Data cleaning for CRM imports
Dedupe
Find the fuzzy duplicates Excel can't see
Excel's Remove Duplicates only catches exact matches. So "Abraham" and "Abrahamm" both survive, "Jon Smith" and "Jonathan Smith" with the same phone number both survive, and your list stays quietly full of near-duplicates. The duplicates that actually pollute a CRM are the ones a byte-for-byte comparison never sees. Sift finds them in your browser, groups them for review, and merges each group into one record. Nothing is uploaded.
Find fuzzy duplicates in Sift, free →
The manual fixes (and why they hurt)
Excel can do fuzzy matching, but not out of the box. Every native route asks you to build the matcher yourself before you can use it:
- Microsoft's Fuzzy Lookup add-in. You install the add-in, build a self-join of your table against itself, and tune a similarity threshold until it stops missing real duplicates without flagging everyone. It works, but it is famously fiddly to set up, and the threshold is guesswork on a list you haven't seen yet.
- Power Query M code. The no-add-in route is to write M, comparing records with a distance function and grouping the matches. That is real code to write, test, and maintain, for a one-off cleanup most people just want done.
- A paid add-in like Ablebits. Ablebits sells a duplicate finder that handles similar entries. It is a solid tool, but it costs money and still lives inside Excel, which means the same CSV-mangling and manual-merge problems once you export.
As one r/excel poster described it, they had a column of 40,000 names full of "typos or very similar duplicate entries but they aren't exact match, like Abraham & Abrahamm". Remove Duplicates does nothing for that. And even when Excel does find a match, it deletes the second row rather than merging the best values from both into one record.
Find fuzzy duplicates in Sift
No add-in, no Power Query M code, no threshold to guess at blind. Sift groups similar records for you and lets you review before anything merges:
- Load the file. Sift profiles every column in your browser; nothing is uploaded.
- Choose what to match on: email, phone, or a name-and-company fingerprint, depending on which field is your best identifier.
- Fuzzy matching catches the near-misses a byte-for-byte comparison drops: "Abraham" and "Abrahamm", "Jon" and "Jonathan", casing and spacing noise. It works on a similarity fingerprint, so it is not perfect, which is why the next step exists.
- Review each cluster with a before/after diff. You see the grouped rows side by side and decide whether they really are the same person or company before anything changes.
- Merge to a golden record with survivorship rules, keeping the phone from one row and the job title from another, instead of deleting a row and losing data.
- Export a clean, deduped file ready for your CRM import.
When Excel's exact Remove Duplicates is enough
Fuzzy matching is the wrong tool when your duplicates really are exact. If a list has literal copy-paste repeats, the same row entered twice with identical values, then Excel's Remove Duplicates handles it in one click and there is nothing to review. Reach for fuzzy matching only when the duplicates are near-misses: typos, nicknames, casing, or the same person under two slightly different spellings. Sift does exact dedupe too, so you can start with the safe exact pass and only bring in fuzzy matching where the near-duplicates actually live.