Lookalike search
In short
Lookalike search is a way of finding new target accounts by starting from example companies instead of filters. You enter a best customer, or a small set of them, and the tool returns other companies that resemble them.
It differs from lookalike modeling, which is the scoring method behind it. Lookalike search is the workflow a rep or marketer runs: pick examples, review results, and export a list.
How to run a lookalike search
1. Pick your seed companies
Choose one to five customers you would happily clone. Prefer accounts with high retention and a clear use case over merely large ones.
2. Run the search
Submit the seeds to a tool that supports similarity search. Results are ranked by closeness to your examples.
3. Review the first results
Read the top 20 or so by hand. If they look wrong, the seed is probably too mixed or too unusual. Adjust and rerun.
4. Filter and export
Apply hard filters, such as region or size, to remove accounts you cannot serve, then add the list to your target account list.
When lookalike search beats filter-based search
- You cannot describe the fit: Sometimes the best customers share traits nobody has named. Examples reveal them.
- Niche markets: Industry labels may be too coarse. Similar companies often share a business model rather than a category.
- Fast list building: One good example can produce a first list in minutes.
Example
Say a team sells to regional logistics software firms. It seeds the search with three retained customers, reviews the top results, finds that results skew toward very large companies, and adds an employee-count filter. This is an illustration, not a result from any dataset.
Common mistakes
- Seeding with a bad customer: One poor-fit seed pulls the whole list off target.
- Skipping validation: Always sample results against your ICP before outreach.
- Treating it as the whole strategy: A similar company is not a company that is ready to buy. Pair it with timing signals.
Lookalike search FAQs
What is a lookalike search?
A lookalike search finds companies that resemble examples you provide, such as your best customers, instead of filtering by fixed criteria. The tool returns similar accounts ranked by closeness, which you review, filter, and use as a target list.
How do you do a lookalike search for B2B prospecting?
Pick a few best-fit customers as seeds, run the search in a tool that supports it, review the top results by hand, then apply hard filters such as region or size before exporting the list for outreach.
How is lookalike search different from filter-based search?
Filter-based search needs you to know which traits matter and set them as criteria. Lookalike search infers the traits from your examples, which helps when the common pattern is hard to describe or spans several fields.
How many example companies should you use in a lookalike search?
Between one and a handful of strong examples is a common starting point. More examples can broaden results, but mixing very different customers can blur them. Review the output and adjust the seed set as needed.
