Build targeted audiences by combining demographic, behavioral, and geographic filters.

Step 1: Discover Traits

Trait discovery depends on the domain:

Non-geo traits — use trait_search with a natural language query. See trait_search for full parameter details.

{
  "entity_type": "person",
  "query": "high income professionals interested in golf and fitness",
  "limit": 10
}

Results include trait_hash (use this in expressions), size (population count), and similarity_score. Use trait:// resources (e.g., trait://person?domain=demographic) to browse non-geo traits by domain.

Geo traits (states, ZIPs, counties, DMAs, CBSAs/MSAs, congressional districts) — trait_search does not surface geo entries. Use one of:

• trait_get(entity_type="person", domain="geo", trait_name=<type>, trait_value=<value>) for a specific known boundary (e.g., trait_name="state", trait_value="CA").
• Browse trait://person?domain=geo[&trait_name=state|county|zip5|dma|cbsa|msa|congressional_district] to page through the full geo catalog. trait_name scopes to one boundary type (geo reads only).

See the Named Geographies section of entity_find for boundary type formats and examples.

Step 2: Build and Execute Your Audience

Combine trait hashes using boolean expressions and query with entity_find. See entity_find for expression syntax and full parameter details.

{
  "entity_type": "person",
  "expression": "a1b2c3d4e5f67890 AND (b2c3d4e5f6789012 OR c3d4e5f67890a1b2)",
  "identifier_types": ["email"],
  "format": "csv"
}

To filter by location, add a location object with latitude, longitude, radius, and unit. Use geocode_location to convert a city name to coordinates first.

{
  "entity_type": "person",
  "expression": "a1b2c3d4e5f67890 AND b2c3d4e5f6789012",
  "location": { "latitude": 37.7749, "longitude": -122.4194, "radius": 25, "unit": "miles" },
  "identifier_types": ["email"],
  "format": "csv"
}

Choose a targeting strategy based on campaign goals:

Related guides:

• ICP Analysis
• Identity Enrichment