Audience filters

The filter sources you build an audience from — Primer data, events, CRM and datasets — with sample filters.

Filters define who's in an audience. They're grouped by source — where the data comes from — and you can combine filters from any source. Each filter matches with operators like is / is not; numeric filters use ranges.

Primer data

Primer's own person and company data. This is the default source, available to everyone.

• People — Job Title, Seniority, Department, Person Location, Skills, Degrees, Majors.

• Companies — Industry, Headcount, Annual Revenue, Company Location, Domain, Keywords, Technology, NAICS Code, Founded Year.

Department and Industry are picked from fixed, Primer-defined lists so audiences stay comparable; the audience builder shows every available value as you filter. Keywords and Technology accept your own values.

Events

The people your website pixel has identified — for example everyone who visited a page or arrived from a campaign. The pixel must be Connected; these filters have nothing to match against until it is, so they only appear once it's connected.

• Pages visited — filter by the page someone viewed.

• UTM parameters — source, medium, campaign, term and content, matching the first, last or any touch.

• Form fills — whether someone submitted a form, and which one.

• Engagement — number of visits, session time, referrer, device and browser, intent level and traffic type.

• Audience membership — whether someone belongs to another of your audiences, most often to exclude one audience from another.

CRM

Custom fields from a connected CRM, so you can filter on your own data — Salesforce (Lead, Contact, Account, Opportunity, Campaign) and HubSpot (Contact, Company, Deal, Form). These appear once the CRM is connected.

Datasets (CSV Files)

Filter against lists you've imported as a CSV.

Why some filters are slower to process

Most filters resolve against pre-indexed (normalized) values, so estimates update quickly. A CRM filter on a custom field that hasn't been normalized is the exception: Primer has to scan the raw values instead of a prepared index, which makes the size and match-rate estimates take longer to compute. When your audience uses one, the builder shows a turtle note — "Unnormalized filters can cause slower estimates."

This is only a speed note, not an error: the filter still matches correctly and the audience still builds and syncs. If estimates feel slow, it's usually one of these CRM filters at work; removing it makes estimates snappy again, or you can simply wait for it to finish.