Product matching—or “matching” in certain situations—involves automatically identifying equivalent products across different retailers or product lines in order to compare their prices, performance, and competitive positioning on a consistent basis. This process addresses a simple yet fundamental issue: comparing prices only makes sense if one is comparing products that are truly equivalent. Without reliable matching, competitive intelligence risks comparing products that are similar but different—such as a distinct format or a slightly different composition—and thereby skewing conclusions about the retailer’s actual price positioning. BOOPER MPS structures this product association through several complementary mechanisms: automatic AI-powered matching for cases where matches can be detected via product names and attributes; cloning when no direct EAN match exists; and chaining to organize relationships among the retailer’s own products. For a retailer, this product association is an often-overlooked but essential prerequisite for any serious competitive pricing strategy: it determines the reliability of price monitoring and, consequently, the relevance of pricing decisions made in response to or in anticipation of market conditions.

The AI simultaneously analyzes product descriptions, attributes, formats, brands, and pricing patterns to detect similarities that are invisible to human analysis. It continuously learns from user validations to improve its accuracy. When available, the AI also incorporates image recognition. The main benefit of AI in this area is its ability to handle heterogeneous product names: two different retailers rarely name the same product in the same way, making it impossible to reliably match items based solely on text correspondence. Natural language processing makes it possible to recognize that two items are the same product despite different names, by relying on attributes, format, and brand in addition to the product name. This accuracy improves over time thanks to a continuous learning mechanism: each validation or correction made by a user enriches the model, which gradually refines its recommendations for future matches rather than remaining fixed on its initial settings. For a retailer, this capability changes the scale at which matching can be performed: manual product matching quickly reaches its limits when dealing with an assortment of several thousand SKUs tracked across multiple competing retailers, whereas AI enables this volume to be handled while still allowing for human validation in uncertain cases.

When there is no direct association via EAN, cloning takes over and links the retailer’s products with those of competitors; this applies equally to private-label and national brands. The other type of complementary relationship is chaining: the retailer’s products are grouped together to establish links based on hierarchy, coefficients, and historical patterns. These two mechanisms serve different purposes. Cloning facilitates competitive comparison: it allows a retailer’s product to be matched with a competitor’s equivalent when automatic matching via a standard identifier (EAN) is not possible—for example, for a private-label product that has no direct equivalent listed by the competitor. Chaining, on the other hand, operates internally within the retailer’s product assortment: it structures hierarchical links between closely related SKUs (variations in formats and packaging) and coefficients that enable the reconstruction of a coherent history even when SKUs evolve or are replaced over time. For a retailer, these two approaches are complementary rather than redundant: cloning ensures the accuracy of external competitive analysis, while chaining ensures the continuity and consistency of internal analysis, particularly when a product changes its SKU without actually changing its nature.

BOOPER identifies substitutable products based on their functional attributes and price positioning in order to structure MN/MDD/PPx chains and analyze the effects of cannibalization and up-selling. This structuring addresses a central challenge in category management: national brands, private labels, and budget brands are not isolated categories but product lines that interact with one another in customers’ purchasing decisions. Linking these products together makes it possible to analyze how a price change for one affects the sales of the others, rather than analyzing each product line in isolation. In practical terms, this analysis highlights cannibalization effects—a customer switching from a national brand to a private label when the price gap narrows too much—or up-selling effects, when the perceived difference in quality justifies a wider price gap without a loss in volume. For a retailer, this cross-analysis of national brands, private labels, and premium products is crucial for building a coherent pricing strategy within a category: setting the price of a private label without considering its positioning relative to the equivalent national brand amounts to ignoring a large part of the actual dynamics of demand on the shelf.

No, the association process is not fully automated. Although recommendations are becoming increasingly intelligent and automated, the association process requires human validation to enable the algorithms to learn. This choice is not a technical limitation but a methodological principle: an erroneous association subsequently skews all competitive analyses that rely on it, with a cascading effect on pricing decisions. Maintaining human oversight over uncertain cases prevents matching errors from silently propagating throughout pricing management. This approach is part of a continuous learning process: the more users validate or correct the AI’s suggestions, the more the model refines its accuracy and, over time, reduces the number of cases requiring manual intervention. Automation is therefore advancing, but it is always accompanied by business-side oversight of the less obvious price matching decisions. For a retailer, this balance between automation and human validation ensures reliability: it prevents the quality of the product database from being sacrificed for the sake of processing speed, given that this database directly determines the accuracy of pricing decisions based on competitive comparisons.

MPS utilizes product catalogs, product descriptions, technical attributes, price histories, and competitive data. The richer the data, the more accurate the product pairing recommendations. The product catalog and product descriptions form the basis of the matching process: the more complete and structured the available attributes are (format, brand, packaging, category), the more reliable information the AI has to distinguish between products that are truly equivalent and those that are merely similar. Price histories and competitive data then refine and validate these matches over time. When product catalogs are incomplete or poorly structured—with generic descriptions or missing attributes—the accuracy of the association suggestions suffers directly, which explains why human validation remains necessary in cases where the source data is of poor quality. For a retailer, this means that the quality of product matching depends in part on a foundational task that is often overlooked: structuring and ensuring the reliability of the internal product catalog, which determines the performance of all the modules that subsequently rely on it—from matching to competitive analysis.

The benefits stem from reduced manual effort, reliable price comparisons, improved business decisions, and overall competitive performance. The first benefits are typically seen within a few months. The time saved on manual work is often the first visible benefit: comparing product lines across competing retailers is traditionally a time-consuming task when done manually, item by item, and is particularly difficult to keep up to date across a broad product range with a high product turnover rate. The gain in reliability, on the other hand, is less directly measurable but carries greater weight in the long term: competitive comparisons built on a reliable product database prevent pricing decisions from being skewed by inaccurate alignments, which improves the quality of pricing decisions over the long term rather than just for a single campaign. For a retailer, this module often serves as a foundational building block that is less visible but crucial: it does not directly generate profit margins on its own, but it determines the reliability of all competitive analyses and pricing decisions that subsequently rely on these product comparisons.