GASP
Product Adoption Metrics Leading

Feature Adoption Rate

Feature Adoption Rate: Percentage of active users who used a specific feature in the period.

Formula

Feature Adoption = Users using feature / Total active users × 100

Computable definition

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

count_distinct(field.user_id where field.feature_id is not null) / count_distinct(field.user_id where field.product_event_type = 'qualifying')

Unit: percent (the formula yields an unscaled ratio)

Notes: Evaluated per feature_id. Numerator counts users who used the feature in the 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
feature_id product_event string
product_event_type product_event string
user_id product_event string
Example SQL (Postgres)
SELECT (CAST((SELECT COUNT(DISTINCT user_id) FROM product_event WHERE event_date >= :period_start AND event_date <= :period_end AND feature_id IS NOT NULL) AS DOUBLE PRECISION) / NULLIF((SELECT COUNT(DISTINCT user_id) FROM product_event WHERE event_date >= :period_start AND event_date <= :period_end AND product_event_type = 'qualifying'), 0)) AS feature_adoption

Access: recommended sensitivity tier internal, access scope unscoped. Guidance your policy engine enforces.

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

What It Tells You

Which features are being used, which are ignored.

Connected in the GASP relationship graph.

Upstream: what drives this

Downstream: what this drives

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