Datagma gives you contact data, but the export it hands back rarely matches what your CRM wants to ingest. Column names drift between plans, the order is whatever Datagma felt like, and you end up renaming headers by hand before every import. This tool takes that export and reshapes it into one fixed, predictable schema so you can drop it straight into your pipeline.
Drop your CSV on the zone (it’s parsed in your browser — nothing gets uploaded). The converter maps the columns it recognises onto a stable set: first_name, last_name, full_name, company, title, email, phone, linkedin_url, source, date_added. So a Datagma header like Job title or Position lands in title, Company name lands in company, and Email 1 or Mobile get normalised too. Every row is stamped with source = Datagma and today’s date in date_added. One click downloads the cleaned file as yourfile-fst-cleaned.csv.
One honest limit: Datagma exports vary by plan, and the converter only maps headers it knows. If your file uses a column name that isn’t in the map, that target field comes out empty rather than erroring — no row is dropped, but the data isn’t guessed at either. The preview shows you how many target columns actually got filled (mapped to target), so check that number and open the first row before you convert a big batch.