional pricing intelligence providers sweat.
Custom Product Relationships for Consistent Pricing and Competitive Positioning
Think like a shopper browsing the dairy aisle. You regularly buy your family’s favorite organic yogurt, the 24oz tub. But today, you notice the larger 32oz size is on sale – except the 24oz isn’t. As you stand there, confused, you wonder: Is the sale only for the bigger size? Did I miss a promotion? Should I buy the 32oz even though it’s more than I need?
For shoppers, this inconsistent pricing across product variations creates a frustrating experience. Establishing clear relationships between related items in your catalog is essential for maintaining consistent pricing and a coherent competitive strategy.
Start by linking products based on attributes like size, brand, and packaging. That way, when you adjust the price of the 32oz yogurt, the 24oz version automatically updates too – no more scrambling to ensure uniform pricing across your assortment. Similarly, products of the same brand but with flavor variations should be connected to keep pricing consistent.
Taking this one step further, mapping your competitors’ exact and similar products is crucial for comprehensive competitive intelligence. Distinguishing between premium and private label tiers, national brands, and regional players gives you a holistic view of the landscape. With this understanding, you can hone your pricing strategies to maintain a clear, compelling position across your entire category lineup.
Consistent pricing, whether across your own product variations or against competitors, provides clarity and accuracy in your overall competitive positioning. By establishing these logical connections, you avoid the customer confusion of seemingly random, inconsistent discounts – and ensure your pricing strategies work in harmony, not disarray.
The Role of AI and Data Sciences in Data Refinement
On the surface, linking products based on attributes like size, brand, and packaging seems like a no-brainer. But developing and maintaining the systems to accurately and automatically identify these connections? That’s a whole different animal.
Think about it – you’re not just dealing with text-based product titles and UPCs. There are images, videos, regional variations, private labels, and a whole host of other data types and industry nuances to account for.
Luckily, DataWeave is one of the few companies that’s truly cracked the code. Our multimodal AI models are trained to process all those diverse data formats – from granular product specs to zany regional produce names. And it’s not just about technology; we also harness the power of human intelligence.
See, in the grocery world, category managers are the real decision makers. They know their shelves inside and out and can spot those tricky connections in product matching, especially when they are not UPC-based. That’s why DataWeave built in a Human-in-the-Loop (HITL) process, where their AI systems continuously learn from expert feedback. It’s a feedback loop that allows our customers to pitch in and keep product relationships accurate, reliable, and always adapting to new market realities.
So while product mapping may seem straightforward on the surface, the reality is it takes some serious horsepower to do it right. Thankfully, DataWeave has both the technical chops and the grocery industry know-how to make it happen. Because when it comes to pricing intelligence, getting those product connections right is half the battle.
3. Strategic Implementation: Turning Insights into Action
The true value of pricing intelligence (PI) is realized through its strategic application. Although many view PI as a technical function, its strategic significance is increasing, particularly in the context of recent economic pressures like inflation. Here’s why:
Tactical vs Strategic Use of Data: From Standard Reporting to Competitive Analysis
Pricing intelligence has come a long way from the days of simply reacting to daily price changes. These days, it’s not just about firefighting—it’s about driving long-term strategy.
You can use pricing data to make quick, tactical adjustments, like matching a competitor’s sudden price drop on milk. Or, you can leverage that same data to predict market trends, optimize your product lineup, and shape your overall pricing strategy. Retailers who take that strategic view can get out ahead of the curve, anticipating shifts instead of just chasing them.
DataWeave supports both of these approaches. Our Standard Reporting tools give pricing managers the nitty-gritty details they need—current practices, historical patterns, and operational KPIs. It’s all the insights you’d expect for making those tactical, day-to-day tweaks.
In addition, DataWeave offers something more powerful: Competitive analysis. This is where pricing intelligence becomes a true strategic weapon. By providing a high-level view of market positioning, competitor moves, and untapped opportunities, competitive analysis empowers leadership to make proactive, big-picture decisions.
Armed with this broader perspective, retailers can start taking a more surgical approach. Maybe you need to adjust pricing zones to better meet customer demands. Or rethink your overall strategies to stay ahead of the competition, not just keep pace. It’s the difference between constantly putting out fires and systematically fortifying your entire pricing fortress.
