Removing duplicates from your catalog Product : to improve the reliability of the internal repository
Product al matching isn't just a matter of competitive intelligence: internally, it eliminates the need to manage prices, inventory, and product assortments based on a catalog riddled with duplicates across brands, channels, or systems.
A reliable " Product " data model is a prerequisite for any serious data analysis: without it, price elasticity, cross-selling, and inventory tracking are calculated based on inaccurate data.
For additional information on price matching against competitors (comparing your prices to the right products offered by competitors), see our article on competitive monitoring.
Does the same item appear under three different SKUs in your ERP, your e-commerce site, and your marketplace? This isn’t just a technical detail—it’s a catalog that’s misleading your management tools.
Unlike competitive matching, which compares your products to those of a third party, internal Product matching involves ensuring the accuracy of your own product database: identifying and merging duplicates that accumulate across systems, retail chains, or as a result of a catalog merger.
This article explains Why how these duplicates arise, what they actually cost, and the method for building a clean Product product database: the essential foundation for any analysis of price, margin, or product assortment.
Internal " Product " matching: a distinct challenge from competitive intelligence
When When people talk about “ Product matching” in retail, they often think of comparing prices against competitors’ SKUs. This is a real issue, which we cover in our article on competitive monitoring.
But before you even compare yourself to others, your own catalog must be consistent. That is the role of internal Product matching: to identify and merge records that actually refer to the same item within your own information system.
Why Catalogs are becoming fragmented
Most retailers aren't starting from scratch: their catalog has been built up over time through the accumulation of systems and processes.
A wide variety of sources (ERP, e-commerce, marketplaces)
The same item ( Product ) is often entered separately in the ERP system for inventory management, in the e-commerce CMS for the product page ( Product), and in the marketplace feed for external distribution. Each system has its own nomenclature and its own required fields, and no one verifies that they are indeed referring to the same item.
Mergers, Acquisitions, and Multi-Brand Operations
When a company acquires or merges with another, it brings together two catalogs that were developed independently, each with its own naming conventions. Without a dedicated merger project, duplicates quietly accumulate over the years.
Decentralized Manual Data Entry
When Multiple teams or stores may create a new product listing ( Product ) without checking to see if one already exists; the temptation to recreate rather than search is strong, especially when under time pressure. The catalog is growing faster than its quality is improving.
The Cost of a Poorly Deduplicated Catalog
A duplicate reference isn't just an extra row in a spreadsheet—it skews every decision based on that data Product.
Biased price and margin analyses
If sales of the same item are spread across two or three separate SKUs, your calculationof price elasticity or contribution margin will automatically underestimate the actual weight of the Product. The pricing decisions that result from this are based on flawed data.
Fragmented inventory management
Duplicate entries often mean that inventory levels for the same item are tracked separately, creating a risk that one SKU could run out of stock while another remains overstocked without anyone noticing, due to the lack of a consolidated view.
A distorted product mix and cross-selling
Cross-selling recommendations and shopping cart analyses rely on the purchase "errors " by product reference: duplicates dilute this signal and make the recommendations less relevant, just as poor competitive matching skews a price comparison.
This is the annual loss attributable to poor-quality data reported by more than a quarter of organizations, with 7% estimating it at $25 million or more (Forrester, 2023 data, reported by IBM in January 2026). A " Product " repository riddled with duplicates is an integral part of this problem.
Methodology for Ensuring the Reliability of Your Reference Framework Product
The logic is similar to that of competitive matching, but this time applied to your own data.
1. Define the scope
Don't process the entire catalog all at once: prioritize the best-selling categories or those most at risk (post-merger, multi-brand).
2. Create the single reference sheet
Define the key attributes for each Product : EAN, brand, model, dimensions. This is the single reference record to which everything else must be linked.
3. Dedup across multiple signals
As with external matching, don' You 't rely solely on the EAN code, which is often missing or entered incorrectly: cross-reference the title, brand, and technical attributes to identify duplicates with a confidence score.
