Lookalike Prospect

Lookalike Prospect

Expand an ICP from a reference set of known-good companies or contacts. Requires a minimum of 5 references — the lookalike model degrades significantly below this threshold.

Step 1 — Validate Input Count

Count the number of reference companies or contacts provided.

If fewer than 5 are provided, stop and explain before doing anything else:

"Lusha's lookalike model needs at least 5 reference [companies/contacts] to return quality results — fewer than that produces unreliable matches. You've provided [N]. Can you add [5−N] more?"

Do not proceed until the user has provided at least 5 references.

If 5 or more are provided, confirm the reference set with the user:

"Running lookalike search using these [N] [companies/contacts] as the reference set: [list]. Shall I proceed?"

Step 2 — Determine Mode

Based on the user's input, determine whether this is a company lookalike or contact lookalike search:

• References are companies (domains, LinkedIn company URLs, or names) → company mode
• References are people (emails, LinkedIn profile URLs, or name + company) → contact mode
• Mixed input → ask the user to clarify

A bare job title is not a valid seed — a lookalike needs concrete reference companies or people. If the user only has a persona/title in mind, route them to prospect (ICP search) or signal-prospect instead.

Step 3 — Assemble the Seed Set

The lookalike tools accept raw identifiers directly as seeds — no enrichment or Lusha-ID resolution is needed in the common case. Pass the references straight through. The seed count (5–100) is the total identifiers across the seed arrays.

Company mode — lookalike_companies.seeds accepts:

• domains (e.g. lusha.com)
• linkedinUrls (company page URLs)

If the user gave company names rather than domains, resolve each name to a domain first with companies_search (enrich: false — you only need the domain, not reveal data), since the seed schema does not accept bare names. If a name can't be resolved, flag it and proceed only if ≥5 seeds remain.

Contact mode — lookalike_contacts.seeds accepts any mix of:

• emails
• linkedinUrls (profile URLs)
• contacts — { firstName, lastName, companyDomain | companyName }
• contactIds — Lusha contact IDs, if you already have them

Pass whatever form the user provided directly. No lookup step is needed.

Step 4 — Run Lookalike Search

Company mode: Use lookalike_companies with the seed set. limit up to 100 (default 25).

Contact mode: Use lookalike_contacts with the seed set. limit up to 50 (default 25).

Pass any known customers/won accounts in exclude (same identifier shape as seeds) to keep them out of the results. Results paginate via dedupeSessionId: omit it on the first call, then pass the returned token back on follow-up calls for the same seeds to fetch more non-duplicate matches (sessions expire after 30 days).

Step 5 — Find Decision Makers (Company Mode Only)

For the lookalike companies, use prospecting_contact_search scoped to them via companyDomains or companyNames, plus the target role. If the user hasn't specified one, ask: "What title or seniority are you targeting at these companies?"

Pass a specific title directly as jobTitles (free-form); for broader targeting, resolve seniority / departments via prospecting_contact_filters first.

Step 6 — Enrich and Reveal Phones

Search results are previews carrying a canReveal[] list per contact. Use prospecting_contact_enrich with the contact ids and reveal set from canReveal[].field to reveal direct and mobile numbers — up to 50 contacts per call. Sum the canReveal[].credits and state the total before enriching large batches.

Step 7 — Present Results

Reference Set Used

List the [N] references that were used. Flag any that could not be resolved.

Lookalike Results

Company mode:

Contact mode:

• Phone columns always appear before email — never reversed
• Mark missing phones with —

Summary

• Lookalike [companies/contacts] found: X
• Decision makers enriched: Y
• Verified phones revealed: Z

Step 8 — Offer Next Actions

1. Narrow results — apply additional filters (industry, geography, company size) to the lookalike list
2. Cross with signals — run signal-prospect on this lookalike list to surface which ones are showing buying signals right now
3. Expand the reference set — add more references to improve match quality
4. Export — format as CSV