Find companies that match the patterns behind your best customers. Not a category filter — a model built from what actually converted for you.
Typical use case: Build a prospecting list that starts from your own closed-won data, not a generic firmographic filter.
What it tells you
A firmographic filter — size, industry, location — treats every company in a category the same, including your competitors’ best customers. A lookalike model starts somewhere different: the specific companies that actually became your best customers, and the patterns that showed up across all of them before they converted.
What Lusha detects
Lusha analyzes your provided list of best customers and surfaces companies matching the same firmographic, technographic, and signal patterns — not a broad category match.
Best for
- Sales — Build outbound lists that mirror your best accounts.
- Marketing — Target ABM campaigns at high-fit lookalike accounts.
- RevOps — Feed lookalike scoring into overall account prioritization.
Turn this signal into action
Build a prospecting list from your wins
Feed in your top 20 customers and get back a ranked list of companies matching their pattern, not a generic filter. Explore playbook →
Prioritize ABM targets by fit
Score target accounts by lookalike similarity to your highest-value customers, not just company size. Explore playbook →
Expand into adjacent markets with confidence
Use lookalikes to find new-market accounts that still match your proven customer pattern. Explore playbook →
Available data
Match score · Matched attributes · Seed company reference · Company firmographics
Available in
API · MCP · Workspace (Enrichment).
Related signals
Lookalike contacts · Headcount increase · IT spend increase · Surge in hiring
