Find lookalike buyers from your best contacts in ChatGPT

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

Example outputs in this play are illustrative — they reflect the structure, fields, and format of real Lusha connector output, but were not pulled from a live session. Run the prompt with your own seed contacts to see live results.

Your best buyers are more than closed deals. They are a pattern.

They usually share something: similar titles, seniority, departments, company types, buying pressure, or operational problems. But most teams turn that pattern into broad targeting rules like “VP Sales at SaaS companies” or “RevOps leaders in mid-market accounts.”

That’s a start, but it misses the real signal: the people who actually bought, engaged, expanded, or moved deals forward.

This prompt uses Lusha in ChatGPT to find contacts similar to your best buyers, verify their current roles, enrich their available contact data, and prioritize the people most worth reaching out to. Instead of building a prospect list from generic titles, you build from the buyers who already proved the fit.

How to start

Open in Claude Open in ChatGPT
1

Open Lusha in ChatGPT

Go to Lusha in ChatGPT and click “Start chat.” Every conversation started this way is automatically Lusha-enabled.

2

Or invoke Lusha in any existing conversation

Type @Lusha in the prompt bar and select Lusha from the dropdown. Unlike Claude, Lusha does not activate automatically in every ChatGPT conversation. You must invoke it every time.

3

Add your seed contacts and send

Copy the prompt below, add at least five seed contacts, include your ICP context, and send. Lusha finds similar buyers, verifies their roles, and helps you prioritize who to contact first.

The prompt

Start from Lusha in ChatGPT or type @Lusha before sending.

@Lusha Find lookalike buyers based on my best contacts.

SEED CONTACTS:
Add at least 5 contacts who represent strong-fit buyers,
champions, customers, or high-quality opportunities.

You can use LinkedIn URLs, business emails, or name +
company details.

1. [LinkedIn URL / email / name + company]
2. [LinkedIn URL / email / name + company]
3. [LinkedIn URL / email / name + company]
4. [LinkedIn URL / email / name + company]
5. [LinkedIn URL / email / name + company]

EXCLUDE:
Contacts or companies we should not include:
- Existing customers: [company domains, if any]
- Competitors: [company domains, if any]
- Existing open opportunities: [company domains, if any]
- Poor-fit segments: [titles, industries, regions, or
  company sizes to exclude]

ICP CONTEXT:
Best-fit titles: [titles]
Best-fit departments: [departments]
Best-fit seniority: [manager / director / VP / C-level]
Best-fit industries: [industries]
Best-fit company size: [employee range]
Best-fit regions: [regions]

MY PRODUCT:
[One sentence describing what you sell and the
problem it solves]

Using Lusha, do the following:

1. VALIDATE THE SEED CONTACTS
   Check whether each seed contact can be matched to a
   real business contact.

   Return:
   - Name
   - Current title
   - Current company
   - Department
   - Seniority
   - Match status

   If I provide fewer than 5 valid seed contacts, ask me
   for more before running the lookalike search.

2. FIND LOOKALIKE CONTACTS
   Use the seed contacts to find similar contacts.

   Prioritize people who match the pattern across:
   - Title
   - Department
   - Seniority
   - Company type
   - Industry
   - Company size
   - Region

3. VERIFY CURRENT ROLE
   For each lookalike contact, return:
   - Name
   - Current title
   - Current company
   - Department
   - Seniority
   - Location
   - LinkedIn profile if available
   - Whether the person appears to still be at the company

4. ENRICH CONTACT DATA
   For each verified lookalike contact, return:
   - Verified business email availability
   - Direct or mobile phone availability
   - DNC status if available
   - Last updated date if available

5. ENRICH THE COMPANY
   For each contact’s company, return:
   - Company name and domain
   - Industry
   - Employee count
   - HQ location
   - Revenue range if available

6. CHECK RECENT SIGNALS
   For high-fit lookalike contacts, check for recent
   company or contact signals from the last 6 months.

