How to Find LinkedIn Profiles from Names and Companies at Scale
Turning a list of names and company names into verified LinkedIn URLs is often the missing first step in enrichment pipelines. Here's the technique, the accuracy expectations, and how to handle disambiguation.
Most enrichment providers need a LinkedIn URL as their lookup key. The problem: most lead lists don’t come with LinkedIn URLs. They come with names and company names.
This creates a gap that many teams paper over with manual LinkedIn searching — which is painfully slow at any meaningful scale.
Why LinkedIn URLs Matter as an Enrichment Input
LinkedIn profile URLs are the most reliable lookup key in B2B enrichment because:
They’re unique: There’s only one profile at /in/jane-smith-1a2b3c. A profile URL unambiguously identifies a specific person.
They’re stable: People don’t change their LinkedIn URL when they change jobs. The URL points to the person, not their employment.
They’re widely supported: Datagma, LeadMagic, Hunter.io, Apollo, and most enrichment providers accept LinkedIn URLs as a primary input key.
Without a LinkedIn URL, enrichment providers fall back to name + company matching — which is less precise, especially for common names.
The Name + Company Resolution Process
How does a name + company get resolved to a LinkedIn URL?
1. Search and filter: LinkedIn is searched for the name. Results are filtered by current employer matching the company name.
2. Company disambiguation: For large companies (Google, Salesforce, Microsoft), filtering by company name returns hundreds of candidates. Additional signals — title keywords, geography, profile completeness — are applied to narrow down.
3. Confidence scoring: The closest match is returned with a confidence score reflecting how strongly the result matches the input. Common names at large companies score lower than uncommon names at unique companies.
4. Fallback matching: If no direct match is found, the system may try domain-based matching (company website domain → LinkedIn company page → employee search) or email pattern guessing.
Accuracy Expectations by Input Quality
| Input | Expected Accuracy |
|---|---|
| Full name + unique company name | 85–92% |
| Full name + large/common company | 65–75% |
| Full name + company + title hint | 88–94% |
| Full name + company + location | 87–93% |
| Common name + any company | 55–70% |
How to improve accuracy: Add more context. Middle initial, title, department, or location significantly reduce ambiguity. Most lead lists have at least title available — always include it.
Handling Disambiguation
Common names are the hardest case. “John Smith at Salesforce” could be dozens of LinkedIn profiles. The disambiguation approach:
Add title: If you know the person is a VP of Sales, filter for LinkedIn profiles with “VP” or “Sales” in the title field.
Add location: If you know the company’s HQ or the person’s likely metro area, geographic filtering eliminates most duplicates.
Add email pattern: If you have an unverified email guess ([email protected]), you can use the email pattern as a secondary verification signal — look for LinkedIn profiles where the email pattern matches known contacts at the company.
Flag for manual review: Low-confidence matches (below 60%) should be flagged for spot-check rather than auto-accepted. One human review per 20 flagged records usually takes 10–15 minutes and catches most mismatches.
The Full Pipeline
Name + company resolution typically sits in the middle of a larger enrichment pipeline:
Input: Conference attendee list
(Name + Company + Title)
↓
Step 1: Name → LinkedIn URL resolution
↓
Step 2: LinkedIn URL → Full profile scrape
(current role, company, location, skills)
↓
Step 3: LinkedIn URL → Email + phone enrichment
(via Datagma, LeadMagic, etc.)
↓
Output: Fully enriched contact listEach step has its own coverage rate. If Step 1 resolves 80% of names to URLs, and Step 3 finds emails for 70% of resolved profiles, the combined pipeline produces verified contact details for approximately 56% of the original list. The remaining 44% require manual research or remain as partial records.
When to Skip LinkedIn URL Resolution
If your enrichment provider accepts name + company directly (without needing the LinkedIn URL), you can skip the resolution step. Most providers do support name + company matching, but accuracy is typically lower than URL-based lookups.
The resolution step adds value when:
- You need profile data (experience, title, skills) in addition to contact details
- Your provider’s name + company lookup accuracy is notably lower than URL lookup
- You want to use the LinkedIn URL for Sales Navigator imports or CRM linking
Output Format
The output of a name → LinkedIn URL resolution should include:
| Column | Content |
|---|---|
| Input Name | Original name from your list |
| Input Company | Original company from your list |
| LinkedIn URL | Resolved profile URL |
| Confidence Score | Match confidence (0–100) |
| Matched Title | Title found on the matched profile |
| Matched Company | Company found on the matched profile (should match input) |
| Review Flag | TRUE if confidence < threshold |
The matched title and company columns serve as a sanity check — if the matched title is completely unrelated to the expected role, the match may be wrong even if the company matches.
Resolve your contact list to LinkedIn URLs → LinkedIn Profile Finder from Names & Companies →