I work in the non-profit sector and use the Salesforce data cleaning tool to remove duplicates. However, duplicates often occur because donors use aliases (for example, different names or email addresses) and some choose to remain anonymous. What data cleaning strategies do you use to handle these challenges? Our team is small, and we have around 5,000 to 10,000 records to clean.
Feel free to ask me anything to provide better context of my situation!
I’d avoid relying on exact name/email matching alone, especially with donors who use aliases or want to stay anonymous. I’d start with a few trusted matching signals—email/domain, phone, address, household info, and name variations—and assign different confidence levels before merging anything.
For 5,000–10,000 records, I’d also do a staged cleanup: identify likely duplicates first, review the uncertain matches manually, and keep anonymous records out of aggressive auto-merge rules.
If the native Salesforce tools aren’t giving you enough flexibility, something like LeadAngel can help with fuzzy matching and custom matching rules across those different signals. The key is to flag uncertain matches rather than automatically merge them, especially for donor data.
