Audiences: How to Create an Audience using the Audience Builder

Mon, 01 Jan 0001 00:00:00 +0000

This guide walks you through creating an audience using the visual Audience Builder.

See Audiences Overview for an introduction to Audiences, supported destinations, and prerequisites
See Audience Builder Reference for condition-type descriptions, operators, and examples

Video walkthrough

Prerequisites

Before creating an audience, talk to your Data team to ensure you have a Data Graph in place.

1: Add a new audience

Navigate to Activate > Audiences and click Add audience.

2: Select the data source

Select a data source for the audience.

You cannot change the data source after the audience is created. If you need a different data source, you will need to create a new audience.

Provide a name and description for this audience.
Confirm and click Create to create the audience with this base definition.

3: Add the audience conditions

Click Add Condition to start defining conditions for the audience.

You can add the following conditions to your audience:

Condition	Description
Properties	Filter on columns of the data source directly
Events	Filter on timestamped event tables
Relations	Filter by the existence or aggregate of related entity records
Audiences	Reference existing saved audiences for inclusion or exclusion

Add property-based conditions

Click Add condition > Properties to add a new property-based condition.

Define a condition. Use the logical AND/OR operator to combine multiple conditions. You can also add a Where condition to further narrow the results.
Click Calculate size to check the updated audience size.

Add event-based conditions

Click Add condition > Event to add a new event-based condition.

Select an event model.
Define a condition.
Click Calculate size to check the updated audience size.

For details on behavioral filtering, see Audience Builder Reference.

Add relation-based conditions

Relations connect two entity models via a one-to-many relationship, while Events connect entity models to event models via one-to-many.

Click Add condition > Relations to add a new relation-based condition.

Define a condition. Optionally, add a Where condition to further narrow the results.
Click Calculate size to check the updated audience size.

For details on relationship paths, quantifiers, and aggregates, see Audience Builder Reference.

Add audience-based conditions

Click Add condition > Audiences to add a new audience-based condition.

Select an existing audience to use as a filter.
Specify whether customers should be a part of or should not be a part of that existing audience.
Click Calculate size to check the updated audience size.

For details on audience nesting and exclusion lists, see Audience Builder Reference.

4: Verify and finalize the audience

Click Calculate size to see how many customers match the final audience conditions.
If you’re not satisfied, tweak the audience conditions and recalculate the size to see the impact.
Click Save to finalize and save the audience — this triggers a final size calculation once again.

5: Sync to destinations

You can sync an audience to multiple destinations, each with its own refresh cadence.

See Syncs for details on how to connect an audience to a destination and activate it on a schedule.

Audiences: Audience Builder Reference

Mon, 01 Jan 0001 00:00:00 +0000

This page covers the Audience Builder’s condition types, operators, and system limits.

For a step-by-step walkthrough on creating an audience, see How to Create an Audience from a Data Graph.

Condition types

The Audience Builder supports four condition types. You can combine any of them freely using AND/OR logic.

Properties (attribute conditions)

Use the Properties tab to filter on columns of the base entity directly.

Example: “Customers with lifetime_value >= 5000.”

The operators available depend on the column’s data type. See the Operator reference below.

Use the Relations tab to filter the base entity by the existence or aggregate of its related records.

Examples:

“Customers belonging to a Corporate account.” (single-hop, many:1)
“Customers who placed at least 3 orders in the last 90 days.” (single-hop, 1:many, count aggregate + time window)
“Customers whose total spend at Flagship stores in the last 90 days is over $1,000.” (two-hop: Customers → Sales → Stores, with sum aggregate and a condition on the final node)

Relationship conditions combine:

A path of one or two relationship hops
An optional condition on the final entity (for example, store_type = "Flagship")
Either a quantifier (any / all / none) or an aggregate (count, sum, avg, min, max), or both
An optional time window (applies when the related entity is an event model)

Events (behavioral conditions)

Use the Events tab to filter on timestamped event tables, for example, Customer Interactions, Purchase, or Loyalty Points. Events use the same structure as relationships but also let you set a time window.

Example: “Customers who had 3 or more Add to Cart interactions in the last 7 days that were not converted to a purchase.”

The following time-window modes are supported:

Label	Description
Any time	No time filter (default if omitted)
In the last	Dynamic relative window, for example “in the last 30 days” (`{ interval, unit }` where unit is `days`, `weeks`, `months`, or `years`)
Between	Absolute date range
After	On or after a specific date
Before	Before a specific date

Audiences (composition and exclusion)

Use the Audiences tab to reference saved audiences that share the same base entity. This is useful for:

Reusable building blocks: Create a “VIP Members” audience once and reuse it across many audiences.
Exclusion lists: Prevent over-messaging with not in "Recently Contacted".

