Create a Metric

Create a metric that measures the average weekly sales per user. The setup depends on where your data lives:

Confidence Cloud, the default for every new account: choose an entity, send an event, and Confidence generates the fact table for you.
Warehouse Native: create an entity, an assignment table, and a fact table in your own data warehouse with SQL.

Both paths end with the same step: create the metric.

Before You Begin

You need a Confidence account.
To do steps with AI, connect the Confidence MCP servers or use the Confidence Agent. See Set up Confidence for your AI assistant.

Use the metric in Roll out a change or Run an A/B test to measure the effect of a change.

Set Up the Data

Confidence Cloud
Warehouse Native

On Confidence Cloud you don’t write SQL tables. You send events, and Confidence generates a fact table for each event that references an entity. See Metrics without SQL.

Choose an Entity

An entity represents what you measure, such as a user, an account, or a device. Every account comes with two built-in entities: User and Visitor. This guide uses User.To measure another kind of unit, create an entity in the app or with the API. The MCP servers can’t create entities, but listEntities shows the ones you have.

Define the Event

An event is a record of something that happens in your product, such as a purchase. For this guide, define a purchase event with these fields:

user_id: a string that references the User entity
amount: a number, which becomes a measurement
product: a string, which becomes a dimension

You can define the event schema in advance, or send events first and then review and create the event from the schema that Confidence detects.In the app:

To define the event in advance, select Events in the sidebar and click Create. In the Create event dialog, enter purchase and add the fields. Set user_id to type String with semantic type Entity.
To create the event from events you already send, find purchase in the Events table. It shows as Sent but not stored. In its row, click the outlined Create, review the schema, set user_id to semantic type Entity, and click Create.

At least one String field must have semantic type Entity. Without an entity field, Confidence generates no fact table.With AI: the Flags MCP server creates event definitions with the createEventDefinition tool. For example:

Create a Confidence event definition called "purchase" with a string field
user_id that references the User entity, a double field amount, and a string
field product.

Send Events

Send the event from your application with a Confidence SDK or the events API. To check that events arrive, ask your AI assistant to query event usage. It uses the queryEventsUsage tool.

Use the Generated Fact Table

Confidence creates a fact table for the event as soon as the event exists:

Numeric fields, such as amount, become measurements.
Other fields, such as product, become dimensions.
The event timestamp becomes the timestamp column.

An event that references no entity gets no fact table, because a fact table needs an entity to join assignments on. Assignments come from flag resolves through the built-in connection, so you don’t create an assignment table.The generated fact table has the same ID as the event, for example purchase. It appears in the regular list of fact tables. To find it, select Admin > Data Tables > Fact tables in the app, or ask your AI assistant to list fact tables (listFactTables).

On Warehouse Native you define the entity, the assignment table, and the fact table yourself, with SQL against your own data warehouse.Before you begin:

Connect your data warehouse to Confidence.
Have assignment data and fact data in your data warehouse.

Create an Entity

The MCP servers can’t create entities. listEntities shows the entities you already have. Create new entities in the app or with the API.

An entity is a representation of the users that are part of your experiments. Entities can be anything really, but typically they are users, customers, or visitors.In Confidence, the entity connects other concepts such as experiments, variants, facts, dimensions and metrics to each other. It’s via the entity that Confidence can understand how it should traverse the data in your data warehouse. For example, when you configure an experiment to use the User entity, Confidence only considers metrics that use facts that have a relationship to the User entity.Confidence comes with two default entities: User and Visitor. If you want to experiment on and create metrics for another entity, follow these steps.

Go to Confidence

Navigate to Entities

On the left sidebar, select Admin > Entities.

Click + Create

Enter User as the name of the entity

Select a data type

Select a data type for how your data warehouse represents the entity. For the sake of this tutorial, select String.

Click Create

You now have an entity that you can tie other concepts to. Next, use the entity in an assignment table so that Confidence knows how the entity is assigned to experiments and variants.

Create an Assignment Table

If you already have an assignment table, skip to Create a fact table.

With AI: the Flags MCP server can create the assignment table for you with the createAssignmentTable tool. For example:

Create an assignment table called "assignment" in Confidence that reads from
assignment_log. Use timestamp as the timestamp column, user_id as the User
entity column, experiment_id as the experiment key and variant_id as the
variant key.

The most fundamental part of Confidence Metrics is the experiment assignment data. Without it you can’t compute metrics for an A/B test or any type of experiment.Assignment data is a log of records that contain information about how users were assigned to experiments and variants. Confidence needs the following data:

A timestamp that indicates when the assignment happened.
An entity identifier, normally a user identifier, that uniquely identifies the user that was assigned a variant.
An experiment identifier that identifies the experiment that the user was assigned to.
A variant identifier that identifies the variant that the user was assigned to.

Any table in your data warehouse can store the assignment data. For Confidence to understand the data, you need to tell Confidence where to find it and how to interpret it. You define this in your Assignment Table, which is a query that projects the data outlined above. You can write the query in any SQL dialect that your data warehouse supports.For this tutorial, your assignment data should exist in a table called assignment_log in your data warehouse. Data should be continuously appended to the table as users are assigned to experiments.The table has the following schema:

CREATE TABLE assignment_log (
  timestamp TIMESTAMP,
  user_id STRING,
  experiment_id STRING,
  variant_id STRING
)

Follow these steps to set up an assignment table:

Go to Confidence

Navigate to Assignment tables

On the left sidebar, select Admin > Data Tables > Assignment tables.

