How do I find out which Apollo contacts already exist in my CRM?

Export your existing CRM contacts, then compare your Apollo export against that export using fuzzy matching on contact name and company name together. Email-only comparison misses most real overlap because Apollo and your CRM often have different emails for the same person. Clean by Similarity API handles the comparison and returns which Apollo contacts are likely already in your CRM and which are net-new.

Does this work for ZoomInfo and Lusha exports too?

Yes — the same process applies to any data vendor export. ZoomInfo, Lusha, Cognism, Clearbit, Clay, and others all have the same overlap problem with existing CRM data for the same reason: they build their databases independently from how your CRM was built. The comparison approach is identical regardless of vendor.

What if Apollo already has a CRM integration?

Apollo's native HubSpot and Salesforce integrations sync contacts in real-time and can check for existing records before creating new ones. If you're using the integration, this overlap problem is largely handled automatically. This article is for teams using CSV exports — either because they're not using the native integration, or because they want to check overlap before committing to an import.

How often should I check for overlap before importing?

Every time you import from a data vendor, regardless of how targeted the list is. Overlap accumulates over time — a list that had 10% overlap a year ago may have 30% overlap now as your CRM has grown. Running the check takes 20 minutes and prevents hours of cleanup.

What similarity threshold should I use for Apollo contact matching?

Start at 0.80. This catches common name variants and company formatting differences without generating too many false positives. If you're seeing real matches being missed at 0.80, try 0.75. If you're seeing too many false positives (two different people being flagged as a match), raise it to 0.85. The right threshold depends on how consistent the naming in your CRM is.

What do I do if a matched contact has a different email in Apollo than in my CRM?

Don't create a new record — update the existing one. If Apollo's email is a work email and your CRM has a personal email (or vice versa), add it as a secondary email on the existing contact record. Most CRMs support multiple email addresses per contact. If you're not sure which email is more current, check the contact's LinkedIn profile or leave both and let the rep verify on first outreach.

How to Check If Your Apollo Export Overlaps with Your Existing CRM Data

Quick answer

Export your existing CRM contacts, then compare your Apollo export against that export using fuzzy matching on company name and contact name together. Email-only comparison misses contacts where the email in Apollo differs from the email in your CRM — which is common when the same person appears with a personal email in one system and a work email in another. Fuzzy matching on name and company catches these and returns three groups: confirmed overlap, net-new, and borderline matches to review.

You've built a prospect list in Apollo. Filtered by industry, company size, job title. Exported 500 contacts. Some of them are almost certainly already in your CRM — active deals, former customers, existing contacts from previous outreach. Importing them creates duplicate records and means reps reach out to people who are already mid-conversation with someone else on your team.

The standard advice is to use Apollo's built-in CRM sync — but that only works if you've already connected Apollo to your CRM and it's running in real-time. For most teams doing occasional exports for targeted campaigns, you're working with a CSV file. And checking that file against your CRM before importing is a manual step with no good tool designed for it.

Until you do it, you're guessing at overlap. Here's how to actually know.

Why the Overlap Is Higher Than You Think

Apollo, ZoomInfo, Lusha, and similar platforms build their databases by aggregating data from LinkedIn, company websites, and other public sources. Your CRM was built from form fills, sales outreach, event attendance, and manual entry. These two datasets were built independently, which means the same person often appears in both — just with slightly different details.

Common scenarios:

Different email addresses. Apollo surfaces the work email it found in its database. Your CRM has the personal email the person submitted on a form two years ago. Or vice versa. Exact email matching misses this entirely.

Name variants. Apollo uses the name as it appears on LinkedIn. Your CRM has whatever the person typed when they submitted a form — sometimes a nickname, sometimes initials, sometimes a different spelling entirely.

Company name formatting. Apollo and your CRM may have the same company written as "Salesforce", "Salesforce.com", "Salesforce.com Inc.", or "salesforce" depending on the source. Exact-match tools treat these as four different companies.

Contacts from previous campaigns. If your team ran an Apollo campaign 18 months ago and imported those contacts, they're in your CRM. When you build a new Apollo list targeting the same persona, many of the same people appear again.

In practice, overlap between an Apollo export and an established CRM tends to be 15–35% for active sales teams — higher if you've been using Apollo or similar tools for more than a year, lower for newer companies.

