How to Export Leads From Apollo and Import Into Your Cold Email Tool

Most people treating Apollo like a magic button are doing it wrong. They export a CSV, drag it into their tool, hit send — and then wonder why their reply rate is 0.3% and their domain is flagged within two weeks.

The export-to-import workflow is where campaigns live or die. Get it right and you're running clean, deliverable outreach at scale. Get it wrong and you're burning sender reputation on unverified contacts who never asked to hear from you.

This is the exact process I use when I need to export leads from Apollo and import into a cold email tool — including the stuff nobody talks about, like why you should never trust Apollo's email verification at face value.

Why the Export Leads Apollo Import Cold Email Workflow Is Broken for Most Teams

Here's the counterintuitive part: Apollo has 275+ million contacts in its database, but a 2023 analysis of B2B data providers found that even premium data sources carry 20–30% invalid or outdated emails at any given time. Apollo is no different.

That means if you export 1,000 leads and send to all of them, you're potentially hitting 200–300 bad addresses. A bounce rate above 2% will get your sending domain flagged by Gmail and Outlook. Above 5% and you're looking at blacklisting.

The fix isn't complicated — but it requires a few extra steps that most people skip because they're in a rush.

Step 1: Build a Targeted List in Apollo (Don't Just Export Everything)

Before you touch the export button, spend 20 minutes tightening your filters. A smaller, tighter list outperforms a massive sloppy one every single time.

Filters I always apply:

• Job title keywords — be specific. "Head of Sales" hits different than "Sales" alone
• Employee count — match this to your ICP. If you sell to mid-market, filter 100–1,000 employees
• Technologies used — Apollo's technographic filters are underrated. Filter by tools your solution integrates with or replaces
• Last verified date — filter to contacts verified within the last 6 months if the option is available
• LinkedIn activity — prioritize contacts who have recent LinkedIn activity signals (indicates the contact is real and active)

One thing I do that most people don't: I cross-reference with LinkedIn Sales Navigator before exporting. If a contact hasn't been active on LinkedIn in 12+ months, I drop them. Stale contacts = stale emails.

Step 2: Export the Right Fields From Apollo

Apollo lets you customize which fields get exported. Don't just grab everything — it creates messy CSVs that are painful to import.

Here are the fields I export every time:

Field	Why It Matters
First Name	Personalization
Last Name	Personalization
Email	Primary contact
Title	Segmentation + personalization
Company Name	Personalization
Company Domain	For email verification
LinkedIn URL	Manual research if needed
City / Country	Timezone-based send scheduling
Phone (optional)	Multi-channel follow-up

Skip the fields you'll never use. A clean 8-column CSV is infinitely easier to work with than a 40-column mess.

How to export from Apollo:

1. Go to People or Saved Searches
2. Select your contacts (use "Select All" carefully — you're capped at 1,000 per export on most plans)
3. Click Export → Export to CSV
4. Choose your fields
5. Download the file

Note: Apollo's free plan limits you to 50 exports per month. Paid plans start at 200–400 per month depending on tier. If you're doing serious volume, you'll hit these limits fast.

Step 3: Clean the CSV Before You Import Anything

This is the step that separates professionals from amateurs.

Never import a raw Apollo CSV directly into your cold email tool. Here's why:

• Duplicate contacts — Apollo frequently returns duplicates, especially across saved searches
• Invalid email formats — rare but it happens, especially with manually added contacts
• Bounce-prone domains — role-based emails like info@, support@, admin@ have terrible deliverability
• Catch-all domains — these accept any email format, so Apollo shows them as "verified" but many never deliver

Run your list through a CSV Email List Cleaner first to strip formatting issues and duplicates. Then run the email column through a proper Bulk Email Verifier to catch invalids, catch-alls, and role-based addresses before they cause damage.

My personal rule: if a list comes back with more than 15% catch-all or risky addresses, I don't send to the risky segment. I'll send to the verified-safe portion and let the results prove the approach before touching the rest.

Step 4: Segment Before You Import

Don't import 3,000 mixed contacts into one campaign. Segment them first.