Beyond Pricing: Comprehensive Data for Broader Insights
Pricing intelligence is just the tip of the iceberg. When you really start to refine and harness your data, the possibilities for grocery retailers expand far beyond simple price comparisons. Think about it – all that information you’re collecting on products, markets, and consumer behavior? That’s a goldmine waiting to be tapped. Sure, you can use it to keep a pulse on competitor pricing. But why stop there?
What if you could leverage that data to optimize your product assortment, making sure you’re stocking the right mix to meet customer demands? Or tap into predictive analytics to get a glimpse of future market shifts, so you can get out ahead of the curve? How about using it to streamline your supply chain, identify availability inefficiencies, and get products to shelves faster?
Sure, pricing intelligence will always be mission-critical. But when you couple it with these other data-driven insights, that’s when grocery retailing gets really interesting. It’s about evolving from a price-matching robot to a true strategic visionary, armed with the intelligence to take your business to new heights.
Looking Ahead: The Future of Grocery Pricing Intelligence
The grocery pricing landscape continues to evolve, driven by:
Integration of AI and machine learning for predictive pricing
Enhanced focus on omnichannel pricing consistency
Growing importance of personalization in pricing strategies
Pricing intelligence isn’t just about having data—it’s about having the right data and knowing how to use it strategically. Success requires a comprehensive approach that combines robust data collection, sophisticated analysis, and strategic implementation.
By embracing modern pricing intelligence tools and strategies, grocery retailers can navigate market volatility, maintain competitive positioning, and drive sustainable growth. The key lies in building a pricing ecosystem that’s both sophisticated enough to handle complex data and flexible enough to adapt to changing market conditions.
Ready to transform your pricing strategy? Check out our grocery price tracker to get month-on-month updates on grocery prices in the real world. Contact us to learn how our advanced pricing intelligence solutions can help your business stay ahead in the competitive grocery market.
Today, the first name that comes to anybody’s mind when they hear about online shopping is Amazon. In the US alone, Amazon accounted for over 37.6 percent of total online retail sales in 2023 with the second place Walmart not even managing to win double-digit numbers on the same scale.
With such a phenomenal market share, it is not surprising that any retail brand would want to have their products listed on Amazon for sale. However, as enticing as the potential exposure could be, the overwhelming presence of brands selling similar products on Amazon is so huge that getting fair visibility for your products may require some heavy-lifting support.
Will the Same SEO You Use for Google Work with Amazon?
Unfortunately, no, as Google and Amazon have different objectives when it comes to search rankings on their respective customer platforms. Google makes the lion’s share of its revenue from search advertising, whereas Amazon makes money when customers buy products listed on its platform by sellers.
Relying on traditional search engine optimization (SEO) techniques may not get the desired results as they are more optimized for search engines like Google. Amazon embraces its unique DNA when it comes to product display rankings on its search option.
How Does SEO Work in Amazon?
Over the years, Amazon amassed data about shopping experiences that billions of customers globally had on its platform. With this data, they developed their custom search algorithm named A9. Contrary to the gazillion objectives that Google has for its intelligent search algorithms, Amazon has tasked A9 with just a simple straightforward target—when a customer keys in a search query, provide the best choice of products that they will most probably purchase, as search results.
A9 works to fulfill the mission of guiding shoppers to the right product without worrying about semantics, context, intent, mind mapping, etc. of the search query in contrast to what Google does. As with Google search, Amazon does have paid advertising and sponsored results options such as Amazon PPC, Headline ads, etc. but their SEO algorithms are aware of how to support and boost search rankings of genuine products and brands that have taken an effort to follow best practices in Amazon SEO as well as have a great offering with attractive prices.
As additional knowledge, Amazon also has clear guidelines on what it prioritizes for search rankings. Known in the SEO world as Amazon ranking signals, these are core factors that influence how a product is ranked for search queries. Some of the top Amazon ranking signals that carry heavy influence on search rankings include on-page signals, off-page signals, sales rank, best sellers rank, etc.
