About Audience Group Filters

Filters help make campaigns more effective by targeting the right customers at the right time. You can use filters to create audience groups based on specific details like behavior, interests, demographics, transactions, and more.

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Note
If your org has real-time audience filtering enabled, the filter list is grouped into two sections when you add a filter:

Realtime filters: Purchase pattern, Cart & catalog promotion, Behavioral event, and User profile filters that support real-time evaluation.

Batched filters: all other filter categories, evaluated on the regular batch schedule.

You can search across both sections, and expand or collapse each section independently. A filter group can only use filters from one section at a time. You can't combine Realtime and Batched filters in the same group. Raise a Jira ticket to the Product Support Team to get it enbaled for your organization.

Type of Audience Group filters-

Category	Description
Loyalty based filters	Build audience groups using loyalty-related attributes such as active, expired, lifetime, redeemed, and about-to-expire points, registration date and store, current tier, and slab migrations.
Transaction based filters	Build audience groups based on customer purchases, including transaction date, time, quantity, visit or transaction count, store/channel, bill amount, and other order-level attributes.
Campaigns & Coupons based filters	Build audience groups based on coupon issuance, redemption, expiry, message delivery count, campaign contact status, timeline journey status, or campaign response behavior.
User profile based filters	Build audience groups based on customer-specific details like demographics, subscription preferences, fraud status, NDNC status, unique identifiers, channel presence, custom fields, test/control group participation, age, and loyalty slab movement.
Lead based filters	Build audience groups based on current lead status or status change history, along with attributes like created date, owner, source, sub-status, and associated products.
Cart & Catalog promotion filters	Create audience groups based on the number of promotions earned by customers during a specific period.
Behavioral event-based filters	Segment users based on custom behavioral events and event attributes, such as frequency and context of the action performed.
AI powered filters	Segment users based on AI-driven predictions like transaction likelihood, lapsation risk, response probability, and product/store affinity.
Purchase pattern based filters	Create audience segments by analyzing customers' shopping frequency, timing, spend behavior, discounts availed, and product preferences.
Common filters	Use these standard options date, store, points, channel status, lead status, and more—to refine your audience across most filters.