Click + Create

Give the table a name

Give the table a name, such as assignment. If your data comes from a particular feature flagging system, you can name it after that, for example launchdarkly.

Enter the SQL query

Enter the SQL query that projects the assignment data from your data warehouse.

SELECT
  timestamp,
  user_id,
  experiment_id,
  variant_id
FROM assignment_log

SELECT
  timestamp,
  user_id,
  experiment_id,
  variant_id
FROM assignment_log

SELECT
  timestamp,
  user_id,
  experiment_id,
  variant_id
FROM assignment_log

Click Run Query

This executes the query and shows you a preview.

To make the query cheap, the query runs with a LIMIT clause to limit the number of rows.

Map the columns

To the right of the result table, you see a form where you can specify the columns in the result that correspond to the assignment data. It’s this mapping that tells Confidence how to interpret the data. Fill in the form as follows:

timestamp as the timestamp column.
For entity, select User and enter user_id as the entity column.
experiment_id as the experiment key column.
variant_id as the variant key column.

Configure data delivery cadence

For Data delivery cadence select Data is delivered continuously. Leave Commit delay at its default value. You can read more about these settings on the data delivery cadence page.

Click Create

You have now set up an assignment table and can move on to create fact tables and metrics for your experiments.

Create a Fact Table

Facts are measurable data that you want to use in your metrics. Facts can be anything that you can measure, such as revenue, number of clicks, or number of impressions.Facts are stored in tables in your data warehouse. For Confidence to understand the data, you need to tell Confidence where to find it and how to interpret it.This tutorial assumes you have your fact data in a table called sales in your data warehouse. Each time a sale occurs, the system adds a new row to the data. amount is the sales amount and product is the product sold.Assume that data is continuously appended to the table as sales happen.The table has the following schema:

CREATE TABLE sales (
  timestamp TIMESTAMP,
  user_id STRING,
  amount FLOAT,
  product STRING
)

The MCP servers can’t create fact tables. Create the fact table in the app, as described here, or with the API.

Follow these steps to set up a fact table:

Go to Confidence

Navigate to Fact tables

On the left sidebar, select Admin > Data Tables > Fact tables.

Click + Create

Give the table a name

Give the table a name, such as sales.

Enter the SQL query

Enter the SQL query that projects the fact data from your data warehouse.

SELECT
  timestamp,
  user_id,
  amount,
  product
FROM sales

SELECT
  timestamp,
  user_id,
  amount,
  product
FROM sales

SELECT
  timestamp,
  user_id,
  amount,
  product
FROM sales

Click Run Query

This executes the query and shows you a preview.

Map the columns

To the right of the result table, you see a form where you can specify the columns in the result that correspond to the fact data. It’s this mapping that tells Confidence how to interpret the data. Fill in the form as follows:

Select timestamp as the timestamp column.
For entity, select User and select user_id as the entity column.
Add amount to the list of measurements.
Add product to the list of dimensions.

Configure data delivery cadence

For Data delivery cadence select Data is delivered continuously. Leave Commit delay at its default value. You can read more about these settings on the data delivery cadence page.

Click Create

The final step is the same on Confidence Cloud and Warehouse Native: create a metric that aggregates the fact data. A metric can be any aggregation of the fact data, such as a sum, an average, or a count. All metrics aggregate measurements over some time window. Watch the following video for a 4-minute overview of the different ways to handle time in metrics.

For this tutorial, create a metric that computes the average weekly sales amount per user.

With AI

The Flags MCP server can create the metric with the createMetric tool. The in-app Confidence Agent can’t create metrics. Create them in the app or with the Flags MCP server. For example:

Create a Confidence metric called "Average weekly sales per user" on the User
entity. Average the amount measurement from the sales fact table, cumulatively
over a 1-week window starting at exposure, filtered to product = "podcast".

To check the metric against real data, ask the assistant to run a metric calculation. It uses createMetricCalculation and queryMetricCalculation.

In the App

To create a metric, follow these steps:

Go to Confidence

Navigate to Metrics

On the left sidebar, select Metrics.

Click + Create

Enter metric details

Enter Average weekly sales per user as the name of the metric and assign yourself as the owner. Skip the description for now.

Select Consumption as the metric type

Select the consumption value

Select the User entity, the fact table that holds your sales data, and the amount measurement as the Consumption value.

Add attribute criteria

Click Add Attribute criteria and select product as the attribute. Set the filter to product is podcast.

Configure when to include users

In the When to Include Users In Metrics Results step, select Cumulatively during a window and 1 week starting at exposure.

Click Next to skip the Suggested usage

Review and create

Review your metric setup, and then click Create.

You now have a metric that you can use in an A/B test or a rollout.

Launch an A/B Test

Use your metric to measure experiment results

Launch a Rollout

Use your metric to monitor a gradual release

Metrics Reference

Deep dive into metric types and configuration

Validate Metrics

Learn how to verify your metrics are working correctly

​Before You Begin

​Set Up the Data

​Choose an Entity

​Define the Event

​Send Events

​Use the Generated Fact Table

​Create an Entity

​Create an Assignment Table

​Create a Fact Table

​Create a Metric

​With AI

​In the App

​Related Resources

Launch an A/B Test

Launch a Rollout

Metrics Reference

Validate Metrics

Before You Begin

Set Up the Data

Choose an Entity

Define the Event

Send Events

Use the Generated Fact Table

Create an Entity

Create an Assignment Table

Create a Fact Table

Create a Metric

With AI

In the App

Related Resources