What Brands Need to Strategize to Master the Amazon SEO Algorithms
From a broad perspective, we can classify the actions brands need to take in this regard in 3 core stages:
Pre-Optimization
This deals with getting first-hand knowledge about both customers who are likely to purchase your product and the competitors who are vying for sales from these very same customers. Filtering your target customer or audience is essential to ensure that you get the most ROI from marketing initiatives and that sales cycles are accelerated. For example, if your product is a premium scented candle, there is no point in wasting advertising dollars trying to win attention from customers who are not likely to ever spend on luxury home décor items.
Knowing how your competitors are performing on Amazon search, the keywords, and SEO strategies they have adapted is critical to ensure that you stay one step ahead.
Product Listing Page Optimization
This includes strategies that a brand can adopt so that its product description page gets the much-needed content optimizations to sync with Amazon’s A9 algorithm. It has a mix of keyword-integrated content, relevant images, descriptions in easy-to-understand language, localized content flavors to resonate with target buyers, etc. For example, a kitchen tool like a grater might be used for different kinds of food preparation techniques in different regions of the same country.
The brand must ensure that the description adequately localizes the linguistic or usage preference representation of the target audience. If the grater is used for grating coconut shells to extract the fibrous pulp in the Midlands and for grating ginger skin in the Far East, both use cases should be part of the product description if the target customers are from both regions.
Sales Optimization
This deals with options that have more sales strategies integrated into their core. For example, blogs on popular websites with the Amazon purchase link embedded in the content, collaboration with social media influencers, paid advertising on Amazon itself as well as on search engines, video ads, banner and display ads, etc.
The key intent here is to drive organic and inorganic traffic to the Amazon product listing page and ultimately win sales.
How Can Your Products Rank High in Amazon Search Results? Top 10 Tactics
Now that you have a clear understanding of the strategies that help in mastering Amazon’s ranking algorithms, here are some great tips to help achieve higher search rankings for your products on Amazon search:
1. Target Relevant Keywords
You need to figure out the best keywords that match what customers put as queries into the Amazon search bar. Your brand needs to clearly understand customer behavior when they arrive on Amazon to search for a product or category of products. The best place to begin looking for the same would be on competitor pages on Amazon. The keywords that helped them rank well on Amazon can help you as well. Manually investigating such a large pool of competitors is nearly impossible but with the right tools, you can easily embrace capabilities to know which keywords can help you in mimicking the success of your competitors.
2. Focus on Product Titles
Every single part of the content in your brand’s Amazon storefront or product page needs dedicated focus. Beginning with the product titles, effort needs to be made to ensure that they include the brand name, key product category or features, and other relevant keyword information.
In other words, product titles must be optimized for searchability. This searchability for product titles needs to be optimized for both mobile and desktop screens.
3. Create Product Descriptions that Resonate with the Audience
For product descriptions on your Amazon webpage, you need to figure out the optimal quality levels needed for the intended audience. Effective content can help achieve better search ranking visibility and convince the incoming traffic of shoppers to make a purchase. It is important to periodically review and modify your page content to suit the interests of visitors from both web and mobile devices.
Leveraging solutions like DataWeave can help with regular content audits to ensure you are putting out the best product content that will delight shoppers and deliver on sales conversion targets.
4. Use High-Quality Media Assets like Images and Videos
Promoting your product doesn’t have to be restricted to just textual content in Amazon product description sections. You can use other multimedia assets of high quality. These include images, videos, brochure images, etc. Every content asset must aim to educate shoppers on why your product should be their number one choice. For example, look at this detailed product description for the viral K-Beauty product COSRX Mucin Essence.
Moreover, images can help attract more attention span from visitors, thereby increasing the probability of purchases.
5. Strengthen the Backend Keywords As Well
Amazon also supports hidden backend keywords that sellers add to their product listings. They help add more relevance to products similar to meta descriptions and titles in traditional SEO for search engines like Google. A typical backend keyword may comprise synonyms, misspelled keywords, textual variations, etc. However, knowing how to pick the right ones is crucial. By analyzing your keyword rankings against competitors and higher-ranking product results in search, the platform can help you consistently optimize your content backend to help grow visibility.
