Import is a commitment point. Clean before it.
Once a list is inside your sender, campaign pressure makes it much harder to stop and clean it. Someone is ready to launch. The sequence is configured. The copy is ready. At that moment, removing records feels like losing work.
That is exactly when teams rationalize sending records they should not send. The pre-import step creates the right kind of friction — before the weak records are inside the system, not after.
Why lists need cleaning before every import.
No source consistently produces clean lists. Database exports go stale. Enrichment tools introduce inaccuracies. Scraped data includes generic inboxes and duplicate records. CRM contacts accumulate over time and may not reflect current email status.
| Source | Common quality problems |
|---|---|
| Apollo or ZoomInfo export | Invalid emails from stale contact data, role-based inboxes, duplicates across lists |
| LinkedIn Sales Navigator | Catch-all domains from company email patterns, work emails that changed after job changes |
| Web scraping | Generic inboxes (contact@, info@), outdated domains, emails that never belonged to a person |
| CRM export | Contacts added years ago, departed employees still in the system, emails verified in a prior tool |
| Manual list | No consistent format, typos, addresses from business cards or event signups |
| Purchased list | Unknown verification date, high proportion of role-based and invalid addresses |
Verification is not a one-time step. It is a standard gate that runs each time a list moves from any source into any sender.
What to clean — before every import.
Pre-import list cleaning has four stages. All four apply before any list enters a sender, CRM, or sequence.
Normalize the list.
Before verification, the list should have consistent formatting: lowercase email addresses, no trailing spaces, no duplicate rows, consistent column structure. Most verification tools expect clean input and return cleaner results when the input is normalized.
Deduplicate.
Remove addresses that appear more than once. Duplicate records result in repeated sends, which increases complaint risk and distorts campaign performance data.
Verify.
Run the normalized, deduplicated list through BillionVerify. The output assigns a signal to each address: valid, invalid, catch-all, role-based, unknown, or risky.
Route by signal.
Apply a routing decision to each result before any record enters the sender.