The DataWeave Blog

Tag: data quality

From Raw Data to Retail Pricing Intelligence: Transforming Competitive Data into Strategic Assets
Poor retail data is the bane of Chief Commercial Officers and VPs of Pricing. If you don’t have the correct inputs or enough of them in real time, you can’t make data-driven business decisions regarding pricing.

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

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

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

Poor Data Refinement vs. Good Refinement

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

Retailer A

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

Now, imagine that your pricing data is outdated and unrefined when you go to price your new sneakers.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

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

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

Retailer B

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

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

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

The Hidden Cost of Unrefined Data

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

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

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

The Two Pillars of Data Refinement

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

Competitive Matches

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

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

Internal Portfolio Matches

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

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

Leveraging AI for Enhanced Match Rates

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

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

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!
January 30, 2025
How AI Can Drive Superior Data Quality and Coverage in Competitive Insights for Retailers and Brands
Managing the endlessly growing competitive data from across your eCommerce landscape can feel like pushing a boulder uphill. The sheer volume can be overwhelming, and ensuring that data meets standards of high accuracy and quality, and the insights are actionable is a constant challenge.

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

The Data Quality Challenge for Retailers and Brands

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

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

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

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

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

Improving the Accuracy of Product Matching

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.

To learn more, reach out to us today or email us at contact@dataweave.com.
January 22, 2025
Mastering Fuel Price Competitiveness: How First-Party Data Outperforms Third-Party in Pricing Accuracy
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.

To learn more, talk to us today!
October 21, 2024
Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy
In today’s expansive omnichannel commerce landscape, pricing intelligence has become indispensable for retailers seeking to stay competitive and refine their pricing strategies. The sheer magnitude of eCommerce, spanning thousands of websites, billions of SKUs, and various form factors, adds layers of complexity. Consequently, ensuring the accuracy and reliability of competitive insights presents a formidable challenge for retailers aiming to leverage pricing data effectively.

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

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

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

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

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

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

Subjectivity in Matching Criteria

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

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

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

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

Contextual Factor

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

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

High Accuracy & Coverage Expectations

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

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

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

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

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

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

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

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

Several Rounds of Data Verification Due to Hierarchical Validation Teams

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

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

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

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

Confidence-based Distribution of Matched Articles for Validation

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

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

Automated, Standardized Process with Iterative Feedback Loop

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

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

Unleash the Power Accurate and Comprehensive Product Matching

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

To learn more, reach out to us today!
May 2, 2024
How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers
Retailers heavily depend on pricing intelligence solutions to consistently achieve and uphold their desired competitive pricing positions in the market. The effectiveness of these solutions, however, hinges on the quality of the underlying data, along with the coverage of product matches across websites.

As a retailer, gaining complete confidence in your pricing intelligence system requires a focus on the trinity of data quality:
- Accuracy: Accurate product matching ensures that the right set of competitor product(s) are correctly grouped together along with yours. It ensures that decisions taken by pricing managers to drive competitive pricing and the desired price image are based on reliable apples-to-apples product comparisons.
- Freshness: Timely data is paramount in navigating the dynamic market landscape. Up-to-date SKU data from competitors enables retailers to promptly adjust pricing strategies in response to market shifts, competitor promotions, or changes in customer demand.
- Product matching coverage: Comprehensive product matching coverage ensures that products are thoroughly matched with similar or identical competitor products. This involves accurately matching variations in size, weight, color, and other attributes. A higher coverage ensures that retailers seize all available opportunities for price improvement at any given time, directly impacting revenues and margins.
However, the reality is that untimely data and incomplete product matches have been persistent challenges for pricing teams, compromising their pricing actions. Inaccurate or incomplete data can lead to suboptimal decisions, missed opportunities, and reduced competitiveness in the market.

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

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

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

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

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

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

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

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

View Product Match Rates Across Websites

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

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

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

Track Data Freshness Easily

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

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

Proactively Manage Product Matches

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

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

Gain Unparalleled Visibility into your Data Quality

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

To learn more, reach out to us today!
January 22, 2024