6. Focus on Reviews and Ratings
Reviews and ratings on product pages are key insights that help customers with their purchasing decisions. So, it is natural for brands to keep a close eye on how their products are faring in this regard. Reviews and ratings are a direct indication of the trustworthiness of your product. When previous buyers rate you high and leave favorable reviews on your product, it will directly promote trust and help you secure a better rapport with new customers.
This upfront advantage can help boost sales conversions better. Leveraging solutions like DataWeave can help you understand the sentiments that customers have for your products by intelligently analyzing reviews and ratings.
7. Implement Competitive Pricing Strategies
The goal of most customers when shopping online is to get their desired product at the most affordable prices. The eCommerce price wars every year are growing in scale today and getting your product pricing right is crucial for sales. However, there is a need to gain comprehensive insights into how your competitors are pricing their offerings and how the market responds to specific price ranges. Solutions like DataWeave help your brand access specific insights into pricing. By analyzing competitor pricing, you can create a winning price model that is sustainable for your brand and favorable for target customers.
8. Track Share of Search
Content and other SEO activities will help improve your search rankings on Amazon. However, it is equally important to know how well your products are performing periodically against your competitors for the same set of specific keyword searches. You need to understand the share of search that your products are achieving to formulate improvement strategies. DataWeave’s Digital Shelf Analytics solution provides share of search insights helping you uncover deep knowledge on your discoverability on Amazon (and other marketplaces) for your vital search keywords.
9. Ensure Stock Availability
To achieve better ranking results, brands need to ensure that the relevant products matching the search keywords are available for quick delivery at the desired ZIP codes where users are more likely to search and order them. Out-of-stock items seldom show up high on search results. Certain products, especially if they’re popular, can get stocked out frequently in certain locations. Keeping a close eye on your stock availability across the map can help minimize these scenarios.
10. Optimize Your Brand Presence
While optimizing content and other key areas within the Amazon webpage for your product is critical, there are other avenues to help boost search rankings. One such option includes registering in the Amazon Brand Registry, which provides more beneficial features like protection against counterfeits and ensuring that your brand page is optimized according to Amazon storefront standards.
The Bottom Line
Winning the top spot in Amazon search ranking is crucial for brands that aim to capitalize on online sales revenue to grow their business. Knowing your workaround for Amazon’s proprietary SEO frameworks and algorithms is the first step to succeeding. The key element of success is your ability to gain granular insights into the areas we covered in this blog post such as competitor prices, sentiments of customers, market preferences, and content optimization requirements.
This is where DataWeave’s Digital Shelf Analytics solution becomes the biggest asset for your eCommerce business. Contact us to explore how we can empower your business to build the most visible and discoverable Amazon storefront that guarantees higher search rankings and ultimately increased sales. Talk to us for a demo today.
Fashion is as dynamic a market as any—and more competitive than most others. Consumer trends and customer needs are always evolving, making it challenging for fashion and apparel brands to keep up.
Despite the inherent difficulties fashion and apparel sellers face, this industry is one of the largest grossing markets in the world, estimated at $1.79 trillion in 2024. Global revenue for apparel is expected to grow at an annual rate of about 3.3% over the next four years. That means companies in this space stand to make significant revenue if they can competitively price their products, keep up with the competition, and win customer loyalty with consistent product availability.
There are three main categories in fashion and apparel. These include:
Apparel and clothing (i.e., shirts, pants, dresses, and other apparel)
Footwear (i.e., sneakers, sandals, heels, and other products)
Accessories (i.e., bags, belts, watches, and so on)
If you look at all of these product types across all sorts of retailers, there is a massive amount of overlapping data based on product attributes like style and size that are difficult to normalize.
Fashion Attributes
Style, color, and size are the main attribute categories in fashion and apparel. Style attributes include things like design, look, and overall aesthetics of the product. They’re very dependent on the actual product category of fashion as well. A shirt might have a slim fit attribute associated with it, whereas a belt might have a length. All these different attributes are usually labeled within a product listing and affect the consumer’s decision-making process:
Color (red, blue, sea green, etc.)
Pattern (solid, striped, checked, floral, etc.)
