Catalog Overlap Analyzer
Compare two product catalogs in your browser. See shared, unique, and near-match SKUs instantly. No upload, no login, no file-size limit.
Paste or upload two product files and the Catalog Overlap Analyzer will compare two product catalogs side by side, showing you which items are shared, which are unique to each source, and which are near-matches hiding behind different SKUs or descriptions. It is built for distributors reconciling a supplier feed against existing inventory, but works for any two CSV or spreadsheet exports.
Catalog Overlap Analyzer
The interactive version of this tool is coming soon. It will run entirely in your browser — no login, no upload limits.
Planned tool: compare two product catalogs
Need this now? Talk to ClaroWhat it checks
The analyzer takes two files (your catalog and a comparison catalog) and computes a line-by-line overlap report:
- Exact matches — rows that share a normalized identifier such as GTIN, manufacturer part number (MPN), or supplier SKU.
- Near-matches — rows with no shared identifier but high similarity on brand, MPN, and description, the kind of pairing a manual review would catch.
- Unique to file A — items in your catalog with no counterpart in the comparison file (potential discontinuations or gaps in the supplier range).
- Unique to file B — items in the comparison file you do not yet carry (sourcing or assortment opportunities).
- Overlap rate — the share of each file that maps to the other, so you can size the reconciliation effort before committing to it.
- Identifier coverage — how many rows actually carry a usable key (GTIN, MPN, UPC), which is usually the real reason two catalogs look further apart than they are.
How it works
There is no universal product key shared across suppliers, so overlap detection happens in two passes. First, a deterministic pass normalizes candidate identifiers — trimming whitespace, stripping separators in part numbers, validating and re-encoding GTINs — and joins rows that share an exact key. This catches the clean matches with full confidence.
The second pass uses fuzzy matching for everything left over. Brand, MPN, and description are tokenized and scored with string-similarity measures (the same Levenshtein and Jaro-Winkler family used in record linkage), and pairs above a similarity threshold are surfaced as near-matches for you to confirm. This is the standard deterministic-then-probabilistic approach to comparing catalogs that lack a common identifier.
- 1Load both filesPaste rows or upload two CSV/TSV exports — your catalog as file A, the comparison catalog as file B.
- 2Map key columnsPoint the tool at your identifier and description columns. It auto-detects common headers like gtin, mpn, sku, and brand.
- 3Review the overlap reportSee exact matches, near-matches, and unique items, with the overlap rate and identifier coverage summarized at the top.
A browser tool is ideal for a one-off comparison or a spot check before a project. When you need to compare two product catalogs continuously — across dozens of suppliers, with confidence scores, reversible merges, and provenance on every match — that is a job for a canonical product-data layer. Claro’s identity resolution and catalog matching engine runs the same deterministic-plus-probabilistic logic at production scale with full audit trails.
Related resources
Playbook
Match Supplier Catalogs to Your Inventory
A step-by-step workflow for reconciling an incoming supplier feed against your master catalog.
Guide
Reconcile 50 Supplier Catalogs Into One Inventory
How to consolidate many overlapping supplier files into a single clean inventory.
Glossary
What Is Fuzzy Matching?
The technique behind near-match detection when two catalogs share no common key.
Tool
SKU / MPN Cross-Reference Builder
Build a clean cross-reference between supplier SKUs and manufacturer part numbers.
Playbook
Find Cost-Saving Alternative Suppliers
Use catalog overlap to identify equivalent items from lower-cost sources.
FAQ
How do I compare two product catalogs that use different SKUs?
Match on a manufacturer-controlled identifier instead of the SKU. GTIN or MPN stays constant across distributors, so the analyzer normalizes those first and joins on them. Rows with no shared identifier fall through to the fuzzy pass, which scores brand, MPN, and description similarity and flags likely pairs for you to confirm.
What file formats can I upload?
CSV and TSV exports, plus pasted tabular data copied from a spreadsheet. Most catalog, ERP, and PIM systems can export to CSV. The tool auto-detects delimiters and common header names, and lets you map columns manually if your headers are non-standard.
Is my supplier and pricing data safe?
Yes. Every step runs locally in your browser — files are parsed and matched in JavaScript and never sent to a server. That means there is no file-size limit imposed by an upload, and confidential cost or pricing columns stay on your machine.
What overlap rate is normal between two distributor catalogs?
It varies widely by category and how clean the identifiers are. Two distributors in the same vertical often share a large core of common manufacturer items, while private-label and regional lines stay unique. The more important number is usually identifier coverage: if many rows lack a GTIN or MPN, your true overlap is higher than the exact-match count suggests, which is why the near-match pass matters.
When should I move from a browser tool to a matching platform?
Use this tool for one-off comparisons and pre-project sizing. Move to a platform when you need to compare catalogs on a recurring basis, apply consistent confidence thresholds, keep a reversible audit trail of every merge, or push matched results back into your PIM or ERP automatically.