Tag: Competitive Matches

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

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

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

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

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

The Match Rate Challenge

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

Let’s put it into focus. Imagine you’re trying to price a 12-pack of Coca-Cola. This is a well-known product that, hypothetically, should be easy to identify across the web. However, every retailer uses their own description in their listing. Some examples include:
- 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.
1. First, conduct a baseline assessment. Figure out where you are on the Matching hierarchy. Are you still only doing direct code-based comparisons? Has your team branched out to compare other non-code-based identifiers?
2. Next, establish clear match rate targets for yourself. If your current match rate is aligned with industry norms, strive to significantly improve it, aiming for a high alignment that supports maximizing the match rate. Break this down into achievable milestones across different stages of the implementation process.
3. Work with your vendor on quality control processes. It may be worth running your current process in tandem to be able to calculate the improvements in real time. With a veteran technology provider like DataWeave, you can rely on the most cutting-edge technology combined with human-in-the-loop checks and balances and a team of knowledgeable support personnel. Additionally, for teams wanting direct control, DataWeave’s Approve/Disapprove Module lets your team review and validate match recommendations before they go live, maintaining full oversight of the matching process.
4. The more data about your products it has, the better your match rates. DataWeave’s competitive intelligence tools also come with a built-in continuous improvement framework. Part of this is the human element that continually ensures high-quality matches, but another is the AI’s ‘learning’ capabilities. Every time the AI is exposed to a new scenario, it learns for the next time.
5. The final step, ensure cross-functional alignment is achieved. Every one from the C-Suite down should be able to access the synthesized information useful for their role without complex data to sift through. Customized dashboards and reports can help with this process.
Future-Proofing Match Rates

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

Key Takeaways

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

Starting this process to add AI to your pricing strategy can be overwhelming. At DataWeave, we work with you to make the change easy. Talk to us today to know more.
February 5, 2025
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