The DataWeave Blog

Tag: retail

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
The Essential Price Management Framework for Retailers
As a leader with over 20 years of experience leading pricing strategy at a major US grocery chain, I deeply understand the complexities pricing teams face when trying to derive, quantify, and execute corporate pricing initiatives.

Providing insights into the competitive marketplace in order to ensure the overall success of directed pricing strategies is more than simple reporting.

That’s what many teams get wrong.

Reporting is a post-mortem, which is a valuable exercise, but not one that will help you achieve your pricing goals all by itself. After all, your pricing goals can change due to a number of reasons: macroeconomic challenges, regional competition, corporate objectives, along with several other factors.

Pricing teams need a well-defined process to devise and implement their pricing strategies. This process needs to holistically examine your product base to provide robust price management. It also needs to be backed up by technology powered by the latest advancements because you can be sure your competition is already thinking that way.

Let’s break down an effective and modern price management process for retailers.

Data Collection

The first aspect of any effective price management framework for retailers is a clearly defined product data collection. You need to understand your collection in terms of who to collect pricing data from, what data to collect, where to collect it from, and how often.
- The who: Consists of both primary competition and others you’d like to keep tabs on
- The what: Can range from targeted single items like Key Value Items (KVIs) or total portfolio
- Where: Can range from targeted locations within your market or the total competitive network
- How often: To be able to support your price management process and for reporting purposes, determining a cadence is essential.
Data is power and the more data you can acquire, the more insights you’ll gain. Make sure that your collection data is well thought out ahead of time. Leaning on a price management framework built for retailers that can aggregate all your data into representative prices can help.

For example, if you have multiple competitive stores in a single market, flattening pricing data into a defined representative price will help speed up your analysis. Don’t get confined to a single store when a comprehensive assortment view across your target markets will provide a more accurate understanding.

Data Refinement

Competitive Matched Items

Next, you need to examine your competitive-matched items. These are the products that you want to be priced in direct response to your competitors’ pricing. The goal is to remain closely aligned with their prices so as not to lose market share while simultaneously achieving your corporate strategies.

Your price management system needs to help you manage your overlapping items. Trying to do so manually will be inefficient and is almost impossible to execute across 100% of your product catalog.

The mapping needs to go beyond exact UPC / PLU matches to encompass other match criteria. It needs to be able to incorporate any number of derivatives, including competitor-specific item codes like Amazon’s ASINs or Target’s DPCIs. This will help you overcome the challenge of mapping exact items to a competitor when the competitor’s site doesn’t showcase a UPC. It will also help you map your own private-label items to your competitor’s private-label counterparts.

A good price management framework will also help you match the same items but with dissimilar sizes (e.g., Cheerios 18 OZ vs. Cheerios 20 OZ), either by letting you match directly within acceptable tolerances or by enabling you to compare prices on a per-unit basis.

We need to leverage GenAI to help facilitate matches beyond UPC / PLU exact matches, such as Exact Item with no Competitor Code, Exact Item with Competitive Specific Codes, Similarity Matching on Private Label, Similarity Matching on Size all need to leverage it.

If you’re playing in a vertical that doesn’t always have a unifying code (restaurants, apparel, etc.) you’ll need to leverage the latest GenAI tools to map items together for price management. The variables are simply too numerous and complex to do manually.

Unmatched Items and Internal Portfolio

Not every product will be included in your competitive-matched items collection. Competitive matches in your internal portfolio offer a proxy for building clear and concise price management strategies that are in line with your corporate initiatives.

However, your unmatched items still need to be factored into your price strategy. If you only manage your competitively priced items, you won’t have a holistic viewpoint of your total product catalog and pricing. It’s critical to ensure that internal portfolio items are effectively mapped and grouped in order to extend overall price management.

Here are three things you need to consider when managing the pricing of your internal product portfolio. A smart price management framework is your best bet for achieving these results:
- Value Size Groupings
  Value size groupings allow for the same branded items of different sizes to be priced accordingly to ensure price parity. You don’t want to sell a private label gallon of milk for $4.00 while the half gallon is at $1.75, for example. You need certain mechanisms in place to alert you when price parity is off. This is especially true when some of your items are competitively matched, and others are not.
- Relationships between Brands
  Relationships between brands are also critical to ensure price parity. There should be well-defined relationships between like-sized products that are from different brands. This will ensure that your private label program is priced ‘at a value’ compared with their national branded counterparts. You need to maintain the balance between different private label tiers along with different national brand tiers.
- Price Links
  Price Links are also critical to keeping up to date from a consumer perspective. Your customers expect that certain items should be priced together and will be put off if they are not. For example, if you sell an item in different sizes or flavors and scents, their prices should be logically linked.
For your internal portfolio, there may be items that don’t have a competitive match or simply don’t fall into one of your internal portfolio groupings. These are unique items to your banner and should be considered margin drivers for your brand.

