Pricing is closely linked to a retailer’s commercial appeal and, therefore, to its competitiveness, success, and long-term viability. In a highly competitive environment marked by online price transparency and the growing volatility of product assortments, developing an effective pricing strategy can no longer rely on intuition or one-off benchmarks. Effective pricing requires comprehensive, comparable, and continuously updated data. Without reliable product matching, there is no data-driven pricing—only costly approximations.
To fully leverage the value of this data, it must first be properly matched. This is where product matching comes into play: the ability to identify, on a large scale, equivalencies or similarities between products sold by different market players. Far from being a simple comparison of EAN codes, this is a strategic process that directly determines the quality of price positioning and the performance of a pricing tool.
The advent of artificial intelligence in data commerce solutions has profoundly transformed this field. Product matching is no longer limited to recognizing identical identifiers: it analyzes attributes, descriptions, visuals, and functional characteristics to detect subtle similarities, equivalencies in use, or adjusted comparability. It thus becomes a foundational pillar for any ambitious, competitive, and sustainably profitable pricing strategy.
In this article, we explore the key role of artificial intelligence in product matching in retail, from establishing reliable competitive comparability to the operational management of pricing.
Product Matching: Competitive Comparability as a Cornerstone of Pricing Strategy
Product matching in retail relies primarily on the collection of competitive data: prices, product descriptions, visuals, and EAN codes. This step is an essential prerequisite for any comparative analysis. Depending on the sector and channel, this data is sourced from web-based collection methods—notably via Web Data Collect and scraping of competitors’ websites—or from in-store surveys (Scan In Store), which are particularly common in the food industry and brick-and-mortar retail.
While this raw data is essential, its value remains limited as long as it is neither structured nor made comparable. As is, it offers a fragmented view of the market and does not allow for precise management of pricing strategy. It is precisely the role of product matching to transform this raw data into actionable information.
By linking a retailer’s data to that of its competitors—whether the data is exactly the same or comparable—matching helps build a reliable and actionable basis for comparison. It organizes competitive data and reveals the actual price positioning, product by product and category by category.
Thanks to reliable matching, pricing teams can:
-
measure the actual level of competitive pressure,
-
incorporate market prices into their pricing rules,
-
define a consistent, competitive, and profitable positioning.
Product matching, therefore, is not merely a technical exercise in matching product codes. It is a prerequisite for pricing management and directly determines the relevance, robustness, and defensibility of any pricing decision.
Why Is Artificial Intelligence Revolutionizing Product Matching?
The Limitations of Manual Matching
Historically, product matching has been a tedious task. Manually cross-referencing thousands of SKUs across multiple competitors, with product assortments that are constantly changing, ties up significant resources and severely limits the scope of the analysis.
For retailers with extensive product catalogs or multi-brand operations, this approach quickly becomes an operational burden that is incompatible with the responsiveness demanded by the market. Artificial intelligence makes it possible to overcome these limitations.
AI Enables Fast, Reliable, and Scalable Product Matching
The advanced pricing solutions include AI-powered matching engines capable of automating, accelerating, and ensuring the reliability of competitor product matching while maintaining business control.
1. Automation of EAN-Based Matching
When EAN codes are identical, the algorithm automatically performs the matching without human intervention.
The benefits are immediate:
-
huge time savings,
-
improved reliability of connections,
-
a consistent and actionable competitive foundation.
Depending on the sector, 40 to 80 percent of product listings can thus be matched automatically.
2. AI-assisted matching for complex cases
Artificial intelligence now makes it possible to match products with a single click, even when the matches aren’t obvious. To do this, algorithms simultaneously analyze the photographs and product descriptions in the database.
Competitor product descriptions are first standardized using thesauri and semantic rules. The AI then assigns each product a similarity score, weighted by price levels and key features. Based on this scoring, competing products are ranked and matched with the retailer’s most relevant products.
When EANs differ or are missing—a common occurrence for private-label products, multiple formats, or variations in quantity—the AI assists the user.
The matching engine simultaneously analyzes:
-
product photographs,
-
standardized wording,
-
technical attributes (format, volume, specifications),
-
price ranges.
Based on this information, the algorithm calculates a similarity score, ranks competing products, and suggests the most relevant matches. The user then confirms the selection with a single click.
The more the system is used, the more it learns and refines its recommendations. Confidence thresholds can be set (for example, a score above 98%) to allow automatic matching without human validation.
3. Scale up matching to enhance price competitiveness
Even in the rare cases where manual processing still occurs, the goal remains the same: to limit the use of high-value-added resources on repetitive tasks. When necessary, some retailers choose to outsource these final steps so that their teams can focus on analysis, strategy, and pricing management.
This comprehensive approach—combining automation, intelligent assistance, and continuous learning—enables the creation of a reliable, consistent, and actionable product database, which is an essential foundation for any effective pricing strategy.
By ensuring reliable competitive comparability, AI-enhanced product matching directly contributes to improving price competitiveness, market positioning, and the quality of pricing decisions.
Product matching at the heart of a comprehensive pricing tool: XPA - Optimix Pricing Analytics
Effective product matching cannot be managed as a standalone process. To create value, it must be part of a comprehensive pricing tool capable of linking the quality of product matches to concrete pricing decisions.
Advanced pricing solutions, such as XPA, the pricing software from Optimix Solutions, include dedicated reporting dashboards for managing matching. They enable continuous monitoring of key metrics, including:
-
the percentage of products matched within the competitive scope,
-
the level of competitive intensity by category or segment,
-
the quality and reliability of the tie rods,
-
Changes in the competitive landscape over time…
This process is not intended solely to measure the performance of the matching process itself. Above all, it helps ensure the integrity of the entire pricing decision-making process: the more reliable the basis for comparison, the more relevant, consistent, and defensible the pricing recommendations, positioning rules, and margin decisions will be.
By integrating price-matching reporting into the core of the pricing tool, retailers gain a unified view—from raw competitive data all the way through to the operational execution of pricing decisions. This continuity is essential for scaling up price management, building team confidence, and supporting a successful pricing strategy over the long term.
Product Matching: Toward EAN-Based Automation?
Automatic linking requires no human intervention. The algorithm analyzes the imported competitor data and compares it with the retailer’s product data. When it identifies identical EANs, it links the corresponding products together. This linking process is automatic.
Obviously, this process saves our customers a great deal of time and performance when it comes to matching. It makes a major contribution to generating a reliable, high-quality product database, useful for improving competitiveness and price positioning.
Depending on the brand’s sector of activity, it is estimated thatbetween 40% and 80% of its products can be matched. However, automatic matching is not always possible. It is then up to the user to check that the products to be linked are identical or comparable. In this case, the AI supports its decision-making.
