How to remove duplicate customers from a spreadsheet by email address
A customer list that has been fed by sign-up forms, imports and the odd copy-paste ends up with the same person on it two, three, four times. Each copy usually differs a little: another order date, a city typed after a move. Mail-merge that list and the same person gets the same email three times. Count it and your customer number is inflated. The usual "remove duplicates" button does not catch…
Customer lists often contain multiple entries for the same person due to various data sources. These duplicates can skew statistics and lead to redundant communications. To remove these duplicate entries, focus on a unique identifier, such as an email address. Below are the steps to clean a spreadsheet in the osFoundry desktop app.
1. Open the customer list in the Spreadsheet Editor by dropping the .xlsx file onto the app. Select the column containing email addresses.
2. Apply conditional formatting to highlight duplicate values in the selected column. This will visually indicate all email addresses that appear more than once, turning them red.
3. Manually review the red-highlighted rows to confirm they represent duplicate customers. Some cases may involve legitimate cases of sharing an email address, thus requiring further decision-making.
4. Select the column with email addresses again, then navigate to the Data tab and click on Remove Duplicates. The dialog box will appear, where under My data has headers, the ticked box ensures the header row is not treated as data.
5. In the Columns field, type 'A' to indicate that only the email column should be considered for duplicate detection.
6. Confirm by clicking OK. The app will retain the first instance of each email address, deleting any subsequent duplicates while preserving other columns like name, city, and last order date.
7. Repeat the process by sorting the list by 'Last order' in descending order before removing duplicates, if you prefer to keep the most recent entry for each customer.
8. Upon completion, the count of entries should decrease significantly. For example, a list initially containing 412 customers might now show only 375 unique entries, with the 38 duplicates removed.
9. The red highlighting from the duplicate values rule will disappear once duplicates are eliminated, signifying a clean list with no redundant email addresses.
10. Keep the duplicate values rule active for future data imports to automatically flag any new duplicates as they are added. The osFoundry app is designed for local-first data management and is available for free on the osFoundry download page.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