Leveraging Data for Action

Now that you have a complete line of sight into both competitively matched items and internal mappings, you can move to fully leveraging your data. Figuring out how to utilize these competitive insights to understand where your price positioning is compared with your competition can be a challenge without a playbook. An effective price management framework will help guide you to the best insights and help you understand how it relates to your corporate strategy.

If you don’t have a well-defined corporate pricing strategy (competitive or margin) or you need to update it to be more modern, the data sets provided by a price management framework can help you ascertain where you are in your pricing journey. They can also help you identify options for where you want to go.

Here are some other ways a price management framework can help you improve your pricing strategy:
- Utilize Competitive Data
  Get competitive insights, identify competitive price zones, and understand your competitors’ pricing philosophy. Figure out if they’re using strategies like:
  - High-Low
  - Everyday Low Price (EDLP)
  - Cost Plus
- Unravel Competitor Strategy
  See if you can unlock what your competition has planned for pricing strategy and promotions. Try relating what you see in corporate filings and tie back to what you see in your competitive data sets.
- Assortment Analysis
  Try looking at the data not only from a pricing perspective but also from a competitive assortment, promotion, and supply chain perspective.
- Proactive Alerts
  Establish alerts for your internal portfolio to ensure that you don’t exceed your tolerance based on price moves.
Leveraging a Price Management Framework Designed for Retailers

A price management system designed specifically for you as a retailer is a game changer. An effective one can be configured specifically for the price owners, whether you have a dedicated team for this function or the price is owned by the category management team.

For category managers, standard reporting offers a clear view of pricing performance and trends. Beyond that, competitive intelligence becomes crucial—using data from various sources like collected pricing data, market filings, social media insights, etc. to provide the senior leadership team with a deeper understanding of competitor strategies and actions. This empowers informed decision-making at the highest levels.

With these price management insights, retailers can gain a holistic view of the competitive marketplace, uncover gaps and opportunities, and scale their business more effectively. As someone with experience on the retailer’s side of the market, I know first-hand how valuable these insights can be.

We’d love to talk with you if you’re interested in learning more about DataWeave’s AI-powered price intelligence solution for retailers. Click here to schedule an introductory conversation.
August 29, 2024
Revolutionizing Fuel Pricing: How Fuel Retailers and Convenience Stores Can Gain a Winning Edge with DataWeave
Consider this scenario: A retailer establishes its fuel prices using pricing data that’s a few days old, only to subsequently discover that a nearby competitor is offering substantially lower prices. The result? Lost customers, decreased foot traffic, and diminished sales. This serves as a stark reality that retailers must confront and address today.

In the fiercely competitive realm of retail, where every decision holds weight, maintaining a competitive edge is paramount. The fuel category, frequently underestimated, has the potential to significantly impact a retailer’s revenue stream. This challenge is not unique; retailers worldwide, particularly in North America, grapple with a common hurdle: mastering the intricate art of real-time fuel pricing.

The Quest For Reliable, Real-Time Fuel Pricing Data

For retailers, traditional methods for procuring and analyzing fuel price data have proven to be both expensive and error-prone, often relying on manual data collection or third-party data providers. These outdated approaches yield frustrating delays, inaccuracies, and missed opportunities. When it comes to obtaining timely fuel pricing intelligence, the majority of fuel retailers grapple with three central challenges:
- Low Accuracy: Ensuring that fuel pricing information remains up-to-date, dependable, and actionable, even when sourced from complex web-based platforms.
- Less Coverage: Acquiring comprehensive data that encompasses all of North America, spanning across retailers, convenience stores, fuel stations, and beyond.
- High Cost: Effectively managing the substantial costs associated with acquiring and processing this vital information.
DataWeave’s Fuel Pricing Intelligence Solution

Comprehensive, accurate, and real-time fuel pricing intelligence can play a huge role in the profitability of retailers throughout North America. DataWeave takes the forefront in delivering this transformative Data-as-a-Service (DaaS) solution to some of the most prominent retailers in the region, including the top 20 fuel retail behemoths.

With a rich and extensive history spanning over a decade in the realm of competitive intelligence, DataWeave boasts an impressive track record of empowering well-informed decision-making in retail. We leverage state-of-the-art technology to bring an unparalleled level of accuracy, timeliness, and coverage to fuel pricing intelligence.

