> ## Documentation Index
> Fetch the complete documentation index at: https://confidence.spotify.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Configure a Metric

> This tutorial shows you how to configure a metric in Confidence.

{user?.groups?.includes("spotify") && <NotForSpotify />}

The tutorial consists of the following steps:

1. [Create an entity](#create-an-entity) that represents the users that are part of
   your experiments.
2. [Create an assignment table](#create-an-assignment-table) that tells Confidence how your
   entity is assigned to experiments.
3. [Create a fact table](#create-a-fact-table) that makes some measurable facts available for metrics.
4. [Create a metric](#create-a-metric) that aggregates a measurement from the
   fact table.

<Note> If you already have an entity and an
assignment table you can skip directly to [create a fact table](#create-a-fact-table). </Note>

This page targets the following audiences:

* Data Engineers or Data Scientists who want to set up Confidence Metrics for
  their organization.

Before you begin:

* You need to have a [Confidence](https://app.confidence.spotify.com) account.
* *(Step 2-3)* You need to have connected Confidence Metrics to your data warehouse.
* *(Step 2-3)* You need to have data for metrics in your data warehouse.

<Tip>
  Use this guide to set up a metric from scratch. Use the metric in the [rollout quickstart](/docs/quickstarts/launch-rollout)
  to measure the effect of the change you roll out.
</Tip>

## Create an Entity

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.

<Steps>
  <Step title="Go to Confidence" />

  <Step title="Navigate to Entities">
    On the left sidebar, select **Admin > Entities**.
  </Step>

  <Step title="Click + Create" />

  <Step title="Enter User as the name of the entity" />

  <Step title="Select a data type">
    Select a data type for how your data warehouse represents the entity. For the sake of this tutorial, select `String`.
  </Step>

  <Step title="Click Create" />
</Steps>

🎉 That's it! You have now created an entity that you can tie other concepts to
in Confidence. In the next section, you use the entity when creating an assignment table
so that Confidence knows how your new entity is assigned to experiments and variants.

## Create an Assignment Table

<Note>
  If you have already set up an assignment table, you can skip this section.
  Jump straight to [Create a metric](#create-a-metric).
</Note>

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](/docs/metrics/assignment-tables),
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:

```sql theme={null}
CREATE TABLE assignment_log (
  timestamp TIMESTAMP,
  user_id STRING,
  experiment_id STRING,
  variant_id STRING
)
```

Follow these steps to set up an assignment table:

<Steps>
  <Step title="Go to Confidence" />

  <Step title="Navigate to Assignment tables">
    On the left sidebar, select **Admin > Assignment tables**.
  </Step>

  <Step title="Click + Create" />

  <Step title="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`.
  </Step>

  <Step title="Enter the SQL query">
    Enter the SQL query that projects the assignment data from your data warehouse.

    <CodeGroup>
      ```sql BigQuery theme={null}
      SELECT
        timestamp,
        user_id,
        experiment_id,
        variant_id
      FROM assignment_log
      ```

      ```sql Redshift theme={null}
      SELECT
        timestamp,
        user_id,
        experiment_id,
        variant_id
      FROM assignment_log
      ```

      ```sql Snowflake theme={null}
      SELECT
        timestamp,
        user_id,
        experiment_id,
        variant_id
      FROM assignment_log
      ```
    </CodeGroup>
  </Step>

  <Step title="Click Run Query">
    This executes the query and shows you a preview.

    <Note>
      To make the query cheap, the query runs with a `LIMIT` clause to limit the number of rows.
    </Note>
  </Step>

  <Step title="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.
  </Step>

  <Step title="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](/docs/metrics/delivery-cadence) page.
  </Step>

  <Step title="Click Create" />
</Steps>

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:

```sql theme={null}
CREATE TABLE sales (
  timestamp TIMESTAMP,
  user_id STRING,
  amount FLOAT,
  product STRING
)
```

Follow these steps to set up a fact table:

<Steps>
  <Step title="Go to Confidence" />

  <Step title="Navigate to Fact tables">
    On the left sidebar, select **Admin > Fact tables**.
  </Step>

  <Step title="Click + Create" />

  <Step title="Give the table a name">
    Give the table a name, such as `sales`.
  </Step>

  <Step title="Enter the SQL query">
    Enter the SQL query that projects the fact data from your data warehouse.

    <CodeGroup>
      ```sql BigQuery theme={null}
      SELECT
        timestamp,
        user_id,
        amount,
        product
      FROM sales
      ```

      ```sql Redshift theme={null}
      SELECT
        timestamp,
        user_id,
        amount,
        product
      FROM sales
      ```

      ```sql Snowflake theme={null}
      SELECT
        timestamp,
        user_id,
        amount,
        product
      FROM sales
      ```
    </CodeGroup>
  </Step>

  <Step title="Click Run Query">
    This executes the query and shows you a preview.
  </Step>

  <Step title="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.
  </Step>

  <Step title="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](/docs/metrics/delivery-cadence) page.
  </Step>

  <Step title="Click Create" />
</Steps>

## Create a Metric

The final step is to create a metric that aggregates the fact data. A metric
can be any aggregation of the fact data, such as sum, average, count, etc.

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.

<iframe className="w-full aspect-video rounded-xl" src="https://www.youtube.com/embed/3faa2_gFjWQ?si=jEeT9zPUFlX8FM9D" title="YouTube video player" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowFullScreen />

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

To create a metric follow these steps:

<Steps>
  <Step title="Go to Confidence" />

  <Step title="Navigate to Metrics">
    On the left sidebar, select **Metrics**.
  </Step>

  <Step title="Click + Create" />

  <Step title="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.
  </Step>

  <Step title="Select Consumption as the metric type" />

  <Step title="Select the consumption value">
    Select the `User` entity, the `sales` fact table, and the `amount` measurement as the **Consumption value**.
  </Step>

  <Step title="Add attribute criteria">
    Click **Add Attribute criteria** and select `content_type` as the attribute. Set the filter to `content_type is podcast`.
  </Step>

  <Step title="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`.
  </Step>

  <Step title="Click Next to skip the Suggested usage" />

  <Step title="Review and create">
    Review your metric setup, and then click **Create**.
  </Step>
</Steps>

That's it! You have now created your first metric. You can now create an A/B
test using the metric. Well done.

## Related Resources

<CardGroup cols={2}>
  <Card title="Launch an A/B Test" href="/docs/quickstarts/launch-abtest">
    Use your metric to measure experiment results
  </Card>

  <Card title="Launch a Rollout" href="/docs/quickstarts/launch-rollout">
    Use your metric to monitor a gradual release
  </Card>

  <Card title="Metrics Reference" href="/docs/metrics/introduction">
    Deep dive into metric types and configuration
  </Card>

  <Card title="Validate Metrics" href="/docs/how-to-guides/validate-metric">
    Learn how to verify your metrics are working correctly
  </Card>
</CardGroup>