# Sum Metrics

## Use cases

Sum metrics calculate the sum of a numeric column, which makes them well suited to comparing behavior across groups. Common examples are:

* Totalling revenue by summing a `revenue` column from purchase logs
* Totalling time spent by summing an aggregated `time_spent` metric on a user-day fact table

## Calculation

At the unit level, sum metrics take the SUM of their input column.

At the group level, Statsig calculates the mean as the SUM of the unit-level sums, divided by the count of UNIQUE UNITS exposed to the experiment.

The SQL for this calculation:

```sql expandable
-- Unit Level
SELECT
  unit_id,
  group_id,
  SUM(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;
```

### Methodology notes

Sum metrics use the SQL SUM aggregation. Refer to the Options section below for many advanced options.

## Options

* Metric Breakdowns: configure Metadata Columns to group results by for dimensional views in pulse results.
* Multi Source: build sums from multiple metric sources, equivalent to a UNION in SQL. Useful when the same measure exists in sharded or disparate tables.
* Winsorization: specify a lower and/or upper percentile bound. Statsig clamps all values below the lower threshold or above the upper threshold to reduce the impact of outliers.
* Capping: specify an exact cap value (per unit type) to limit a unit's daily value. For example, cap daily purchases at 100 on an e-commerce site to exclude reseller behavior.
* CUPED: enable CUPED and specify the lookback window for pre-experiment data inputs.
* Thresholding: convert this metric into a 1/0 unit count metric that flags whether the unit's total sum meets or exceeds a given threshold.
* [Cohort Windows](https://docs.statsig.com/statsig-warehouse-native/features/cohort-metrics): specify a window for data collection after a unit's exposure. For example, a 0-1 day cohort window counts only actions from days 0 and 1 after exposure. Select **Only include units with a completed window** to exclude units from pulse analysis until their cohort window has completed.
* [Baked Metrics](https://docs.statsig.com/statsig-warehouse-native/features/cohort-metrics#metric-bake-windows): specify how long a metric needs to mature. Metric maturation is common for chargebacks or cancellations. Statsig delays loading the data until the window has elapsed, and calculates pulse results only after a unit's metric has matured.