What these tools are for
Most CRM cleanup starts with a CSV: you export contacts from HubSpot, Salesforce, Klaviyo, Mailchimp, or Shopify, fix what's wrong, and import the file back. These free tools cover the steps of that loop that go wrong most often: finding duplicate rows, checking a file against the platform's import rules before it fails halfway, putting a dollar figure on what duplicates cost, and generating realistic test data so you can try a cleanup process without touching real customer records.
Everything runs in your browser. There's no account to create, and the tools that read a file never send it anywhere: close the tab and it's gone.
Which tool to use when
- Before you trust channel reports. The Attribution Readiness Checker scores an export on source coverage, catch-all values, first-touch fields, UTMs, and the duplicates that split one person's history across records.
- Sizing the problem. The Duplicate Cost Calculator estimates what duplicate records cost you each year in contact tiers, storage, or wasted sends, using your platform's list pricing.
- Cleaning an export. The CSV Dedupe Tool finds rows that match on the columns you choose, with exact or simple fuzzy matching, and gives you a deduplicated file plus a file of the duplicates for review.
- Before you re-import. The CRM Import Checker runs your file against the target platform's rules: required columns, file and row limits, header names, email format, date formats, and the damage spreadsheets do to ZIP codes and long numbers.
- Testing a cleanup tool or process. The Test Data Generator builds a contacts CSV with planted duplicates and messy fields, plus an answer key, so you can grade what any tool catches and misses.
What the free tools don't do
Exact and simple fuzzy matching catch re-imports, copy-paste doubles, and formatting drift. They can't tell that Jon Smyth at Acme and Jonathan Smith at ACME Industrial are the same person. That takes semantic matching, which is what SmartMatch in CleanSmart does, alongside formatting, filling gaps, and validation across the whole dataset in one reviewed pass.