The following are some compelling advantages offered by our solution:

Accurate and Real-Time First Party Data

We deliver retailers an unparalleled advantage through real-time, first-party fuel price data. Our data originates directly from the retailer’s own channels, encompassing websites and mobile apps, rendering it the industry’s foremost and most reliable source.

Imagine having access to fuel pricing information that updates as frequently as every 30 minutes. This rapid update cadence guarantees that you, as a retailer, constantly possess the latest pricing insights at your fingertips, empowering you to respond swiftly to market fluctuations and competitor manoeuvres. Our comprehensive data spans a wide spectrum of fuel types, including:
- Gasoline: Be it regular, mid-grade, super, premium, ethanol-free, ethanol blends, methanol blends, or reformulated gasoline, we have got you covered.
- Diesel: Our data encompasses biodiesel, biodiesel off-road, biodiesel blends, biodiesel ultra-low sulfur (ULS), diesel ultra-low sulfur (ULS), diesel off-road, standard diesel, and premium diesel.
Armed with our real-time, first-party data, you can make pricing decisions with unwavering confidence, secure in the knowledge that you possess access to the most current, authoritative, and extensive fuel pricing intelligence in North America.

The data points we capture directly from relevant web sources include: gas station postal code, store name and code, location, city, state, ZIP code, fuel type, competitor name, regular price, member price (if available), time and date of data capture, and more.

Click here if you wish to access a sample report of our fuel pricing data.

Unrivaled Geographical Coverage

Our extensive coverage of fuel data spans over 30,000 ZIP codes and encompasses the top 100 retailers across the western, mid-western, and eastern regions of the United States.

Retailers benefit from the flexibility to configure and tailor the solution to their precise needs, whether it involves adding more locations or selectively acquiring specific segments of the data. This far-reaching coverage guarantees that retailers, whether situated in bustling urban centers or remote areas, can readily access the essential data required to maintain their competitive edge.

Moreover, if you currently source your fuel pricing data from alternative providers, our solution seamlessly integrates, amplifies, and complements your existing array of data sources, ensuring a harmonious and unified approach to data acquisition.

Optimization of Dynamic Pricing Strategies

In the world of retail, the importance of timing cannot be overstated. Even a mere difference of a few cents can translate into millions of dollars in revenue impact. With DataWeave, retailers gain the capability to make data-driven decisions that provide them with a competitive edge around the clock, every single day.

Our platform empowers you to unearth margin gaps by pinpointing opportunities to raise prices while maintaining your competitive pricing position. It also identifies instances where you may be substantially overpriced, prompting necessary price adjustments to ensure competitiveness within the market. All these valuable insights are available at a hyperlocal level, facilitating pricing efficiency and optimization across your various regions of coverage. Equipped with this real-time data, you can swiftly adapt to ever-changing market conditions.

Furthermore, our comprehensive competitive data seamlessly integrates into your existing pricing systems through APIs, facilitating quick and informed pricing actions based on robust data.

Reliable and Customer-First Tech Platform

Our platform boasts a remarkable level of sophistication when it comes to data aggregation, normalization, visualization, and integration capabilities. It stands as a massively scalable system with the capacity to aggregate billions of data points daily, spanning thousands of web sources. This includes the intricate handling of sources like mobile apps and websites known for frequently altering their site structures, among others.

What truly sets us apart is our proficiency in addressing these challenges through a blend of human expertise and large-scale machine learning. Additionally, our commitment to delivering unmatched service extends to round-the-clock, 24/7 support. This comprehensive approach makes our fuel pricing intelligence solution not only effective but also cost-efficient in meeting your fuel data requirements.

We also provide a variety of options for you to consume our data, which includes receiving our reports via email, SFTP, S3 buckets, data lakes like Snowflake, and APIs.

Enhance your Fuel Pricing Strategies with DataWeave

In the ever-competitive world of retail, staying ahead is not just a goal; it’s a necessity. The fuel pricing landscape, often overlooked, holds immense power to impact a retailer’s profitability. DataWeave’s real-time, comprehensive, and accurate fuel pricing intelligence solution is the key to securing this advantage. Retailers and convenience stores now have a powerful platform at their disposal, offering unparalleled precision, comprehensive coverage, and the agility needed to navigate this landscape.

Join the ranks of industry leaders who have already harnessed the potential of DataWeave. Reach out to us today to redefine your approach to fuel pricing and propel your business to new heights!
September 29, 2023
Valentine’s Day eCommerce Insights

Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions and help drive profitable growth in an intensifying competitive environment. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

February 11, 2022