What Doesn't Work

Relying on Apollo's CRM sync: Only works if you've connected Apollo directly to your CRM and it's actively syncing. For CSV exports, this doesn't apply.

Email matching in a spreadsheet: VLOOKUP or COUNTIF on email catches cases where both files have the exact same email. Misses everyone with a different email or no email in one of the files.

Importing with "update existing contacts" turned on: CRM import deduplication runs on email exact match. Contacts with different emails, missing emails, or name-only records in your CRM still create new records.

Manual review: For 500+ contacts, manual comparison isn't realistic. You'd need to search your CRM for each name — which takes hours and still misses the variants you're not specifically looking for.

How to Do It

Step 1: Export your CRM contacts

Pull a full contact export including first name, last name, company name, and email.

HubSpot: Contacts → Actions → Export → All properties or selected properties
Salesforce: Reports → New Report → Contacts and Accounts → export as CSV
Pipedrive / Zoho / other: look for "Export" under Contacts settings

Save as CSV.

Step 2: Prepare your Apollo export

Apollo exports include first name, last name, company, title, email, LinkedIn URL, and other fields. The LinkedIn URL is particularly useful — if both your CRM and Apollo have the same LinkedIn URL for a contact, that's a definitive match regardless of email or name differences. Check if your CRM stores LinkedIn URLs and if so, use that as your first matching pass.

For contacts without a LinkedIn URL match, proceed to fuzzy matching on name and company.

Step 3: Run a fuzzy match across both files

Upload both files to Clean by Similarity API. Select company name and contact name as your matching columns, set your similarity threshold (0.80 is a good starting point for contact data), and run the comparison.

The output groups your Apollo contacts into:

Matched — contacts that likely already exist in your CRM, with similarity scores
Net-new — contacts with no close match in your CRM
Borderline — contacts scoring between your threshold and ~0.70, worth a manual look

Want to reconcile your datasets in under 2 minutes?

Upload your CSV and find duplicates in seconds — no signup, no install, 1,000 rows free.

Try it for free →

Step 4: Handle each group differently

Matched contacts: Don't import as new records. If the Apollo export has useful data not in your CRM — direct phone number, updated title, company headcount — update the existing record or flag it for your rev ops team to enrich. If there's an active deal or recent activity on that contact, loop in the owning rep before doing anything.

Net-new contacts: These are your actual new leads. Import them, assign to appropriate reps or sequences, and treat them as cold outreach.

Borderline matches: Spot-check 10–15 of these manually. If they're mostly real matches, lower your threshold slightly and rerun. If they're mostly false positives, raise it.

Apollo-Specific Tip: Use LinkedIn URL First

Apollo exports include a LinkedIn URL column. This is the most reliable deduplication key available — if your CRM also stores LinkedIn URLs (HubSpot has a native LinkedIn field, Salesforce can be configured with a custom field), an exact LinkedIn URL match is a definitive match regardless of email or name differences.

Before running fuzzy matching, do a quick exact match on LinkedIn URL between your two files. Pull out all confirmed matches first, then run fuzzy matching only on the remainder. This reduces the fuzzy matching workload and eliminates false positives from the most reliable identifier you have.

What the Overlap Data Tells You

Beyond just preventing duplicates, knowing your overlap percentage is useful intelligence:

High overlap (>40%): Your Apollo targeting is hitting the same persona you've already worked. This isn't necessarily bad — these are qualified contacts — but it means your CRM already has most of this market segment. You may need to go broader or deeper in your search filters.

Low overlap (<10%): You're reaching people your team hasn't touched before. This is either a genuinely new segment, or a sign that your CRM data for this persona is sparse and worth enriching.

Overlap concentrated in specific companies: If 80% of your overlap is contacts at 5 companies you already have in your CRM, those are existing accounts — not new leads. Route them as account expansion opportunities rather than new business.

Key Takeaways

Apollo, ZoomInfo, and Lusha exports consistently overlap with established CRM databases — typically 15–35% for active sales teams
Email-only comparison misses most of the overlap because the same person often has different emails across the two systems
LinkedIn URL is the most reliable exact-match key when available in both files — use it as a first pass before fuzzy matching
Fuzzy matching on name and company together catches the name variants and company formatting differences that exact-match tools miss
Knowing your overlap percentage before import prevents duplicate records, incorrect routing, and outreach to contacts already in active deals