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Fuzzy Matching

Data managementIntermediate Level

Fuzzy matching finds text or records that are similar but not identical, using a similarity score to catch typos, abbreviations and word order differences.

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What is Fuzzy Matching?

Fuzzy matching is a technique that finds text or records that are similar but not exactly the same, such as "Samsung Galaxy S24 Ultra 256GB" and "Galaxy S24 Ultra (256 GB) Samsung". Instead of a yes or no, a fuzzy match gets a similarity score, and anything above a chosen threshold counts as a match. Common methods include edit distance (how many characters you must change, as in Levenshtein distance), token matching (comparing words regardless of order) and phonetic matching (words that sound alike). Exact matching only links identical values, such as the same EAN; fuzzy matching catches typos, abbreviations, word order and spacing differences. In e-commerce it is used to find duplicate products, match supplier data to an existing catalog when identifiers are missing, standardize attribute values and make site search tolerant of typos. Our guide to product data deduplication explains how to combine it with exact checks.

Why Fuzzy Matching matters for e-commerce

Product data comes from many sources that name the same thing in different ways. Without fuzzy matching, those differences create duplicate products, messy filters with five spellings of one color, and supplier updates that cannot be linked to the right item. Fuzzy matching also helps shoppers. Site search that tolerates typos and word order finds products that exact search would miss.

Examples of Fuzzy Matching

  • 1Value A: Samsung Galaxy S24 Ultra 256GB, Value B: Galaxy S24 Ultra (256 GB) Samsung, Result: Likely the same product
  • 2Value A: Navy blue, Value B: navy-blue, Result: Same attribute value, standardize to one spelling
  • 3Value A: Search for "addidas", Value B: Brand "adidas", Result: Search shows adidas products
  • 4Value A: Supplier item "Drill 18V w/ battery", Value B: Catalog item "Cordless drill 18 V with battery", Result: Suggested match for review
  • 5Value A: T-shirt size M, Value B: T-shirt size L, Result: High score but different products, so not a duplicate
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How WISEPIM Helps

  • Review all values of an attribute on the Attribute values page in WISEPIM and spot spelling variants of the same value.
  • Standardize those variants in one step with bulk editing.
  • Match imported rows to existing products by identifiers such as SKU or EAN before anything else.
  • Use Quality Guard rules to flag products with missing identifiers that make matching harder.

Common mistakes with Fuzzy Matching

  • Setting the threshold so low that different products are treated as duplicates.
  • Merging matches automatically without a review step.
  • Using fuzzy matching where an exact identifier such as an EAN is available.
  • Comparing raw text without normalizing case, spacing and units first.

Tips for Fuzzy Matching

  • Match on exact identifiers first and use fuzzy matching only for what is left.
  • Normalize text before matching: lowercase, remove punctuation and standardize units.
  • Test thresholds on a sample with known answers.
  • Send borderline scores to a person instead of deciding automatically.

Trends around Fuzzy Matching

  • Semantic matching with AI embeddings that compares meaning, not only characters.
  • Matching across languages, so a Dutch and an English product name can be linked.
  • Image similarity to confirm product matches.
  • Human review of borderline matches built into the matching workflow.

Tools for Fuzzy Matching

  • OpenRefine: Free tool with clustering to find and merge similar values.
  • RapidFuzz: Python library for fast fuzzy string matching.
  • Elasticsearch: Search engine with fuzzy queries for typo-tolerant search.
  • Algolia: Hosted search with built-in typo tolerance.
  • WISEPIM: PIM with an Attribute values page and bulk editing to standardize similar values.

Related Terms

Attribute CollisionCanonical Data ModelCircular Economy Data ModelCross-Border Trade (CBT) AttributesData ContractData Decay

Also Known As

Approximate string matchingFuzzy matchProbabilistic matchingInexact matching

Frequently Asked Questions

A fuzzy match is a pair of values that are not identical but similar enough to probably mean the same thing, such as "Nike Air Max 90" and "Air Max 90 Nike". The similarity score decides whether a pair counts as a match.

Exact matching only links values that are character for character the same, such as two identical EAN codes. Fuzzy matching also links values that differ by typos, spacing, abbreviations or word order. Many processes use exact matching first and fuzzy matching for what is left.

Test on a sample where you know the right answers. A low threshold finds more matches but also more false ones; a high threshold misses real matches. Pick the point where false matches are rare, and send borderline scores to a person for review.

It can flag likely duplicates, but merging them automatically is risky, because similar names can be different products, such as two sizes of the same item. Review suggested matches before you combine records.

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