How to move B2B prospecting data from ZoomInfo to Apollo without losing enrichment

To move B2B prospecting data from ZoomInfo to Apollo without losing enrichment, export your ZoomInfo contacts as a CSV with all firmographic and contact fields, map each column to the matching Apollo field during import, then run Apollo's re-enrichment on the uploaded records. This preserves company data and refills any gaps Apollo can match against its own database.

The biggest mistake teams make is treating this like a simple copy-paste. ZoomInfo and Apollo store enrichment differently, so a careless import drops job titles, technographics, and email verification status. Done right, you keep what ZoomInfo gave you and gain Apollo's live data refresh.

Why enrichment gets lost during migration

Enrichment data is everything beyond name and email — company size, revenue, industry codes, tech stack, seniority, and email deliverability status. When you export from one platform and import to another, three things break it:

• Field name mismatches. ZoomInfo calls a field "Employee Count"; Apollo calls it "# Employees." Unmapped columns get silently ignored.
• Format differences. Phone numbers, country codes, and revenue ranges use different formats across tools.
• Overwrite-on-import. Apollo may overwrite richer ZoomInfo data with thinner matches if you don't control the merge behavior.

Most teams get this wrong because they skip the field-mapping review step and let the importer auto-guess.

Step-by-step migration process

1. Export the full dataset from ZoomInfo

In ZoomInfo, build a saved list or use the export feature to pull contacts. Select every available column, not just name and email. Critical fields to include:

• First name, last name, full name
• Business email and email status
• Direct dial and mobile phone
• Job title and seniority/management level
• Company name, domain, and website
• Employee count and annual revenue
• Industry and SIC/NAICS codes
• LinkedIn URL (this is the strongest match key)

Export as CSV. ZoomInfo enforces export credit limits, so plan batch sizes around your plan's allowance.

2. Clean and standardize the CSV

Before importing, normalize the file:

• Split full names into separate first/last columns if needed
• Ensure company domains are clean (acme.com, not https://www.acme.com)
• Convert revenue ranges to Apollo-compatible values
• Remove duplicate rows by email or LinkedIn URL

LinkedIn URL and company domain are your highest-confidence match keys. Apollo matches records far more accurately on domain than on company name alone.

3. Import into Apollo with manual field mapping

Use Apollo's CSV import under Contacts. When the mapping screen appears, map each ZoomInfo column to its Apollo equivalent manually. Don't trust auto-detection.

For fields Apollo lacks a native slot for, create custom fields first so the ZoomInfo enrichment lands somewhere instead of being dropped.

4. Run Apollo re-enrichment

After import, select the uploaded contacts and run Apollo's enrichment. Apollo matches each record against its database and fills missing fields — but configure it to append, not overwrite wherever possible, so your ZoomInfo data stays intact when Apollo's match is weaker.

Apollo's email verification re-checks deliverability, which is genuinely useful since ZoomInfo emails can go stale. See Apollo's data and enrichment docs for current API and import behavior.

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Preserving enrichment: the overwrite problem

The core risk is Apollo replacing accurate ZoomInfo values with its own. Control this by:

1. Importing into custom fields first for any data you absolutely want to keep (e.g., zi_revenue, zi_email_status).
2. Running enrichment in append mode so blank Apollo fields fill from its database without touching populated ZoomInfo fields.
3. Comparing before bulk-applying — test on 50 records, audit the result, then process the full list.

This matters more if you're picking between platforms long-term. The Apollo vs ZoomInfo vs Lusha comparison covers which tool holds better contact data for your segment, which affects whether you migrate fully or run both.

Use the API for large or recurring migrations

For datasets above a few thousand records, or ongoing sync, skip CSVs and use Apollo's API. A basic enrichment call matches a ZoomInfo record and returns Apollo's data:

curl -X POST https://api.apollo.io/v1/people/match \
  -H "Content-Type: application/json" \
  -d '{
    "api_key": "YOUR_API_KEY",
    "email": "jane@acme.com",
    "domain": "acme.com",
    "first_name": "Jane",
    "last_name": "Doe"
  }'

Pass the ZoomInfo email and domain as match keys, then merge the response into your records in code — letting you decide field-by-field which source wins. This is the cleanest way to avoid enrichment loss because you fully control the merge logic.

Validate the migration

After import, spot-check quality:

• Match rate — what percentage of records Apollo successfully matched
• Field completeness — are titles, revenue, and employee counts populated
• Email validity — how many emails Apollo flagged as verified vs risky
• Duplicate count — confirm dedup worked across both datasets

Clean prospecting data feeds directly into outreach quality, which shapes whether your outbound B2B sales motion generates qualified pipeline. Bad data here cascades into low reply rates downstream. Reliable contact data also makes discovery call preparation faster since reps start with accurate firmographics.

Key takeaways

• Export all ZoomInfo fields, not just name and email — enrichment lives in the extra columns.
• Map fields manually in Apollo; auto-detection drops mismatched columns.
• Use LinkedIn URL and company domain as match keys for the highest accuracy.
• Run enrichment in append mode and stash critical ZoomInfo data in custom fields to prevent overwrites.
• For large or recurring jobs, use Apollo's people/match API to control the merge logic yourself.
• Always test on a small batch and validate match rate before processing the full dataset.