Why does my CRM create duplicate companies from a Sales Navigator import?

Your CRM deduplicates on exact match — account name or domain. Sales Navigator exports company names as typed by each LinkedIn member, so the same company appears under multiple variants. None of these match exactly, so your CRM creates a new account for each one. Normalizing and fuzzy-deduplicating the company name column before import prevents this.

Does this apply to Apollo, ZoomInfo, and Lusha exports too?

Yes — the same problem applies to any data vendor export. Apollo, ZoomInfo, Lusha, and similar tools pull company names from LinkedIn or other sources that don't enforce consistent naming. The cleaning process is identical: normalize, fuzzy-deduplicate, check against existing CRM data.

What if the same contact appears in both my Sales Navigator export and my existing CRM?

Export your existing CRM contacts and run a comparison against your Sales Navigator export. Records that score as likely matches already exist — skip importing those rows or map them to the existing record's ID so your CRM updates rather than creates.

How do I handle contacts who have the same name but work at different companies?

Multi-column matching handles this correctly — it scores similarity on both name and company together. Two people named "David Park" at different companies score low on the combined signal and are correctly treated as different records.

Is it worth cleaning a small export — say, 50 contacts?

For 50 contacts, a quick manual scan takes less time than setting up a tool. The cleaning process described here pays off at 200+ rows where manual review becomes unreliable and time-consuming.

How to Deduplicate a LinkedIn Sales Navigator Export Before Uploading to Your CRM

Sales Navigator is one of the best prospecting tools available. Its exports are some of the messiest CSVs you'll import into a CRM.

Company names come out exactly as they appear on LinkedIn profiles — which means the same company appears as "Acme Corp", "Acme Corporation", "Acme", and "ACME Inc." depending on how each employee filled in their profile. Import that directly and your CRM ends up with four account records for one company, each with a handful of associated contacts and no shared activity history.

This is a five-minute fix before import. It's a multi-hour problem after.

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Why Sales Navigator Exports Are Particularly Messy

Unlike a list you built yourself with consistent data entry, Sales Navigator pulls company names directly from LinkedIn member profiles. LinkedIn doesn't enforce a canonical company name — each person types their employer as they see fit.

The result in a typical export:

The same company appearing under 3–5 name variants
Job titles that differ wildly for the same role ("VP Sales", "VP of Sales", "Vice President, Sales")
Contacts who are already in your CRM under a different email (personal vs work) or a slightly different name
Companies that exist in your CRM under a legacy name that doesn't match their current LinkedIn name

None of these are caught by exact-match deduplication. They all create duplicate records on import.

Step 1: Normalize Company Names First

Before deduplicating, normalize the company name column. This converts many fuzzy matches into exact matches and makes the deduplication step more reliable.

What to normalize:

Strip business suffixes — remove Inc., Corp., LLC, Ltd., GmbH, PLC, Limited, Incorporated before comparing. "Acme Corp" and "Acme Corporation" both become "Acme".
Lowercase everything — "ACME" and "Acme" are the same company.
Remove punctuation — "Johnson & Johnson" and "Johnson and Johnson" should match.

You can do this manually in a spreadsheet for small exports. For anything over a few hundred rows, a dedicated tool handles it automatically as a preprocessing step before matching runs.

Step 2: Deduplicate on Company Name and Contact Name Together

This is the step most people skip — and the one that matters most for Sales Navigator exports.

Matching on company name alone catches companies written differently. But it misses cases where the company name is close but not identical, or where the same person appears twice with slightly different details. Matching on contact name alone catches "Jennifer Walsh" and "Jen Walsh" — but risks merging two different people at the same company with similar names.

Matching on both fields together is significantly more reliable. "Jen Walsh at Acme Corp" and "Jennifer Walsh at Acme Corporation" is almost certainly the same person. Neither field matches exactly, but the combined signal is strong.

Clean by Similarity API does this in one step — upload your Sales Navigator CSV, select company name and contact name as matching columns, and it groups near-duplicates with similarity scores. You review the clusters and download a clean file. No install required, no account needed to get started.

Step 3: Check Against Your Existing CRM Data

Your export might be clean internally but still contain contacts and companies that already exist in your CRM — just under a different name or email than what LinkedIn shows.

This is especially common if:

Your CRM has records built from form fills or manual entry over years
The same prospect has a personal email in your CRM and a work email on LinkedIn
Your CRM has the company under an older name or abbreviation

To catch these: export your existing CRM contacts and accounts, then compare against your Sales Navigator export. Records that score as likely matches already exist — update those rather than creating new ones.

Step 4: A Few Fields Worth Cleaning Before Import

Beyond deduplication, a few specific fields in Sales Navigator exports commonly cause problems:

Company name — as covered above, normalize before importing.
LinkedIn URL — if your CRM stores LinkedIn URLs as a contact property, check for duplicates on this field specifically. The same profile URL appearing twice is a guaranteed duplicate.
Email — Sales Navigator sometimes includes email addresses from connected profiles. Check format validity and watch for personal emails (gmail, hotmail) vs work emails that may already exist in your CRM as separate records.
Location — Sales Navigator outputs city and country separately. Check your CRM's field structure matches before mapping.

Key Takeaways

Sales Navigator exports are inherently inconsistent because LinkedIn doesn't enforce canonical company names — the same company appears many ways across different member profiles
Exact-match deduplication misses most real duplicates in a Sales Navigator export — you need fuzzy matching on both company name and contact name together
Normalizing company names before matching (stripping suffixes, lowercasing) converts many fuzzy matches into exact matches and makes the whole process more reliable
Checking against existing CRM data before importing prevents creating duplicates of records you already have, especially for contacts who appear in your CRM under a different email