search_people is the widest surface in the API: sixteen parameters, most of them exact-match. The difference between a good search and a wasted one is almost always which filter you reach for, not how many you stack.
Start with a free count
search_linkedin_profile accepts count_only: true, which returns the number of matching results without fetching data or charging tokens. Use it to size a search before committing to it.
Tokens are only charged when data is found and returned. A search that matches nothing costs nothing — but it still counts against your rate limit. See Rate Limits.
Filters are free precision
Search cost is driven by the free-textquery, not by the filters attached to it.
Two rules follow.
Never send a bare
query. Attaching country, seniority, or anything else costs nothing and sharply narrows the result set. A query-only call is the least precise shape available and no cheaper than a filtered one.
Drop query entirely when filters can carry the whole intent. “VPs of sales at US software companies” is fully expressible as seniority, department, industry, and country — as a pure filter search it runs ~40× cheaper than the same request phrased as free text.
This is about execution cost, not billing. Tokens are charged per record returned regardless of query shape — a cheaper query returns faster and puts less load on search capacity, but it doesn’t change what you’re charged.
If all you have is text, add a country
country is ~99% filled — the highest fill rate of any filter. Attaching it to a free-text search discards almost no legitimate matches while removing everything from other markets. It is the cheapest precision available, and the default filter to reach for when you have nothing else.
Put the right thing in query
query is a full-text search across full_name, first_name, last_name, company name, title (~85% filled), and headline (~65% filled). Multi-word queries distribute terms across fields — "John Smith" matches first_name: John and last_name: Smith rather than either alone.
Naming a company in
query gets it diluted across the name and title fields. Use company or company_domain to scope to an employer.
Know each filter’s fill rate
Every exact-match filter silently discards records where that field is empty. Stacking three sparse filters can cut a viable audience to nothing.Choose your industry granularity
Three filters describe industry at different resolutions. Pick the one that matches how specific your targeting actually is.naics_code works at any level of the hierarchy. A 2-digit sector is broad ("54" = Professional Services); a 6-digit code is surgical ("541120" = Offices of Notaries). Shorten the code to widen the net.
Get location right
country, state, and city all filter the person’s location and all require exact stored values.
- Use full English names, not abbreviations —
"Texas", not"TX". "UK"and"HK"exist in the data as dirty values. Prefer"United Kingdom"and"Hong Kong".- City names repeat across regions. Combine with
stateorcountryto disambiguate —city: "Portland",state: "Oregon". citymatches the exact stored city, not its suburbs. For metro-area coverage, filter onstateorcountryinstead.
Target by company size without naming a company
employee_range and revenue_range on search_people filter on the person’s current employer. This finds people at companies of a given size or revenue without you having to supply a company at all.
search_companies. Legacy values ("Small", "Mid-Market", "Enterprise", "Unknown") exist but have far thinner coverage — prefer the numeric ranges.
Match exact strings exactly
Every non-query filter is an exact match. The most common failure is a near-miss on capitalisation or spacing:
"C Suite"has a space, not a hyphen"Marketing & Advertising"uses an ampersand"Medical & Health"uses an ampersand"5001+"and"$500k - $1M"must match character for character
Emails come back in the search
search_people results already include business_email and its business_email_risk_score. For contacts where the search returns an email, you don’t need a follow-up enrichment call. See Getting Valid Emails for what the grades mean.
Paginate deliberately
Results are ranked — a higher_score means a stronger match, so the first page is the best page.
Every response carries
results.total, the count of matching records across all pages. Read it before paginating to decide whether the search is worth walking, then increment offset by your limit.
Related
Searching for Companies
Qualify accounts first, then find people at them.
Getting Valid Emails
Turn a contact into a deliverable address.