Computed Attributes

Computed attributes are derived metrics about customer entities. They transform raw warehouse data into meaningful values — like a customer’s lifetime value, their most-purchased product category, or the number of days since their last login — that can then be used to build audiences.

What Is a Computed Attribute?

A computed attribute is a single computed value attached to an entity type. Every computed attribute produces one value per entity instance. For example, a computed attribute called total_order_value on the User entity type would compute one number for each user in your warehouse.

Computed attributes are warehouse-native and computed on read: Zeotap generates SQL and runs it directly against your data warehouse whenever the attribute is referenced — in an audience, an estimate, a sync, or a journey. No data is copied out of your warehouse to compute them, and no values are stored in a separate table. Each result always reflects your latest warehouse data and the latest definition.

Computed Attribute Types

Zeotap offers six ways to define computed attributes, ranging from full SQL flexibility to visual no-code builders:

Type	What it produces
Aggregation	A count, sum, average, min or max over related records
Occurrence	A single value picked by position or frequency — first, last, most or least frequent
List	Related values collected into a list
List of objects	The top N related rows as a list of objects
SQL	Anything you can express as a SQL expression
Formula	A new value composed from computed attributes you already have

SQL Computed Attributes

SQL Computed Attributes let you write custom SQL to compute any metric. This is the most flexible option — anything you can express in a SQL query, you can turn into a computed attribute.

Use when: You need multi-table joins, window functions, CTEs, warehouse-specific functions, or any computation that doesn’t fit the visual builders.


-- Example: Customer lifetime value
SELECT
  user_id,
  SUM(order_total) - SUM(refund_amount) AS lifetime_value
FROM orders
LEFT JOIN refunds ON orders.order_id = refunds.order_id
GROUP BY user_id

Aggregation Computed Attributes

Aggregation Computed Attributes use a visual builder to define common aggregations without writing SQL. You pick a source table, an aggregation function, a column to aggregate, and optional filters.

Use when: You need a straightforward count, sum, average, min, max, or count distinct over a single table.

Supported functions:

Function	Description	Example
Count	Number of rows	Total number of orders
Sum	Sum of a numeric column	Total revenue
Average	Mean of a numeric column	Average order value
Min	Minimum value	First purchase date
Max	Maximum value	Most recent login
Count Distinct	Unique values in a column	Number of distinct products purchased

Occurrence Computed Attributes

Occurrence attributes pick one value out of a customer’s related rows, chosen by position in time or by how often it appears.

Use when: you want “the value that came first”, “the latest one”, or “the one they pick most often” — a first-touch channel, a most-recent shipping city, a favourite product category.

Function	Picks
First	The value on the earliest related row
Last	The value on the most recent related row
Most frequent	The value that appears on the most related rows
Least frequent	The value that appears on the fewest

First and Last need to know what “earliest” means, so they order by an order column. Leave it unset and the source model’s timestamp column is used; a model with no timestamp column requires you to name one explicitly.

List Computed Attributes

List attributes collect a column’s values across a customer’s related rows into a single list.

Use when: you want every value rather than one — all the categories someone has bought from, every campaign that touched them.

Options are a value column, an optional distinct toggle to collapse repeats, an optional limit on how many values to keep, and filters to narrow which related rows count.

List of Objects Computed Attributes

List-of-objects attributes collect whole related rows — several columns each — rather than one column’s values.

Use when: a list of bare values loses too much. “The last 10 products purchased” is only useful with the product name, price and date together.

You choose which columns to capture, the key each becomes in the resulting object, how the rows are ordered, and how many to keep. The result is an array of objects, which destinations that accept nested payloads can receive directly.

Formula Computed Attributes

Formula Computed Attributes combine existing computed attributes using arithmetic and logical expressions. They operate on the results of other computed attributes, resolved as part of the same on-read computation.

Use when: You want to derive new metrics from computed attributes you’ve already built, like ratios, scores, or boolean flags.


average_order_value = total_revenue / order_count
is_high_value = lifetime_value > 1000

Computed Attribute Properties

Every computed attribute has these core properties:

Property	Description
Name	Human-readable label displayed in the UI and available for audience building
Slug	URL-safe identifier used in the API to reference the computed attribute
Entity Type	The entity type this computed attribute is computed for (e.g., User, Account)
Data Type	The output type: string, number, boolean, date, or timestamp
Type	SQL, Aggregation, or Formula

How Computed Attributes Are Used

Once computed, computed attributes are available throughout Activation:

Audience conditions — Filter customers by computed attribute values (e.g., lifetime_value > 500)
Formula inputs — Reference computed attributes in formula expressions to derive new metrics
Audience sync fields — Include computed attribute values as fields sent to destinations
Insights — Track computed attribute distributions and trends over time

Computed Attribute Evaluation

Computed attributes are evaluated on read: Zeotap generates SQL and runs it against your warehouse whenever the attribute is referenced, so results always reflect your latest data and definition. Dependency ordering (formula computed attributes that depend on other computed attributes) is resolved as part of that same on-read computation.

Next Steps

SQL Computed Attributes — Write custom SQL for maximum flexibility
Aggregation Computed Attributes — Use the visual builder for common metrics
Formula Computed Attributes — Combine existing computed attributes with expressions
Computed Attribute Evaluation — Understand how and when computed attributes are evaluated