Reconcile the batch
Record the intended row count, deliberately excluded rows, and source filename. Compare these with what the application reports or what you can inspect. A difference calls for investigation, not an immediate conclusion that data was lost. Blank records and validation rules may explain why fewer rows became usable leads.
Inspect difficult examples
Check more than the first row. Review long names, accented characters, international numbers, punctuation, and missing optional fields. Compare each with the source file. These examples expose mapping and formatting errors that a total cannot reveal. Keep a small repeatable sample so future imports can be checked in the same way.
Check repeat submissions
Confirm that nobody uploaded the file again while the first batch was processing. Look for repeated test records and establish a clear retry rule. With a queued import, submitting a request and completing a batch are separate states. Use the processing feedback before assuming another upload is needed.
Tell the team what is ready
Record the checks performed and any remaining issues. In Happitap, inspect the resulting leads before scheduling follow-up work. A concise validation note helps the team avoid treating uncertain records as ready for contact. Preserve the original file until discrepancies are resolved and the batch is accepted.
Continue reading
Guide / Lead data
How to prepare a lead spreadsheet for CSV import
A practical guide to cleaning names, email addresses, and phone numbers before importing a lead spreadsheet.
Read the guide →Guide / Lead data
The minimum useful lead record for a small team
Start with enough information to act, then add detail when it becomes useful.
Read the guide →Guide / Lead data
How to review duplicate leads before an import
Use repeatable matching rules and review uncertain matches before combining records.
Read the guide → Browse all guides →