Key Takeaways
- The free fuzzy matching tool compares two lists and returns a similarity score for every match.
- Every pair gets a clear percentage score, so you can see exactly how close two values are.
- Everything runs in your browser; nothing is uploaded.
- For full dataset matching inside Google Sheets, Flookup adds phonetic and semantic matching.
Introduction
"John Smith" and "Jon Smyth". "Acme Corporation" and "Acme Corp". "Miriam K." and "Miriam Kamau".
Exact matching sees four different people. A human sees the same record written four ways.
We built a free fuzzy matching tool to settle those comparisons instantly: paste two lists, press Match and see how similar every value is, as a percentage, in your browser, with nothing uploaded. Open the Fuzzy Matching Tool.
What Is Fuzzy Matching?
Fuzzy matching is the process of finding values that are similar but not identical. Instead of asking "are these two strings exactly the same?", it asks "how close are these two strings?" and returns a score.
That one question is behind a huge share of real data work: cleaning customer lists, reconciling vendor names, merging leads from two systems and deduplicating CRM records.
What the Tool Does
The tool supports two modes:
- Best match: for every item in list A, find the closest item in list B and its similarity score.
- Near-duplicates: within a single list, find every pair that looks similar.
You can set a similarity threshold, see a summary of how many values matched and download or copy the results as a table.
How Similarity Is Scored
Every pair of values is compared and given a single percentage score, so you can see at a glance how close two entries are. The score is high for values that look alike and low for unrelated ones.
Set a threshold to keep everything above it as a likely match and leave the borderline cases for a quick human check.
Reading the Scores
| List A | List B | Score | What it means |
|---|---|---|---|
| Jon Smith | John Smith | 91% | Likely the same person; a typo explains the difference |
| Acme Corp | Acme Corporation | 87% | Same company written with an abbreviation |
| Smith John | John Smith | 85% | Same name, different word order |
| Robert | Rob | 78% | Nickname relationship; decide with a lower threshold or manually |
| Apple | 18% | Unrelated values that only a low threshold would join |
These example scores show why a threshold matters. At 85%, Jon Smith and John Smith match, Acme Corp and Acme Corporation match and Smith John versus John Smith matches too. Robert versus Rob falls below the line, which is often what you want: it flags a possible link without quietly merging two different people.
Two Modes and When to Use Each
Best match mode is for comparing two distinct lists, such as a sales list against a master customer list. It answers "for every item in A, what is the closest item in B and how close is it?" Use it when you are joining two sources.
Near-duplicates mode is for finding duplicates inside one list, such as a single CRM export full of double entries. It answers "which pairs within this list look like the same record?" Use it when you are deduplicating one source.
Privacy First
Your lists are processed in a background worker inside your browser. They are never sent to a server, never stored and never used for anything else. That makes it safe to compare customer lists and other sensitive data.
How to Use It
- Paste list A (one value per line) and list B.
- Set a similarity threshold. 85% is a good starting point for names.
- Press Match and review the scores or open the near-duplicates mode for a single list.
- Download or copy the results.
Troubleshooting Common Issues
- Too many matches: raise the threshold. Values in the 70 to 84 percent band are often nicknames or shared words rather than true duplicates.
- Too few matches: lower the threshold, then review the borderline rows by hand rather than trusting them blindly.
- Empty or misaligned rows: the tool treats each line as one value, so blank lines create empty entries. Trim trailing blank lines before pasting.
- Header rows included: if your list came from a spreadsheet, remove the header row first or it will be compared like any other value.
When to Use Something Stronger
The tool compares text similarity. Real datasets often need more: matching across an entire spreadsheet, handling abbreviations and word order or reconciling thousands of rows against a master list. That is what Flookup Data Wrangler does inside Google Sheets, with configurable thresholds, phonetic matching and AI-powered semantic matching. See the data cleaning tools for the full range.
Final Thoughts
The Fuzzy Matching Tool is the fastest way to answer "are these the same?" Paste two lists, set a threshold and every value comes back with a percentage score you can act on. Because everything runs in your browser, it is safe for customer data, lead lists and any other sensitive comparisons.
Use it for quick checks and one-off merges. When matching needs to happen inside Google Sheets at dataset scale, with phonetic and semantic matching, scheduled runs and a similarity score on every row, Flookup Data Wrangler is the next step.