Duplicate SKU Finder

Paste a column of SKUs or upload a CSV to find duplicate SKUs instantly — exact repeats, plus near-duplicates that hide behind inconsistent casing, padding, and punctuation. It runs entirely in your browser, so nothing leaves your machine.

What it checks

The Duplicate SKU Finder groups your identifiers and surfaces every collision it can detect, with a plain-language reason for each:

• Exact duplicates — identical SKU strings repeated across rows, the simplest and most common case.
• Normalized duplicates — values that are the same once you strip leading/trailing whitespace, collapse case, and remove separators like hyphens, dots, slashes, and spaces (for example ABC-1024, abc1024, and ABC 1024).
• Zero-padding collisions — numeric SKUs that differ only by leading zeros, such as 004500 vs 4500, which Excel and ERP exports routinely mangle.
• Whitespace and hidden-character issues — trailing spaces, tabs, and non-breaking spaces that make two “identical” SKUs sort apart in a spreadsheet.
• Near-duplicates — SKUs one keystroke apart (transposed digits, a dropped or extra character) and, when you map a title or MPN column, different SKUs that share an MPN or carry near-identical titles. These often signal a fat-fingered re-key rather than a genuinely distinct part.
• Duplicate counts and row references — how many times each value appears and where, so you can jump straight to the conflicting rows.

How it works

There is no official standard for SKU formatting — unlike a GTIN, a SKU is an internal identifier each company defines for itself. That freedom is exactly why duplicates accumulate: a furniture retailer might key the same chair as OAK-DESK-01 and oakdesk1, an MRO distributor might import GLOVE-L from one supplier and GLOVE_L from another, and a CPG team migrating between systems can double-load a row when an export is re-run.

To find duplicate SKUs reliably, the tool applies the same logic a deduplication pipeline uses, in two passes:

1. 1
   Normalize

   Each SKU is trimmed, lowercased, and stripped of common separators and hidden characters to produce a comparison key. Identical keys are grouped as exact-or-normalized duplicates.

2. 2
   Compare for near-matches

   Remaining values are compared for near-misses so transpositions and dropped characters surface as likely-but-not-certain duplicates for you to review, rather than being silently merged. A single changed character is how sequential SKUs differ (SKU-10001 vs SKU-10002), so those pairs are not flagged on the SKU alone; they show up as similar titles when you map a title column and the titles agree.

This is a fast, honest first pass. It is intentionally conservative: it flags candidates and explains why, but it will not auto-merge records. Deciding which of two duplicates is canonical — and merging them reversibly without losing pricing, supplier, or transaction history — is the harder problem that a canonical product record and a real entity-resolution layer are built to solve. Claro’s deduplication and identity-resolution platform does this across millions of records with full provenance and write-back, so a flagged duplicate becomes a tracked, reversible merge instead of a one-off spreadsheet edit.

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