Material (cotton, polyester, leather, denim, silk, etc.)
Fit (regular, slim, relaxed, oversized, tailored, etc.)
Type (casual, formal, sporty, vintage, streetwear)
Color Complexity in Fashion
Color is perhaps the most visually distinctive attribute in fashion, yet it presents unique challenges for retailers. This is because color naming can vary across retailers and marketplaces. There are several major differences in color convention:
A single color can be labeled differently across brands (e.g., “navy,” “midnight blue,” “deep blue”)
Seasonal color names (e.g., “summer sage” vs. “forest green”)
Marketing-driven names (e.g., “sunset coral” vs. “pale orange”)
Size: The Other Critical Dimension
Size in fashion refers to the dimensions or measurements that determine how fashion products fit. Depending on whether the product is a clothing item, shoes, or a hat, there will be different sizing options. Types of sizes include:
Standard sizes (XS, S, M, L, XL, XXL, XXL)
Custom sizes (based on brand, retailer, country, etc.)
A single type of product may have different sizing labels. For instance, one pants listing may use traditional S, M, L, XL sizing, while another pants listing may use 24, 25, or 26, to refer to the waist measurement.
Size is a dynamic attribute that changes based on current trends. For example, there has recently been a significant shift towards inclusive sizing. Size inclusivity refers to the practice of selling apparel in a wide range of sizes to accommodate people of all body types. Consumers are more aware of this trend and are demanding a broader range of sizing offerings from the brands they shop from.
In the US market, in particular, some 67% of American women wear a size 14 or above and may be interested in purchasing plus-size clothing. There is a growing demand in the plus-size market for more options and a wider selection. Many brands are considering expanding their sizes to accommodate more shoppers and tap into this growing revenue channel.
Pricing Based on Size and Color
Many fashion products are priced differently based on size and color. Let’s take a look at an example of what this can look like.
A popular beauty brand (see image) is known for its viral lip tint. While most of the color variants are priced at $9.90 on Amazon, a specific colorway option, featuring less pigmented options, is priced at $9.57. This price differential is driven by both material costs and market demand.
Different colorways (any of a range of combinations of colors in which a style or design is available) of the same product often command different prices also. This is based on:
Dye costs (some colors require more expensive processes)
Seasonal demand (traditional colors vs. trend colors)
Exclusivity (limited edition colors)
An example of price variations by size is a women’s shirt that is being sold on Amazon as shown below. For this product, there are no style attributes to choose from. The only parameter the shopper has to select is the size they’d like to purchase. They can choose from S to XL. On the top, we can see that the product in size S is ₹389. Below, the size XL version of this same shirt is ₹399. This price increase is correlated to the change in size.
So why are these same products priced differently? In an analysis of One Six, a plus-size clothing brand, several reasons for this difference in plus-size clothing were determined.
Extra material is needed, hence an increase in production costs
Extra stitching costs, hence an increase in production costs
Production of plus-size clothing often means acquiring specialized machinery
Smaller scale production runs for plus-size clothing means these initiatives often don’t benefit from cost savings
Some sizes are sold more than others, meaning that in-demand sizes for certain apparel can affect pricing as well. Brands want to be able to charge as much as possible for their listing without risking losing a sale to a competitor.
The Competitive Pricing Challenge: Normalizing Product Attributes Across Competitors in Apparel and Fashion
There are hundreds of possible attribute permutations for every single apparel product. Some retailers may only sell core sizes and basic colors; some may sell a mix of sizes for multiple style types. Most retailers also sell multiple color variants for all styles they have on catalog. Other retailers may only sell a single, in-demand size of the product. Also, when other retailers are selling the product, it’s unlikely that their naming conventions, color options, style options, and sizing match yours one-for-one.
In one analysis, it was found that there were 800+ unique values for heel sizes and 1000+ unique values for shirts and tops at a single retailer! If you’re looking to compare prices, the effort involved in setting up and managing lookup tables to identify discrepancies when one retailer uses European sizes and another uses USA sizes, for example, is simply too onerous to contemplate doing. Colors only add to the complexity – as similar colors may have new names in different regions and locations as well!
