Finds targeting options by name, such as locations, interests or behaviors, and returns the ids to use in ad targeting.

Resolve a human-readable query into the platform's opaque targeting ids used in the TargetingSpec (countries/regions/cities/zips/metros geo keys, and interests/behaviors entity ids) on POST /v1/ads/create, POST /v1/ads/targeting/reach-estimate, and saved_targeting audiences.

The dimension param selects what is searched:

• geo: locations, further scoped by geoType
• interest
• interestSuggestion: Meta only. Interests related to the interest names in q, from Meta's interest suggestions. q lists interest names, case sensitive, names and not ids: either comma-separated (NBA,Michael Jordan) or, for names that contain a comma, a JSON array of strings (["Earth, Wind & Fire","Yoga"], URL-encoded). A q starting with [ is always read as JSON. A q with no name or malformed JSON returns 400 naming q. Meta also suggests behaviors and demographics; Zernio keeps only interests (entries whose path starts with Interests), so every id goes in TargetingSpec.interests, like interest results. Zernio asks Meta for three times limit (at most 100) before filtering, so fewer than limit results can come back. Meta weights the first name heavily: a multi-name q mostly returns suggestions for the first one
• behavior: Meta, TikTok and LinkedIn, matched by name; ids feed TargetingSpec.behaviors. Meta: its fixed behaviors catalog (e.g. Small business owners, Frequent Travelers). TikTok: video and creator interaction categories (e.g. Software & Apps), with ids like video:1913101 or creator:24001 and path starting with Video interactions or Creator interactions (hashtags are their own hashtag dimension). LinkedIn: member behaviors (e.g. Frequent Travelers, Job Seekers, Recently Promoted), ids like urn:li:memberBehavior:9. Google has no separate behavior catalog: its in-market and affinity segments come back from interest, and X removed behavior targeting from its Ads API
• interestKeyword: TikTok only. The "Additional interests" of TikTok Ads Manager (e.g. folk returns Folk Music), from TikTok's keyword recommendations for the seed q. Ids look like keyword:123456 and go in TargetingSpec.interests, next to interest categories. Only keywords TikTok lets an ad group target come back (status EFFECTIVE). TikTok returns no audience size
• hashtag: TikTok only. Hashtags recommended for the seed q, to target people who viewed videos with them. Ids look like hashtag:123456 and go in TargetingSpec.behaviors. Only hashtags TikTok reports ONLINE come back. Spaces in q are removed first, because TikTok recommends no hashtags for a multi-word seed (acoustic guitar searches acousticguitar)
• income: the household-income tiers the platform can target (Meta, TikTok, Google). The id is the normalized tier (top_5, top_10, top_10_25, top_25_50) to pass as TargetingSpec.incomeTier, never a platform segment id. Meta's tiers are US-only ZIP-code percentiles (label and audienceSize come live from Meta), TikTok expresses all four and Google only top_10. q is matched against the label, so top or income lists every tier
• language: Google-only
• workPosition, workEmployer, workIndustry: the Meta-only work demographics, whose ids feed TargetingSpec.workPositions/workEmployers/workIndustries
• industry, jobFunction, seniority, companySize, skill: the LinkedIn-only B2B facets, whose URNs feed TargetingSpec.industries/jobFunctions/seniorities/companySizes/skills
• community: Reddit only. Subreddits matching q; the id is the subreddit name for TargetingSpec.communities / excludedCommunities, platformId Reddit's own id and audienceSize its subscriber count when Reddit reports it
• conversationKeyword: Reddit only. Reddit's keyword suggestions for the seed q; the id is the keyword for TargetingSpec.conversationKeywords / excludedConversationKeywords

Availability of each dimension varies by platform. A dimension a platform cannot search returns a 400 naming dimension; it never falls back to another dimension, and every result's type is the requested dimension (or the geo level for geo).

TikTok interest searches TikTok's interest category catalog (about 700 categories over four levels, path holds the parent categories), matched by name in Zernio. The ids are what TargetingSpec.interests sends to TikTok as interest_category_ids. For interestKeyword and hashtag, a q made only of prefixed ids (keyword:101,keyword:102 or hashtag:201) looks those ids up instead of searching, unavailable ones included with their status (INEFFECTIVE or OFFLINE), so ids read back from an ad group (interest_keyword_ids, actions in nativeSettings) resolve to names. An interestKeyword lookup takes at most 50 ids (more returns 400 naming q). Work industries are a fixed ~30-entry Meta catalog with no server-side query, so workIndustry matching, ranking and limit happen in Zernio. language is likewise a fixed, checked-in table of Google's targetable language_constant rows (id, ISO code, name) matched by name or code, capped at 20, with no network call; its ids feed TargetingSpec.languages.

