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Use Cases

This metric type is an analogue for DAU, and is useful for growth accounting and for measuring upkeep of usage and retention.

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Calculation

At the unit level, unit count metrics create a 1/0 flag for if they participated on a given day. At the group level, the mean is calculated as the SUM of the unit-level flags divided by each unit’s days in th experiment, divided by the count of UNIQUE UNITS exposed to the experiment. This would look like the SQL below: 
-- Unit Level
SELECT distinct
  source_data.unit_id,
  exposure_data.group_id
  COUNT(distinct source_data.date) 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,
  unit_id,
  date_diff(first_exposure_ds, today()) + 1 AS days_exposed
FROM exposure_data
GROUP BY group_id;

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

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Options

  • Metric Breakdowns
    • You can configure Metadata Columns to group results by, getting easy access to dimensional views in pulse results
  • Multi Source
    • Sums can be built with inputs from multiple metric sources; think of this as a UNION in SQL. This can be useful if you have the same measure in sharded or disparate tables
  • Rollup Mode
    • Rollup Mode controls the specific way that Unit Count metrics are aggregated
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