Even if you managed to find all the discrepancies between product attributes, you would still need to update them any time a competitor changed a convention.
Still, monitoring your competitors and strategically pricing your listings is essential to maintain and grow market share. So what do you do? You can’t simply eyeball your competitor’s website to check their pricing and naming conventions. Instead, you need advanced algorithms to scan the entire marketplace, identify individual products being sold, and normalize their data and attributes for analysis.
Getting Color and Size Level Pricing Intelligence
With DataWeave, size and color are just two of several dimensions of a product instead of an impossible big data problem for teams. Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification.
This works by using AI built on more than 10 years of product catalog data across thousands of retail websites. It matches common identifiers, like UPC, SKU code, and other attributes for harmonization before employing a large language model (LLM) prompts to normalize color variations and sizing to a single standard.
For example, if a competitor has the smallest size listed as Sm but has your smallest listing identified as S, DataWeave can match those two attributes using AI. Similar classification can be performed on color as well.
Complex LLM prompts are pre-established so that this process is fast and efficient, taking minutes rather than weeks of manual effort.
Harmonizing products along with their color and sizing data across different retailers for further analysis has several benefits. Most importantly, product matching helps teams conduct better competitive analysis, allowing them to stay informed about market trends, competitors’ offerings, and how those competitors are pricing various permutations of the same product. It helps ensure that you’re offering the most competitive assortment of sizing in several colors to win more market share as well. Overall, it’s easier for teams to gain insights and exploit their findings when all the data is clean and available at their fingertips.
Product Matching Size and Color in Apparel and Fashion
Color and size are crucial attributes for retailers and brands in the apparel and fashion industry. It adds a level of complexity that can’t be overstated. While it’s a necessity to win consumers (more colors and sizes will mean a wider potential reach), the more permutations you add to your listing, the more complicated it will be to track it against your competition. However, This challenge is worth undertaking as long as you have the right solutions at your disposal.
With a strategy backed by advanced technology to discover identical and similar products across the competitive landscape and normalize their color and sizing attributes, you can ensure that you are competitively pricing your products and offering the best assortment possible. Employing DataWeave’s AI technology to find competitor listings, match products across variants, and track pricing regularly is the way to go.
Interested in learning more about DataWeave? Click here to get in touch!
Fuel retailers today operate in a highly competitive and volatile market. Consumer behavior is increasingly driven by price sensitivity, particularly in industries like fuel where small changes in price can significantly influence where consumers choose to fill up. The stakes are even higher when you consider the razor-thin margins many fuel retailers work with, making every cent count.
For years, retailers have relied on third-party apps and services to provide them with location-based competitive fuel price data. These services collect pricing data based on customer transactions. While these platforms offer a convenient way for consumers to find cheaper fuel prices, their value to retailers is limited. The data they provide is often riddled with inaccuracies, lags, and incomplete coverage, leaving retailers vulnerable to missed pricing opportunities.
In this rapidly shifting landscape, retailers need data that is not only accurate but also real-time. Solving this involves directly tapping into retailers’ own data sources (first-party or 1P data) —such as websites and apps. This is believed to be the most comprehensive and reliable source of fuel price data in the market.
To validate this hypothesis, we conducted a comprehensive analysis comparing first-party and third-party (3P) fuel price data. Our analysis compared pricing (at the same time of the day) across more than 40 gas stations—including major players like Circle K, Costco, Speedway, and Wawa. The data was captured several times a day for over a week.
Accurate Pricing Matters More Than Ever
Our analysis revealed that nearly a quarter (24.4%) of the fuel pricing data provided by third-party sources was inaccurate when compared to first-party data. On average, these inaccuracies amounted to a price difference of 10.9%.
Such discrepancies, though seemingly minor, can significantly affect consumer behavior. Inaccurate prices could drive customers to competitors who are listed with lower prices—even if the real difference is negligible. For fuel retailers, this leads to lost revenue, missed opportunities, and reduced market share.
The implications are clear: relying on third-party competitive data alone puts retailers at risk. With inaccurate data, retailers may fail to adjust their prices in time to respond to market changes, losing customers to competitors.
