Find lookalike companies of your best customers
A Claude prompt that takes a seed list of your best customers and returns verified lookalike companies with firmographics. The Lusha connector runs the lookalike model on the company graph. Claude ranks and filters the output by the criteria you actually care about.
Once Lusha is connected in Claude, the connector runs in the background — no special syntax needed. Just paste the prompt and run.
The prompt
<context>
I want to expand prospecting from a list of my best customers using lookalike modeling.
</context>
<task>
1. Take this seed list of my best customers (5-100 domains or LinkedIn URLs):
[PASTE DOMAINS OR LINKEDIN URLS]
2. Use Lusha's lookalike companies search to return companies similar to the seed list.
3. For each lookalike, return:
- Company name
- Domain
- Industry
- Headcount
- Country and city
4. Filter the output:
- Drop companies under [HEADCOUNT MINIMUM]
- Drop companies outside [TARGET GEOGRAPHY], if specified
- Drop companies already in my seed list
5. Return the top 25 matches after filtering.
</task>
<constraints>
- Seeds must be domains or LinkedIn URLs (not free-text company names).
- Lookalike modeling is broad by design — filtering on headcount and geography is what makes the list usable.
- If fewer than 25 matches remain after filtering, surface what filter is too tight.
</constraints>What you'll get back
nput: Seed list — 7 mid-market B2B SaaS customers (Motive, CreatorIQ, Tempo Software, Altana, Array, Candex, DispatchTrack). Filter — headcount 100+, US-based.
Output: 15 lookalikes returned in the first batch, more available via pagination. Two strong matches in the target headcount range surfaced immediately — Aithent and Odeko, both NYC-based B2B SaaS companies. The rest of the batch was filtered out for being below the headcount floor.
| Company | Headcount | Industry | City |
|---|---|---|---|
| Aithent | 492 | Software Development | New York, NY |
| Odeko | 209 | Software Development | New York, NY |
This is a real result from running the prompt against the live Lusha connector. Lookalike modeling returns a broad set of candidates — the filter pass in step 4 of the prompt is what turns the output into a usable list.
Why it works
ICP discovery usually starts from a hunch. Running it from a seed list of real closed-won customers turns it into a data exercise — pairing the Lusha lookalike model with Claude’s filter pass lets you go from “I think we should target fintechs” to “here are 25 companies that look like our best customers, in our target size and region.”
Two things matter for results to land:
Strong seeds. Five to fifteen of your most-similar closed-won customers produces sharper output than a mixed bag across stages and sizes. The model matches on multiple signals, so seed homogeneity is what tightens the result.
A filter pass. Lookalike modeling is broad by design — it surfaces candidates across the signal space. Claude’s filter step is what narrows the output to your target headcount, region, and industry.
The prompt pulls candidates from 300M+ verified companies under GDPR, CCPA, SOC 2, ISO 27701, ISO 31700, and TRUSTe.
FAQ
How many seed companies do I need?
Five minimum, one hundred maximum. Seven to fifteen highly-similar customers produces the sharpest match. Below five, the model has too little signal. Above fifteen, similarity dilutes.
Can I seed with company names instead of domains?
The Lusha lookalike model accepts domains or LinkedIn URLs as seeds. Company names need to be resolved to domains first — Claude can do that step automatically if you paste the names, but accuracy is higher when you provide domains directly.
Why does the unfiltered output include very small companies?
Lookalike modeling matches across many signals — industry, location, growth pattern, and others. If your seeds vary in size or include any small companies, the candidate pool reflects that. The filter pass in step 4 is built to handle this — set a headcount floor and the noise drops out.
Can I exclude existing customers from the result?
Yes. The Lusha lookalike tool supports an exclusion list. Pass your current customer domains in the prompt and Claude will route them to the exclude parameter.
What's the difference between this prompt and a regular company search?
Company search filters on attributes you specify upfront — industry, headcount, technology. Lookalike modeling finds companies similar to a seed across attributes you didn’t think to specify. Use lookalike when you trust your closed-won data more than your hypothesis.
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