Example: “High-LTV customers who are not in the New Customer Welcome audience.”

Combine conditions with AND / OR

By default, conditions inside a group are joined with AND
Toggle a group to OR to match any condition instead of all
Nest one group inside another for expressions, for example, (A AND B) OR (C AND D)

Operator reference

Audience expressions use a fixed set of operators. The operators available in each dropdown depend on the selected column’s data type.

Label	Operator	String	Number	Boolean	Datetime
equal	`eq`	✓	✓	✓	✓
not equal to	`neq`	✓	✓	✓	✓
greater than	`gt`	—	✓	—	✓
greater than or equal to	`gte`	—	✓	—	✓
less than	`lt`	—	✓	—	✓
less than or equal to	`lte`	—	✓	—	✓
between	`btw`	—	✓	—	✓
not between	`nbtw`	—	✓	—	✓
in	`in`	✓	✓	✓	✓
not in	`nin`	✓	✓	✓	✓
containing	`like`	✓	—	—	—
not containing	`nlike`	✓	—	—	—
set	`nnull`	✓	✓	✓	✓
not set	`null`	✓	✓	✓	✓
in the last	`inlast`	—	—	—	✓

inlast takes an interval and unit, for example “in the last 30 days” or “in the last 6 months”.

Aggregate comparison operators: You can compare COUNT, SUM, AVG, MIN, and MAX with eq, neq, gt, gte, lt, and lte.

Preview and size estimation

Automatic preview: When you open a saved audience, RudderStack runs a preview automatically. It re-runs after every save.
Manual preview: Use the Calculate size button to preview unsaved changes in the builder.
Sampling: If your data team has enabled sampling in the Data Graph, the preview runs against a sample of the warehouse data for speed. The reported size is an estimate — RudderStack computes the exact membership at sync time.
Sample members: Alongside the size, the preview also shows a sample of matching record IDs so you can sanity-check the definition.

Examples

The following examples are drawn from a typical ecommerce Data Graph where Customers is the base entity and is related to Accounts, Sales, Purchase, Customer Interactions, Products, and Stores.

High value customers

Customers with a lifetime value of 5,000 or more.

Tab: Properties
Condition: LIFETIME_VALUE >= 5000

Big spenders (last year)

Customers whose total sales exceeded $3,000 in the last year.

Tab: Events (or Relations if Sales is modeled as an entity)
Relationship: Customers → Sales
Aggregate: SUM(TOTAL_AMOUNT) >= 3000
Time window: In the last 365 days

Frequent buyers (last 90 days)

Customers who made 10 or more purchases in the last 90 days.

Tab: Events
Relationship: Customers → Sales
Aggregate: COUNT(Sales) >= 10
Time window: In the last 90 days

Churn risk: High value but lapsed

High-value customers who have not made a purchase in the last 90 days.

Group 1 (Properties): LIFETIME_VALUE >= 500
Group 2 (Events): Customers → Sales, quantifier none, time window in the last 90 days
Combine with: AND

Engaged non-buyers, excluding win-back

Customers who are highly engaged but haven’t purchased and aren’t already being targeted by the win-back campaign.

Events: Customer Interactions count >= 5 in the last 14 days
Events: Loyalty Points count >= 1 in the last 30 days
Events: Sales quantifier = none in the last 30 days
Audiences: not in Win-Back Lapsed High-Value
Combine with: AND

Current limitations

Relationship hops: A relationship path can currently traverse up to 2 hops. Deeper traversal will be supported in a future release.
Logical nesting: Expressions can nest AND/OR groups up to 2 levels deep.
Predicates per audience: A single audience can contain up to 100 predicates (configurable per workspace).
Audience reference depth: An audience can reference other audiences up to 2 levels deep.
Time windows: Time windows apply only to event models. Entity relationships query across all time by default.
Aggregates on multi-hop paths: Aggregates on a multi-hop relationship are flattened across the full path and are allowed only when the path contains a single 1:many edge.

FAQ

What’s the difference between a quantifier and an aggregate?

A quantifier answers a Yes/No question. For example, “do any matching related records exist?” (any, all, none).

An aggregate answers a numeric question. For example, “what’s the count/sum/avg/min/max across related records?”. Use a quantifier for existence, an aggregate for counting or summing.

Why is the size in the preview an estimate?

If your Data Graph has sampling enabled, the preview runs against a sample of the warehouse rather than the full table to keep response times fast. RudderStack computes the exact membership at sync time.

RudderStack Docs – Audience Builder