The Core Challenges of Third-Party Data
Third-party data comes with inherent limitations. The way this data is collected presents significant challenges for fuel retailers looking to optimize pricing strategies. Here are the main issues:
Inconsistent Data Frequency: Third-party pricing data is often gathered through customer card transactions. As a result, pricing data updates only when and where transactions occur. This can lead to irregular data availability, particularly in stations with lower transaction volumes. For instance, in rural areas or during off-peak hours, fewer transactions lead to fewer updates. Retailers are left with outdated data, making it difficult to keep pace with real-time price fluctuations.
Limited Geographic Coverage: Regions with lower transaction volumes are particularly affected by data gaps. While urban centers may enjoy more frequent updates, rural and less-frequented stations often suffer from a lack of data. This limited geographic coverage creates blind spots, making it impossible for retailers in these regions to stay competitive.
Potential Data Inaccuracies Across Fuel Types: Our analysis showed that inaccuracies in third-party pricing data were most pronounced for Unleaded fuel, with errors occurring nearly 80% of the time. While Diesel prices fared slightly better, inaccuracies were still frequent. This inconsistency across fuel types further complicates the challenge for retailers relying on third-party data.
Leveraging First-Party Data
At DataWeave, our Fuel Pricing Intelligence solution leverages real-time 1P data directly from fuel retailers’ websites and mobile apps, ensuring that retailers always have access to the most up-to-the-minute and accurate pricing information.
Here’s why first-party data stands out:
Real-Time Updates: Our solution provides near-instantaneous updates across more than 30,000 ZIP codes, ensuring that retailers always have the most up-to-date pricing information. This real-time accuracy is essential for making dynamic pricing adjustments in a highly competitive market.
Wide Geographic Coverage: DataWeave’s first-party solution captures data across a broad geographic range, ensuring no blind spots in coverage. Retailers in rural or less-frequented areas benefit from the same level of insight as their urban counterparts, giving them the ability to optimize pricing in real-time.
Complementary to Existing Solutions: For retailers already using third-party data, DataWeave’s first-party solution can complement and enhance their current systems. By filling in data gaps and providing more frequent updates, our solution ensures that retailers are never left in the dark when it comes to competitive pricing.
Retailer-Wise Variances
Among the retailers analyzed, we found that some were more affected by third-party data inaccuracies than others. Speedway and Wawa, for instance, experienced inaccuracies in up to 28% of third-party price data. In contrast, Circle K exhibited fewer discrepancies, but even they were not immune to the challenges posed by third-party data.
For their competition, relying on third-party data alone presents a significant risk. By switching to first-party data sources, or complementing their existing third-party data with DataWeave’s first-party solution, retailers can ensure they stay competitive in the eyes of price-sensitive consumers.
In an industry as price-sensitive as fuel retail, accurate data is a strategic asset. Leveraging first-party data allows fuel retailers to:
Maximize Revenue: By using real-time, accurate data, retailers can avoid under- or over-pricing their fuel, ensuring they capitalize on high-demand periods while minimizing losses during low-demand times.
Enhance Margins: First-party data provides the precision needed to fine-tune margins, ensuring profitability even in fiercely competitive markets.
Boost Customer Retention: Competitive pricing fosters customer loyalty. With better data, retailers can maintain customer trust and retention, even during volatile market shifts.
Shift into High Gear with DataWeave
As the fuel retail industry becomes increasingly competitive, the need for accurate, real-time pricing data has never been more important. DataWeave’s Fuel Pricing Intelligence solution empowers retailers with the insights they need to stay ahead of the competition, optimize pricing strategies, and boost profitability.
With first-party data, fuel retailers can eliminate the blind spots and inaccuracies associated with third-party sources. This shift toward data-driven pricing strategies ensures that every price adjustment is backed by real-time insights, giving retailers the edge they need to succeed.
Brands are investing millions of dollars in digital retail media to make their products stand out amid unrelenting competition.
The ad spend on digital retail media worldwide was estimated at USD 114.4 billion in 2022, and the current projections indicate that it will grow to USD 176 billion by 2028. This amounts to a 54% increase in just six years.
The current surge in digital retail media advertising has led brands to find an effective way to monitor the efficacy of their ad spend. While Share of Search has long been used to measure brand visibility effectively, the metrics often missed tracking ads on retail sites.
