Audience Builder Reference Private Beta

Complete reference for the Audience Builder’s condition types, operators, and system limits.

Available Plans

enterprise

6 minute read

For a step-by-step walkthrough, see How to Create an Audience.

Condition types

Type	What it filters	Example
Properties	Columns on the data source	`lifetime_value >= 5000`
Relations	Related records (existence or aggregate)	Customers who placed 3+ orders in the last 90 days
Events	Timestamped event tables (with time windows)	3+ `Add to Cart` interactions in the last 7 days
Audiences	Membership in other saved audiences	Not in `Recently Contacted`

Relations

Relationship conditions combine:

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

Events

Events use the same structure as relations but also support time windows:

Mode	Description
Any time	No time filter (default)
In the last	Relative window, for example In the last 30 days
Between	Absolute date range
After	On or after a specific date
Before	Before a specific date

AND / OR logic

Conditions inside a group are joined with AND by default
Toggle to OR to match any condition instead of all
Combine groups for mixed logic: (A AND B) OR (C AND D)

Operator reference

The operators available depend on the 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`	✓	—	—	—
is empty	`empty`	✓	—	—	—
is not empty	`nempty`	✓	—	—	—
set	`nnull`	✓	✓	✓	✓
not set	`null`	✓	✓	✓	✓
in the last	`inlast`	—	—	—	✓

Aggregates: Compare COUNT, SUM, AVG, MIN, and MAX with eq, neq, gt, gte, lt, and lte.

For string columns, is empty / is not empty match a blank value (''). They are distinct from not set / set, which match NULL / non-NULL.

Array operators

Array columns (for example, string tags or ID lists) use a dedicated operator set:

Label	Operator	Description
containing	`contains`	Array includes the given string value
not containing	`ncontains`	Array does not include the given string value
item count equal to	`size_eq`	Array length equals a number
item count greater than	`size_gt`	Array length is greater than a number
item count greater than or equal to	`size_gte`	Array length is greater than or equal to a number
item count less than	`size_lt`	Array length is less than a number
item count less than or equal to	`size_lte`	Array length is less than or equal to a number
is empty	`empty`	Array has zero elements
is not empty	`nempty`	Array has one or more elements
set	`nnull`	Column is not `NULL`
not set	`null`	Column is `NULL`

For arrays, is empty means length zero. A NULL array matches not set, not is empty.

JSON columns

JSON (and warehouse equivalents such as VARIANT / OBJECT) columns support:

Column-level set / not set to check whether the JSON value itself is present
Optional path filter to match a nested scalar inside the column

To filter on a nested value:

Choose the JSON property.
Open the with… path filter.
Enter a dot-separated object path (for example, plan.tier or cart.total).
Choose the leaf type: string, number, or boolean.
Choose an operator and value for that leaf (or path-level set / not set to check whether the path exists).

Note that:

Path filters use object keys only (for example, a.b.c) — array indexes and wildcards in the path are not supported.
Leaf operators follow the same rules as scalar columns of that type. For example, a string leaf supports equal to, containing, is empty, and set. A number leaf supports comparisons such as greater than.
For Amazon Redshift, some SUPER columns can use the same path-filter flow when the builder exposes them for JSON-style filtering.

Audience size calculation

You can see the audience size based on the currently specified conditions whenever you open a saved audience and after every save.

To see the calculation for any unsaved changes, click Calculate size.

Preview sample audience data

Click Preview to see a sample of the audience data.

Examples

The following examples use a typical ecommerce Data Graph where Customers is the data source, related to Accounts, Sales, Customer Interactions, Products, and Stores.

High value customers

Properties: LIFETIME_VALUE >= 5000

Frequent buyers (last 90 days)

Events: Customers → Sales, COUNT(Sales) >= 10, time window in the last 90 days

Churn risk: High value but lapsed

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

Customers with a tag and enterprise plan

Properties (array): TAGS containing vip
Properties (JSON): METADATA with path plan.tier equal to enterprise

Current limitations

Relationship hops: Up to 2 hops. Deeper traversal will be supported in a future release.
Logical nesting: Up to 2 levels deep.
Predicates per audience: Up to 100 (configurable per workspace).
Audience reference depth: Up to 2 levels deep.
Time windows: Apply only to event models. Entity relationships query across all time by default.
Aggregates on multi-hop paths: Allowed only when the path contains a single 1:many edge.

FAQ

How do I use AND and OR?

Each group is all-AND or all-OR. Click the AND/OR label between conditions to switch. To combine both, create separate condition groups.

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

A quantifier answers yes/no, for example, Do any matching records exist?
An aggregate answers a numeric question, for example, What’s the count/sum/avg? (count, sum, avg, min, max).

Use a quantifier for existence and an aggregate for counting or summing values.

What’s the difference between is empty and not set?

is empty / is not empty on string columns match a blank string ('') versus any non-blank value.
On array columns, is empty / is not empty match length zero versus one or more elements.
not set / set match whether the column is NULL or has any value (including '' or an empty array).

A row with email = '' matches is empty, not not set. A row with email IS NULL matches not set, not is empty.

How do I filter on a value inside a JSON column?

Choose the JSON property.
Open the with… path filter.
Enter a dot-separated object path (for example, plan.tier or cart.total).
Choose the leaf type: string, number, or boolean.
Choose an operator and value for that leaf (or path-level set / not set to check whether the path exists).

Use column-level set / not set when you only care whether the JSON column itself is present.