Data cleaning for CRM imports
Dedupe
Remove duplicates from a CSV or mailing list, free
To remove duplicates from a CSV or mailing list: open the file in Sift, pick a match key (email, phone, or a name and company fingerprint), choose exact or fuzzy matching, review the duplicate clusters with a before and after diff, and merge each into one record. Then export a clean file. It's free, it runs entirely in your browser, and nothing is uploaded.
Dedupe your CSV in Sift, free →
The manual fix (and where it stops)
Excel and Google Sheets both have a built-in way to strip duplicate rows, and for a truly clean list they work. In Excel it's Data → Remove Duplicates: pick the columns to compare on, and it deletes any row that repeats. In Google Sheets the equivalent is =UNIQUE(range), which returns only the distinct rows.
Both share the same two limits. First, they only catch exact, character-for-character matches. The duplicates that actually pollute a mailing list are rarely exact: the same person shows up as "Bob" and "Robert", as "Jon Smith" and "Jonathan Smith" on the same phone number, and the same email in two different casings. Exact-match dedupe keeps every one of them. As one CRM admin described the manual approach on Reddit, companies handle dedupe "with Excel and building formulas... you are going to end up missing quite a few if you use Excel."
Second, when they do find a duplicate, they delete the duplicate row. If one row has the phone number and the other has the job title, you lose whichever row Excel drops. There's no merging two half-complete rows into a single, more-complete record.
Remove duplicates in Sift
- Load the file. Drop your CSV or Excel file in and Sift profiles every column in your browser; nothing is uploaded.
- Pick a match key: email, phone, or a name and company fingerprint, whichever best identifies the same person or account.
- Choose exact or fuzzy. Exact catches identical values; fuzzy also catches near-matches like Bob and Robert or Jon Smith and Jonathan Smith.
- Review the clusters. Sift groups suspected duplicates together and shows a before and after diff so you decide what actually merges.
- Merge into one golden record with survivorship rules, so the best value from each row is kept instead of deleting a row and losing its fields.
- Export the clean file, and if you like, save the whole dedupe as a reusable pipeline.
When a simple exact-match tool is enough
Not every list needs fuzzy matching or survivorship. Reach for Excel's Remove Duplicates or Google Sheets UNIQUE when:
- The duplicates really are exact, character-for-character copies of the same row.
- The list is small enough that you can eyeball it and confirm nothing is being lost.
- You don't need to merge fields from two rows; deleting one of them is fine.
Once the duplicates are near-matches, or two rows each hold a field you want to keep, an exact-match tool stops short. That's the point where fuzzy dedupe and golden-record merging earn their place.