DataWeave’s Share of Media solution helps solve this problem.
What is the Share of Media?
At DataWeave, Share of Media is a metric used to measure a brand’s presence in sponsored listings and banner ads on eCommerce platforms. It captures how often a brand appears in paid promotions compared to competitors, offering insights into advertising visibility and effectiveness.
These days most marketplaces seamlessly blend banner ads and sponsored listings into organic search results. Let’s take a closer look.
Banner Advertising
Banner advertising strategically places creative banners across websites—often at the top, bottom, or sides. Some eCommerce platforms also integrate these banners into product search listings.
What makes banner ads so special is the unique ability to allow marketers to use various types of media in a single ad, such as images, auto-play videos, and animations. Brands can also present curated collections of products. This flexibility provides marketers with creative opportunities to differentiate from competitors, capture customer interest, and encourage conversions.
Sponsored Listings
Sponsored listings are paid placements within search engine results or eCommerce platforms. They are usually marked as ‘sponsored’ or ‘ad,’ and they often appear at the top of search results and alongside organic product listing results.
Unlike organic search results, sponsored listings are prioritized based on the advertiser’s bid amount and relevance to users’ search queries.
Sponsored listings offer a strategic advantage by enabling businesses to connect directly with consumers who are actively searching for their products. This targeted approach ensures that marketing efforts are focused on individuals with high intent of making a purchase, maximizing the potential return on investment.
The Power of Banner Ads and Sponsored Listings
Banner ads and sponsored listings are great choices for boosting customer engagement and product sales. Here are four key advantages they offer:
Enhanced Visibility: Digital retail media strategically places your brand where it will stand out—outshining competitors and grabbing the attention of high-purchase-intent consumers.
Precision in Reach: These ads target specific keywords or categories, allowing for highly focused advertising based on demographics and search intent.
Minimal Conversion Friction: Smooth transitions from ads to a brand’s native store or product listing on the marketplace keep conversion friction to a minimum.
Brand Awareness and Recall: Consistent exposure to your brand through banner ads and sponsored product listings can leave lasting impressions and build brand recognition.
The bottom line is that it’s increasingly important for brands to monitor their Share of Media.
How to Monitor Your Brand’s Share of Media
DataWeave’s Digital Shelf Analytics (DSA) platform extends beyond the traditional Share of Search metrics and provides robust support for monitoring the Share of Media.
DataWeave monitors the Share of Media in two ways: keywords and product categories. Users can view Share of Media insights through aggregated views, trend charts, and detailed tables. The views are designed to show brand visibility and the overall competitive landscape. For example, the screenshot below, taken from DataWeave’s dashboard, showcases the Share of Media across keywords, categories, and retailers.
Share of Media by Keyword
The Share of Media metric captures a brand’s advertising presence within search listings for a designated keyword. This provides a comprehensive view of a brand’s visibility and promotional efforts across retail platforms, helping brands validate and gauge the effectiveness of their ad spend.
For example, the screenshot below shows the trend of manufacturer’s Share of Media by keyword—‘baby food.’
Share of Media by Category
The Share of Media metric measures the presence of brands’ banner ads and sponsored listings across product categories on retail sites. This helps brands see which product categories require more investment, making it easier for them to spend their ad budget wisely.
The screenshot below illustrates manufacturers’ Share of Media by category across retailers.
Share of Media: An Essential Ecommerce Metric
As retail media continues to evolve, our analytics must follow—after all, knowledge is a competitive advantage. In the dynamic world of eCommerce, where competition is fierce and consumer attention is scarce, understanding your share of media is crucial.
Analyzing the Share of Media can give brands a competitive edge. By regularly monitoring and analyzing this metric, you can make data-driven decisions to improve your brand’s visibility, attract more customers, and ultimately drive sales growth. With a deeper understanding of their target audience and market dynamics, brands can refine promotional efforts to drive more effective results and optimize return on ad spend (ROAS).
For more information on how Digital Shelf Analytics can enhance your brand’s digital shelf presence, request a demo or contact us at contact@dataweave.com.
