Your data has "John Smith" in one file and "Jon Smtih" in another. VLOOKUP returns nothing. Manual matching takes hours. This guide shows how to match messy columns automatically using fuzzy matching.
The problem with exact matching
VLOOKUP and similar functions require exact matches. Real-world data rarely matches exactly. Names have typos. Companies use abbreviations. Formatting varies between systems.
| Issue | File A | File B |
|---|---|---|
| Typos | John Smith | Jon Smtih |
| Abbreviations | IBM Corporation | IBM |
| Spacing | Apple Inc. | Apple Inc. |
| Case | MICROSOFT CORP | Microsoft Corp |
| Format | 555-123-4567 | (555) 123-4567 |
None of these would match with VLOOKUP. You'd need to clean both datasets manually, standardize formatting, then still miss variations you didn't anticipate.
Fuzzy matching: the solution
Fuzzy matching algorithms compare text similarity rather than requiring exact equality. They calculate how "close" two strings are and assign confidence scores. "John Smith" and "Jon Smtih" might score 87% similarity - different but clearly the same person.
Step-by-step: matching with MergeItAI
Step 1
Prepare your files
Export both datasets to Excel or CSV. You need at least one column to match on - typically names, company names, or identifiers. No cleaning required.
Step 2
Upload both files
Go to app.mergeitai.com and upload your files. Supports .xlsx, .csv, and .xls formats up to 50,000 rows.
Step 3
Select columns to match
Choose which column from each file should be compared. For better accuracy, you can match on multiple columns (e.g., Name + City).
Step 4
Run matching
Click match. The algorithm compares every entry, handles typos and variations automatically, and assigns confidence scores. Completes in under 30 seconds.
Step 5
Review and export
Review matches sorted by confidence. Verify lower-confidence entries manually. Export to Excel, CSV, or JSON with all original columns plus match scores.
Tip: Set a minimum confidence threshold (e.g., 80%) to automatically filter out weak matches. Review entries between 70-90% manually.
Example results
| File A | File B | Score |
|---|---|---|
| Microsoft Corporation | MSFT | 94% |
| Apple Inc. | Apple Inc | 98% |
| Jon Smith | John Smtih | 89% |
| IBM Corp | International Business Machines | 87% |
Common use cases
Sales: CRM reconciliation
Match customer names from Salesforce with accounting data when sales reps abbreviate company names differently.
HR: Employee records
Merge payroll, benefits, and performance data where names have typos or formatting differences.
Finance: Vendor matching
Reconcile invoices with purchase orders when vendor names vary between systems.
Marketing: List dedup
Find duplicate contacts across multiple sources with slight name or email variations.
Frequently asked questions
What is fuzzy matching?
Algorithms that find similar (not identical) text. They handle typos, abbreviations, spacing, and formatting differences by calculating text similarity scores.
How accurate is it?
95%+ accuracy on typical business data. Every match includes a confidence score for verification. You control the threshold for what counts as a match.
Can I match multiple columns?
Yes. Matching on Name + City, for example, improves accuracy when names alone might be ambiguous.
What file formats work?
Excel (.xlsx, .xls), CSV, and TSV. Up to 50,000 rows per file.
Is my data secure?
Encrypted in transit and at rest. Files deleted after processing. No permanent storage of your data.