Product Matching and Chaining: Make Your Product Comparisons More Reliable with AI

Product matching—or "matching" in some contexts—involves automatically identifying equivalent products across different brands or product lines in order to compare their prices, performance, and competitive positioning on a consistent basis.

This study addresses a simple yet fundamental issue: comparing prices only makes sense if one is comparing products that are truly equivalent. Without a reliable framework, competitive intelligence may end up comparing similar but different products (a different format, a slightly different composition) and skew the conclusions regarding the retailer’s actual price positioning.

BOOPER MPS organizes this association through several complementary mechanisms: automatic AI-powered matching for cases where matches can be detected based on product names and attributes; cloning when there is no direct match via EAN; and chaining to organize the relationships between the retailer’s own products.

For a retailer, this product pairing is an often-overlooked but essential prerequisite for any serious competitive pricing strategy: it determines the reliability of price monitoring and, consequently, the effectiveness 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 feedback 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 descriptions: two different retailers rarely refer to the same product in the same way, which makes it impossible to reliably match them based solely on text correspondence. Natural language processing makes it possible to recognize that these are the same products 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 References Tracked across multiple competing brands, whereas AI makes it possible to handle this volume while still allowing for human validation in cases of uncertainty.

Manual matching pairs products one by one by comparing product names and product listings, often in a spreadsheet; AI-based matching automatically suggests matches. At BOOPER, the EAN code immediately matches products that share it; if there is no common EAN, the AI reads the product names using natural language processing (NLP), compares attributes, formats, and brands, and analyzes the web Product photo. Each suggestion is assigned a confidence score.

The difference lies primarily in volume and precision. Manually compiling several thousand product References Tracked s across multiple competing brands—and keeping them up to date with every change in the product lineup—takes a considerable amount of time. AI processes this volume and distinguishes between very similar variants within the same product line: capacity, color, and scent.

AI-powered matching does not eliminate human validation: it focuses that validation on uncertain matches—indicated by a low confidence score—and each validation helps the algorithm learn. For more information, see our guide to competitive product matching.

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, weighting factors, and historical patterns.

These two mechanisms serve different purposes. Cloning is used for competitive comparison: it allows a product from a particular retailer to be matched with an equivalent product from a competitor when automatic matching by standard identifier (EAN) is not possible—for example, in the case of a private-label product that has no direct equivalent listed by the competitor.

The retail chain, for its part, works internally on the brand’s product assortment: it establishes hierarchical relationships between similar SKUs (variations in size and packaging) and uses coefficients that make it possible to reconstruct a coherent history even when SKUs change or are replaced over time.

For a retailer, these two relationships 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’s SKU changes without actually changing its nature.

Yes: BOOPER identifies substitutable products based on their functional attributes and price positioning, allowing you to structure your product chains (national brands, private labels, budget brands) and product lines. The relationships established (hierarchy, coefficients, history) follow your own rules.

Chaining links products within your own product line, whereas cloning links them to those of competitors. It is used to maintain consistent price tiers within a product line and to analyze cannibalization and up-selling between national brands, private labels, and budget brands.

As with matching, the proposal is still subject to your teams' approval: your management rules determine which chains are applied. For an overview of the concept, see the definition of automatic chaining.

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-market positioning.

This structure addresses a key challenge in category management: national brands, private labels, and budget brands are not isolated categories but rather 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.

Specifically, this analysis highlights cannibalization effects (a customer switching from a national brand to a store brand when the price gap narrows too much) or upmarket 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 PPx is crucial for building a consistent pricing structure 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 matching process is not fully automated. Although the recommendations are becoming increasingly intelligent and automated, the matching process requires human validation so that the algorithms can learn.

This choice is not a technical limitation but a methodological principle: an erroneous product-market association subsequently skews all competitive analyses based on it, with a ripple effect on pricing decisions. Maintaining human oversight of uncertain cases helps prevent mismatching errors from silently propagating throughout the pricing management process.

This process is part of a continuous learning approach: 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 thus progresses, but is always accompanied by business-side oversight for the less obvious matches.

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, even though the accuracy of pricing decisions—which rely on competitive comparisons—depends directly on that database.

MPS leverages product catalogs, product descriptions, technical attributes, price histories, and competitive data. The richer the data, the more accurate the product pairing suggestions.

The product catalog and product descriptions form the basis for product matching: the more comprehensive 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. Historical price data and competitive data are then used to refine and validate these matches over time.

When product catalogs are incomplete or poorly structured (generic descriptions, missing attributes), the accuracy of the suggested associations 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 database, which determines the performance of all the modules that rely on it—from product 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 noticeable benefit: reconciling data between competing retailers is traditionally a time-consuming task when done manually, item by item, and is particularly difficult to keep up to date for a broad product assortment with a high product turnover rate.

The gain in reliability, on the other hand, is less directly measurable but has a greater impact over the long term: competitive comparisons based on a reliable product benchmark prevent pricing decisions from being skewed by flawed comparisons, which improves the quality of pricing decisions over the long term rather than just for a single campaign.

For a retailer, this module is often a foundational component that is not very visible but plays a crucial role: while it does not directly generate profit on its own, it determines the reliability of all competitive analyses and pricing decisions that are subsequently based on these product comparisons.