Composite Metrics

Composite metrics add or subtract 2+ aggregated metric sources to produce a single result.

Use cases

Composite metrics are a flexible metric type designed to sum aggregated metric sources at the user level. Use them when you need to add or subtract two or more aggregated values into a single signal, such as:

Net value (e.g., revenue minus refunds)
Unit-level experiment change (latestvalue minus first value)

Calculation

At the unit level, composite metrics first compute the aggregations of each component. Statsig then adds or subtracts the aggregated results according to the formula specified (for example: A + B or A - B + C).

Statsig performs this calculation at a daily, 7-day, and cumulative level during experiment analysis.

Composite metrics don't currently support daily rollups in turbo mode.

At the group level, Statsig calculates the mean as the average of the unit-level composite aggregation.

Example

If you define a composite gap metric as Max Price - Min Price, Statsig computes Max Price and Min Price for each user, then subtracts to get each user's net value. The experiment group mean is the average of those per-user results.

The SQL for the individual components would look like the following:

MAX Component:

sql

-- Unit Level
SELECT
  source_data.unit_id,
  exposure_data.group_id,
  MAX(source_data.value_column) as value
FROM source_data
JOIN exposure_data
ON
  -- Only include users who saw the experiment
  source_data.unit_id = exposure_data.unit_id
  -- Only include data from after the user saw the experiment
  -- In this case exposure_data is already deduped to the "first exposure"
  AND source_data.timestamp >= exposure_data.timestamp
GROUP BY
  source_data.unit_id,
  exposure_data.group_id;

-- Experiment
SELECT
  group_id,
  COUNT(distinct unit_id) total_units
FROM exposure_data
GROUP BY group_id;

-- Group Level
SELECT
  group_id,
  SUM(value)/SUM(total_units) as mean
FROM unit_data
JOIN group_data
USING (group_id)
GROUP BY group_id;

MIN Component:

sql

-- Unit Level
SELECT
  source_data.unit_id,
  exposure_data.group_id,
  MIN(source_data.value_column) as value
FROM source_data
JOIN exposure_data
ON
  -- Only include users who saw the experiment
  source_data.unit_id = exposure_data.unit_id
  -- Only include data from after the user saw the experiment
  -- In this case exposure_data is already deduped to the "first exposure"
  AND source_data.timestamp >= exposure_data.timestamp
GROUP BY
  source_data.unit_id,
  exposure_data.group_id;

-- Experiment
SELECT
  group_id,
  COUNT(distinct unit_id) total_units
FROM exposure_data
GROUP BY group_id;

-- Group Level
SELECT
  group_id,
  SUM(value)/SUM(total_units) as mean
FROM unit_data
JOIN group_data
USING (group_id)
GROUP BY group_id;

Composite Aggregation:

sql

-- Unit Level
SELECT
  source_data.unit_id,
  exposure_data.group_id,
  MAX(source_data.value_column) - MIN(source_data.value_column) AS value
FROM source_data
JOIN exposure_data
ON
  -- Only include users who saw the experiment
  source_data.unit_id = exposure_data.unit_id
  -- Only include data from after the user saw the experiment
  -- In this case exposure_data is already deduped to the "first exposure"
  AND source_data.timestamp >= exposure_data.timestamp
GROUP BY
  source_data.unit_id,
  exposure_data.group_id;

-- Experiment
SELECT
  group_id,
  COUNT(distinct unit_id) total_units
FROM exposure_data
GROUP BY group_id;

-- Group Level
SELECT
  group_id,
  SUM(value)/SUM(total_units) as mean
FROM unit_data
JOIN group_data
USING (group_id)
GROUP BY group_id;

Options

Cohort Windows
- You can specify a window for data collection after a unit's exposure. For example, a 0-1 day cohort window would only count actions from days 0 and 1 after a unit was exposed to an experiment
  - Select Only include units with a completed window to remove units from pulse analysis for this metric until the cohort window has completed
Winsorization
- Specify a lower and/or upper percentile bound to winsorize at. Statsig applies these bounds separately to the top-level metric and its individual components.
Baked Metrics
- Baked Metrics let you specify how long a metric needs to mature. This is common in situations like chargebacks or cancellations. Statsig delays loading the data until the window has elapsed, and only calculates pulse results for that metric after the unit's metric has matured.

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