Results are normalized across platforms into a single shape, so the same client code consumes Meta, TikTok, LinkedIn, X, Pinterest, Google and Reddit results.

TikTok geo searches return every matching level in one list (type is country, region, city, district, or metro for DMA areas), and geoType is not applied. Results are scoped to the advertiser's targetable markets (pass adAccountId when the connection holds several advertisers). A two-letter q also matches a country by ISO code (q=GB returns the United Kingdom first). A country result's id is its ISO 3166-1 alpha-2 code, for targeting.countries; every other id is TikTok's numeric location id, usable in regions/cities/metros keys on POST /v1/ads/create.

LinkedIn geo searches also return every matching level in one list, and neither geoType nor countryCode is applied: LinkedIn's typeahead only returns a name and a URN per result, with no level or country field to filter on. A result whose URN is a country Zernio holds a code for has type: country and its ISO 3166-1 alpha-2 code as the id, for targeting.countries. Every other result has type: region and keeps its urn:li:geo:* URN as the id, usable as a regions[].key on POST /v1/ads/create, POST /v1/ads/boost and POST /v1/ads/targeting/reach-estimate (LinkedIn puts countries and regions in the same locations facet, so both target the same way).

LinkedIn B2B searches (industry, jobFunction, seniority, companySize, skill) return the full URN to pass straight back, so no URN id fragment has to be assembled by hand: urn:li:industry:4, urn:li:function:8, urn:li:seniority:6, urn:li:staffCountRange:(51,200), urn:li:skill:1346. Only industry and skill are server-side name searches (LinkedIn's typeahead finder). LinkedIn exposes no typeahead for job functions, seniorities and company sizes, so Zernio fetches each whole table (26, 10 and 9 entries), caches it, and does the matching, ranking and limit cutoff itself. Those three never carry audienceSize, and countryCode and geoType are not applied to any of the four.

Google geo searches resolve against Google's geoTargetConstants and return every matching level in one list; geoType is not applied (Google's target_type is an open taxonomy that does not map one-to-one onto the geoType enum), so filter client-side on the returned type (country, region, city, zip, metro, or the lowercased Google target type for rarer levels). countryCode scopes the search to one country. A country result's id is its ISO 3166-1 alpha-2 code, for targeting.countries; every other id is Google's numeric criterion id, usable as a regions/cities/zips/metros key on POST /v1/ads/create. Google city radius is not supported (pass a customLocations lat/lng pin for a radius); country targeting also accepts plain ISO codes via countries with no search call.

Pinterest resolves against three whole-catalog endpoints (interests, locations, regions) with no server-side query or pagination, so matching, ranking and the limit cutoff all happen in Zernio; the catalog is independent of any ad account and results never carry audienceSize. Names come back localized to the connected Pinterest account's language (there is no way to force a locale), so match against whatever language that account returns.

geoType routes to a different Pinterest catalog:

• country and metro_area read the locations catalog (type is country or metro)
• region reads the regions catalog (type is region, its id a regions[].key on POST /v1/ads/create)
• all and the default city merge both catalogs with honest per-entry types, since Pinterest has no city-level catalog and city is an alias for all, not a literal city search
• zip, subcity, neighborhood, place and geo_market return a 400: Pinterest exposes no postal-code catalog, pass postal codes directly as targeting.zips: [{ key }] on POST /v1/ads/create

Reddit (redditads connection) searches geo, interest, community and conversationKeyword; any other dimension returns 400. Its geo search honours geoType city (the default, a server-side city search), zip (q is the postal code), country, region and metro_area (matched in Zernio against Reddit's geolocation catalog) and all; other values return 400. countryCode scopes every geo search. Each result's id is Reddit's geolocation id, usable as a regions/metros/cities/zips key (country ids go in countries). interest matches Reddit's interest catalog in Zernio, with the category as path.

For geo queries, q should contain only the locality name (e.g. "Amsterdam", not "Amsterdam, NL"). Use countryCode to disambiguate.

Meta language: Meta matches q against names in one language and returns names and path in that language. Pass locale (e.g. pl_PL) to choose it. Without locale, the behavior and workIndustry catalogs are in en_US, and interest, interestSuggestion, geo, workPosition and workEmployer follow the language of the Facebook user who connected the account (so a Russian-language Facebook user gets Russian paths, and Polish names need locale=pl_PL). A non-English behavior or workIndustry query without locale never matches, so pass the locale of the names you search. Meta's interest search matches its own interest names and has no fuzzy or synonym mode, so a phrase Meta does not use as an interest name (e.g. dental implants) can return nothing while a shorter word (dentistry) does. If Meta returns an empty behavior, workIndustry or income catalog the request fails with a 502 platform_api_error instead of an empty results list.