Common segmentation cuts I use:

• By company size (SMB vs. mid-market vs. enterprise) — these need different messaging
• By title seniority (VP+ vs. Director vs. Manager) — authority level changes the CTA
• By geography — US vs. EMEA vs. APAC have different sending time preferences
• By tech stack — if you filtered by technology in Apollo, create separate campaigns per tech segment

A 300-person, tightly segmented campaign will almost always outperform a 3,000-person blast. I've seen segmented campaigns hit 18–22% reply rates where unsegmented equivalents hit 3–4%.

Step 5: Import Into Your Cold Email Tool

Once your CSV is clean and segmented, importing is straightforward — but the mapping step trips people up.

Field mapping checklist:

• Map First Name to your personalization variable ({{first_name}})
• Map Company Name to {{company}}
• Map Title to {{title}} if you're using it in sequences
• Double-check that your email column maps to the primary email field — not a custom field

If you're using Cleanmails, the import flow handles CSV mapping with column preview so you can verify every field before contacts are added to a campaign. The built-in email validation layer also flags risky addresses on import — which means even if you skipped Step 3, you've got a safety net.

For tools without native validation, you need to be extra rigorous at the cleaning stage.

Step 6: Validate Your Sending Infrastructure Before You Touch Send

This is where most people completely forget the other half of the equation.

You can have the cleanest list in the world, but if your sending domain doesn't have SPF, DKIM, and DMARC configured correctly, your emails are going straight to spam — or getting rejected outright.

Before launching any campaign with fresh Apollo leads, run your domain through an SPF/DKIM/DMARC Checker to verify your authentication records are live and correct. If you need to set these up from scratch, the guide to setting up SPF, DKIM, and DMARC in under 10 minutes covers the exact steps.

Also check your emails against a spam word checker before you send. A single phrase like "guaranteed results" or "act now" can trigger spam filters across an entire campaign.

Step 7: Write Copy That Matches the Segment

I'm not going to write a full copywriting guide here, but one thing I'll say: the reason Apollo-sourced campaigns underperform isn't usually the data. It's the copy.

People export hyper-targeted leads, then send a generic template that could have been written for anyone. You've done the hard work of filtering by title, company size, and tech stack — your email should reflect that.

If you're sending to 300 HubSpot users who are VP of Marketing at 50–200 person companies, your first line should reference something specific to that intersection. Not "I help companies improve their marketing."

For scaling personalization across large lists without writing 300 unique emails, spintax is your best friend. Done right, it creates enough variation to avoid spam filters and feel personal at scale.

The Full Workflow in Under 30 Minutes

Here's the complete process distilled:

1. Build tight filters in Apollo (10 min) — ICP-specific, not broad
2. Export clean fields to CSV (2 min)
3. Clean CSV — remove duplicates, fix formatting (5 min with CSV cleaner)
4. Verify emails — remove invalids, catch-alls, role-based (5 min with email verifier)
5. Segment the list — split by size, title, or tech (3 min)
6. Import with correct field mapping (2 min)
7. Check sending infrastructure — SPF/DKIM/DMARC live (2 min)
8. Write segment-specific copy — reference the filters you applied

Total: under 30 minutes for a campaign-ready list that won't torch your domain.

One More Thing: Don't Over-Rely on Apollo's "Verified" Badge

Apollo marks emails as "verified" based on their own validation logic — but "verified" in Apollo means it passed a syntax check and possibly a catch-all test at the time of their last crawl. It does not mean the email is live today.

I've seen Apollo-verified lists come back with 12–18% bounce rates after running through independent verification tools. That's domain-killing territory if you send without checking.

Always verify independently. Always. Your sender reputation is worth more than the 5 minutes you save by skipping it.

If you want to go deeper on why deliverability issues happen even with "clean" lists, read why your cold emails are landing in spam — it covers the authentication and infrastructure side in detail.

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Related:

• Why Your Cold Emails Are Landing in Spam: A Deep Dive into Email Authentication
• Spintax for Cold Email: A Complete Guide to Writing Unique Emails at Scale
• Why 93% of Cold Emails Never Get Opened (And How to Fix It)
• Tool: Bulk Email Verifier — verify your Apollo export before you send