4. Merge and trace
Merge the duplicate records into this reference record while retaining a " errors ": which has been merged with What and Why, so you can revert the changes if an error occurs.
5. Lock in future creation
The real challenge is to prevent new duplicates from being created: mandatory searches before creating a record, centralized validation, and naming conventions that are consistent across all systems.
Who should lead this project?
A clean Product repository is not just an IT issue—it is a matter of shared governance.
The category manager is familiar with the products and can resolve ambiguous cases. The data/IT team ensures the technical im Reliability of the deduplication process. The Product , or pricing department stands to gain the most from a clean catalog: it is this department that sets prices and margins based on this data.
Competitive matching compares your products to those of a third party to set your price in response to Market. Internal Product matching, on the other hand, compares existing product records within your own systems (ERP, e-commerce, marketplace) to eliminate duplicates of the same item. Both rely on similar techniques (multi-signal confidence scoring) but address different needs.
The " Product " record is considered the single, reliable reference for a given item, and all duplicates identified in your various systems are consolidated into it. It serves as the foundation for pricing, inventory management, and product assortment analysis.
One-time deduplication is not enough if nothing prevents new duplicates from being created. The creation of duplicates must be prevented at the source: a mandatory search of the repository before any new entry is created, centralized validation, and naming conventions common to all systems that feed into the catalog.
Matching links equivalent references, whether from competitors or within your own systems. Chaining links a reference to its successor (change in code or format). Cloning replicates the behavior of an existing Product for a new Product without errors. See matching, cloning, and chaining.
When If the EAN is missing or differs (private label, formats, lots), the matching process relies on other indicators, such as the manufacturer’s reference, attributes (brand, volume, variant), and product description—which is analyzed using natural language processing to identify the same Product under two different names. Each match is assigned a confidence score. Only high scores are processed automatically; ambiguous cases are reviewed by a human. Finally, prices are converted to a common unit (kilogram, liter, piece) before being compared.
Automating this deduplication process—rather than performing it manually on a project-by-project basis—is precisely the purpose of BOOPER’s product matching and chaining module: a single, reliable reference record per Product, continuously updated, and applied to both your own catalog and competitive comparisons.
For the external aspect of this topic—comparing your prices to those of quality products offered by competitors—check out our article on competitive monitoring.
Reversibility is negotiated before the contract is signed, never after. Once the contract is signed and the system is live, the balance of power has completely shifted. Four assets are at stake: your raw data, your rule set, your arbitrage errors , and your competitive matching. Only the first is generally covered by contracts.
TheGap between perception and reality is well documented: 89% of executives believe they can switch suppliers in less than a month, but among those who have tried, 58% report failure or an effort that was much greater than expected (Zapier, 2026). The goal is not to leave, but to have the option to do so: this is what maintains a healthy supplier relationship throughout the term of the contract.
Pricing decisions account for only a small portion of the workweek in most organizations. The rest is divided among preparation, coordination, formatting, and justification. This time is not wasted: justifying a price to a buyer is real work. The problem is that it takes the place of what creates value.
The measurement is based on a reconstructed typical week, not on a self-reported estimate: the “Gap ” between the two is commonly a factor of two. The correction then follows a specific order: eliminate, then automate, then delegate. Automating a useless task is tantamount to making it permanent.
The accumulation of rules is a cumulative process, not an isolated incident: it grows with every legitimate decision made in isolation, even though no single decision is open to criticism. Five forms predominate: the exception that has become permanent; the seasonal rule that is never suspended; rules that contradict one another; the orphaned rule whose author has left; and the redundant rule.
The cost isn’t what we think it is: before it eats into profits, debt erodes trust. A team that no longer understands Why —the driving force behind pricing—will stop following it. The solution lies in a consistent rhythm rather than a major cleanup effort every three years: a quarterly portfolio review, four questions per rule, and an expiration date set from the outset.