   Prioritize:
   - Promotion
   - Company change
   - Hiring surges
   - Hiring surges by relevant department
   - Headcount increases or decreases
   - IT spend changes
   - Website traffic changes
   - Commercial activity news
   - Corporate strategy news
   - Financial events news
   - People news
   - Product activity news

7. SCORE AND PRIORITIZE
   Rank each contact:

   Tier 1:
   Strong lookalike match + high ICP fit +
   verified contact data + recent relevant signal

   Tier 2:
   Strong lookalike match + high ICP fit,
   but weak or no recent signal

   Tier 3:
   Medium fit or unclear urgency

   Exclude:
   Poor fit, disqualified, duplicate, competitor,
   existing customer, or unclear match

8. CREATE THE OUTREACH ANGLE
   For each Tier 1 contact, write:
   - One reason this person looks like our best buyers
   - One timely reason to reach out now, if available
   - One subject line under 7 words
   - One opening line under 30 words

9. OUTPUT FORMAT
   Return:
   - Seed contact validation
   - Lookalike buyer table
   - Verified contact data availability
   - Company enrichment
   - Recent signal, if available
   - Priority tier
   - Suggested outreach angle
   - Excluded contacts and why

Do not invent contacts, emails, phone numbers, companies,
or signals. If Lusha cannot verify a contact, mark it clearly.

What you’ll get back

 

A prioritized list of lookalike buyers, verified contact data, company context, and outreach angles for the best-fit prospects. Here’s what the output looks like:

Lookalike buyers — Lusha

FieldValue
Seed contacts5 contacts validated · same buyer pattern detected
Lookalike buyers found25 contacts · 8 high-fit contacts · 4 Tier 1 prospects
Best-fit contactR.M. · VP Sales · B2B SaaS · North America
Verified dataBusiness email available · mobile available · DNC false
Recent signalCompany hiring surge in Sales · detected in the last 30 days
Outreach angleLooks like your strongest buyers and has a current team-growth signal

Example outputs in this play are illustrative — they reflect the structure, fields, and format of real Lusha connector output, but were not pulled from a live session. Run the prompt with your own seed contacts to see live results.

 

Why use Lusha in ChatGPT to find lookalike buyers

 

Your best buyers are one of the clearest signals your team has. They show which titles care, which departments feel the pain, which company types convert, and which seniority levels can move a deal forward.

Lusha helps turn that pattern into a new prospect list. The prompt starts with real people who already worked for your business, finds similar contacts, verifies their current roles, and enriches the available contact data. That makes the output more precise than a generic title search.

The signal layer adds timing. A lookalike buyer is useful. A lookalike buyer at a company with a recent hiring surge, IT spend increase, role change, or relevant company news is more useful because the message can connect fit with a reason to act now.

The result is a prospecting list built from proof, not assumptions.

Lusha data is sourced and used in accordance with Lusha’s Privacy Policy and Terms of Use. Lusha is GDPR compliant and covers contacts across North America, EMEA, and APAC.

FAQ

  • How many seed contacts do I need?

    Use at least five seed contacts. Choose people who represent strong-fit buyers, champions, customers, or high-quality opportunities. The stronger the seed list, the better the lookalike results.

  • What kind of seed contacts should I use?

    Use contacts who represent the type of buyer you want more of. Good seeds can include closed-won buyers, active champions, expansion contacts, high-intent demo requests, or prospects who moved quickly through the pipeline.

  • Can I exclude existing customers or open opportunities?

    Yes. Add existing customer domains, competitors, open opportunities, or poor-fit segments in the exclude section. The prompt asks Lusha to keep those contacts or companies out of the final list.

  • How is this different from searching by job title?

    A job title search starts with a broad assumption. A lookalike buyer search starts with real people who already fit your business. That helps uncover prospects who match the pattern of your best buyers, not just a keyword in a title.

  • What if no recent signals are found?

    A lookalike contact can still be useful without a recent signal, but the outreach may be less urgent. The prompt separates strong-fit contacts with timely signals from strong-fit contacts that may be better for later outreach or nurture.

Related Prompts

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