Match unknown emails to verified contacts

Built by: Lusha
Time to build: 1 min
Difficulty: Easy
Tools: Claude ▪ Lusha
Type: Prompt

A Claude prompt that takes a list of email addresses — webinar signups, form fills, Stripe exports, conference registrations — and returns the verified person behind each. Name, current title, current company, LinkedIn URL, and any signals. Built for inbound qualification, MQL routing, and event follow-up where the email is all the rep starts with.

Once Lusha is connected in Claude, the connector runs in the background — no special syntax needed. Just paste the email list and run.

Images on this webpage are for illustrative purposes only. Any named individuals shown in live demo outputs are real, with last names abbreviated for privacy.

The prompt

Open in Claude Open in ChatGPT 
<context>
List of email addresses. Resolve who is behind each one: name, current title,
current company, LinkedIn URL. Goal: qualify and route the inbound lead correctly.
</context>

<task>
1. Email list (one per line):
   [PASTE EMAILS]
2. For each email, resolve the verified person record via Lusha:
   full name,
   current title and current employer,
   validated work email(s) (with confidence grade and update date),
   validated phone with DNC status,
   LinkedIn URL,
   job start date (for promotion or new-role signals),
   previous job (for trajectory context).
3. Output a resolution table:
   Email input | Resolved name | Current title |
   Current company | LinkedIn | Resolution status.
4. Assign a resolution status per row:
   MATCHED: Lusha returned a verified person record for this email.
   MATCHED-MOVED: verified person, but the input email's domain no longer matches their current employer.
   NO MATCH: Lusha cannot resolve the email (likely personal, made-up, or an unindexed alias).
5. Summary at the top: total matched, matched-moved, no-match.
   Surface the matched-moved count separately. Those rows are useful intelligence.
</task>

<constraints>
- The lookup tool resolves the person, not the email itself. If the input email's domain is from a prior employer, Lusha returns the verified person at their current company. Flag this as MATCHED-MOVED.
- The verified emails Lusha returns may not include the input email. That is expected, especially for older inbound addresses.
- A no-match returns no credit charge. Personal Gmail, Yahoo, Outlook, and made-up addresses will not resolve and will not be billed.
- Do not invent any fields. If Lusha returns no record, the row stays NO MATCH.
</constraints>

What you'll get back

Input: 5-email reverse-lookup list — mix of corporate emails (some current, some from a prior employer), one guessed format, one personal Gmail.

Output: 2 MATCHED (input email is current and valid), 1 MATCHED-MOVED (verified person but the email’s domain is from a previous employer), 2 NO MATCH. Below is the real result from running the prompt against the live Lusha connector.

Email inputResolved nameCurrent titleCurrent companyStatus
b.r.****@s***f****.comBrian R.Chief Financial Officer[Cloud data platform]MATCHED
s.k.****@d******h*.comSean K.Vice President of Enterprise Sales[Cloud observability SaaS]MATCHED (promoted Jan 2026)
t.m.****@n*****.com.brTori M.Global Head of Revenue Operations[Business planning SaaS] (moved from productivity SaaS)MATCHED-MOVED
l.****@d**r**.ai (guessed format)———NO MATCH
****@gmail.com (personal)———NO MATCH

Names, emails, and company identifiers abbreviated for privacy. Full records (including company names) are returned inside your Claude session.

The MATCHED-MOVED row is the most important pattern on this page. The input email’s domain is from the contact’s previous employer (a productivity SaaS). Lusha resolved the person and returned her current employer (a business planning SaaS), not the employer the email’s domain belongs to. The verified emails on the record do not include the input email. This is the right behavior for inbound qualification: the email got the rep to the person, and the person’s current context tells the rep how to respond.

Three credits consumed for three matched rows. The two no-matches charged nothing.

Why use Lusha

A list of email addresses is a list of unknowns until each one is resolved to a person. Three patterns repeat across every inbound qualification pass.

The lookup resolves the person, not the email. When a rep submits an old work email to Lusha, the tool finds the person and returns their current employer — even if the input email’s domain belongs to a previous company. That is the right behavior for inbound routing. The email got the rep to the right human; the human’s current context tells the rep how to respond.

A MATCHED-MOVED row is qualified intelligence. A form fill with an old company email means the person filled the form from a contact list, a saved cookie, or a habit. Their current employer is the one that matters for outreach. The prompt routes the row to the new company automatically and the AE picks up a thread that LinkedIn alone would not have surfaced.

No-charge no-match protects the budget on noisy inbound. Webinars, ungated forms, and trial signups all attract personal email addresses and made-up entries. The prompt routes those rows to NO MATCH with no credit consumed. The rep moves on without paying for ambiguity.

Data drawn from 300M+ verified contacts under GDPR, CCPA, SOC 2, ISO 27701, ISO 31700, and TRUSTe.

FAQ

  • Why does the verified email Lusha returns sometimes differ from the input email?

    The lookup resolves the person, not the email string. When Lusha finds a person whose record includes the input email — even an old one — it returns the verified emails currently on file. Those may differ from the input. This is intentional: the rep needs the current sendable address, not the one that brought them to the record.

  • What does MATCHED-MOVED mean?

    The input email’s domain belongs to the contact’s previous employer. Lusha resolved the person and returned their current employer (which is different). The status surfaces the move so the rep knows the old email may bounce and the right outreach context is at the new company.

  • Can the prompt handle personal Gmail addresses?

    Lusha indexes verified work contacts. Personal Gmail, Yahoo, Outlook, and similar consumer addresses return no match. Those rows route to NO MATCH with no credit charged.

  • What if the input email has a typo?

    The prompt does not auto-correct or guess email variations. A typoed input returns NO MATCH. For partial inputs (name + company without an email), the find-missing-fields prompt is the better fit.

  • Can I use this for a list of emails I scraped or purchased from a third party?

    The prompt works on any email list. The relevant question is what the rep does with the result. Using Lusha to verify and route inbound leads is core use. Cold-blasting a scraped list because Lusha resolved the names is not — and would violate the compliance frameworks the data is built under.

  • How is this different from cleaning a CRM export?

    The CRM cleanup prompt assumes the rep has a person record and needs to validate the fields. This prompt starts from an email address and resolves the person. The CRM prompt cleans known records. This prompt qualifies unknown ones.

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