Latest articles and updates from MatchCustomerNames
Learn how to clean and standardize customer names, fix Excel data quality issues, and maintain privacy while automating data cleaning workflows. Our guides cover everything from pivot table errors to GDPR compliance for data cleaning tools.
Our name matching system uses three complementary algorithms (character-level, token-set, and sequence alignment) combined with statistical filtering to achieve 90% accuracy in identifying duplicate customer names.
Matthijs de Bock
Co-Founder
Using AI to clean your spreadsheets feels like magic, but it comes with a major privacy risk. Learn how to clean your data intelligently without giving away your customer secrets.
Martijn Smeets
Co-Founder
Pivot tables break when customer names don’t match exactly. Learn how to fix spelling errors across your entire list without finding and replacing them by hand.
Martijn Smeets
Co-Founder
Excel can't reliably match similar names or remove duplicates. Learn how to clean Excel data faster using tools that think like a human, not a robot.
Martijn Smeets
Co-Founder