How to Check a Lead List Before You Import It
The twenty minutes before an import are the cheapest in the whole process. Check where the list came from and when, how many phone numbers are actually valid, how many rows you already have, and whether it is B2B at all, then import knowing what it is.
- Time
- 20 minutes
- Steps
- 6
- You'll need
- 3 things
- Topic
- Data
Why bother
The most expensive moment in a bad import is the hour after it, when reps start dialling a file nobody looked at. Everything you can learn before that point is close to free, and the things that go wrong are boringly consistent: dead phone numbers, duplicates of leads someone is already working, and rows that were collected so long ago the contact has forgotten the enquiry. None of that is visible once the rows are mixed into the pipeline.
The procedure
6 steps, about 20 minutes.
- 01
Ask where it came from and when it was collected
Before opening the file, get two facts from whoever supplied it: the source, and the collection date. Age is the single strongest predictor of connect rate on a bought list, and it is almost never in the file itself. If nobody can answer the second question, treat the whole list as old.
- 02
Check phone validity, not phone presence
A populated phone column tells you nothing. Count how many values are ten digits, start with a valid Indian mobile prefix, and are not obviously repeated placeholder numbers. A file where a third fails this is not a rep-effort problem; it is a file problem, and no amount of dialling fixes it.
- 03
Count duplicates against what you already have
Duplicates inside the file are easy. The expensive ones are duplicates of leads a rep is already working, two people calling the same prospect from different lists is how you lose a deal you were winning. Match on normalised phone, not on name or email.
- 04
Decide whether it is B2B at all
Look at the company column. If most rows are blank or look like individuals, you have a consumer list, whatever it was sold as. That changes what you do with it entirely, and it is better discovered now than after a week of calls.
- 05
Check fit before quality
Take twenty rows at random and ask whether you would want them as customers, sector, size, geography. If the answer is mostly no, the data quality is irrelevant. A clean list of the wrong companies is still the wrong list.
- 06
Import, then measure by band
Once imported, watch connect rate by freshness band and grade distribution by source rather than raw totals. Two lists with identical row counts can differ enormously in what they produce, and this is the only way to tell before the money is spent again.
Where it goes wrong
- Judging a list by row count. Volume is the least informative number in the file.
- Treating completeness as quality. A fully-populated row with a fake number is worse than a sparse one with a real decision-maker.
- Importing without recording the source, which makes it impossible to tell later which purchase was worth repeating.
- Blaming reps for a poor connect rate on a list nobody validated.
Questions
Ready when you are
Or have it done in less than 20 minutes.
Leadkaun ships this as a default: graded leads, a ranked queue per rep, and the morning brief. Import a CSV and it runs the same afternoon.
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