Product Adoption Metrics Operational

Feature Depth

Feature Depth: How intensively users engage with features: average feature actions per user who used a feature in the period.

Formula

Feature Depth = Average feature actions per user per period

Computable definition

The canonical formula in the GASP formula grammar. The MCP server compiles it to SQL over the datasets listed below.

sum(field.usage_count) / count_distinct(field.user_id where field.usage_count > 0)

Unit: ratio

Notes: Average feature actions per user per period.

Data requirements

Grain: Per event, aggregate monthly · Frequency: Daily · Aggregation: aggregate (month) · Staged: no · Main source: Product Analytics.

Field	Dataset	Type
`event_date`	product_event	date
`usage_count`	product_event	integer
`user_id`	product_event	string

Example SQL (Postgres)

SELECT (CAST((SELECT COALESCE(SUM(usage_count), 0) FROM product_event WHERE event_date >= :period_start AND event_date <= :period_end) AS DOUBLE PRECISION) / NULLIF((SELECT COUNT(DISTINCT user_id) FROM product_event WHERE event_date >= :period_start AND event_date <= :period_end AND usage_count > 0), 0)) AS feature_depth

From an agent: with the GASP MCP server, call lookup_metric({ "name": "Feature Depth" }) or generate_query({ "metric": "Feature Depth", "adapter": "<your adapter JSON>" }).

What It Tells You

How heavily people use the features they have adopted, which breadth measures miss.

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