Core Feature Adoption: Percentage of users using the core features that define the product.
Formula
Core Feature Adoption = Users using all core features / Total active users × 100Computable 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.core_features_adopted = true) / count_distinct(field.user_id where field.product_event_type = 'qualifying')Unit: percent (the formula yields an unscaled ratio)
Notes: Core set is 3 to 5 features defined per product. core_features_used is true when a user used all of them.
Data requirements
Grain: Per event, aggregate monthly · Frequency: Daily · Aggregation: aggregate (month) · Staged: no · Main source: Product Analytics.
| Field | Dataset | Type |
|---|---|---|
core_features_adopted | user | boolean |
event_date | product_event | date |
product_event_type | product_event | string |
snapshot_date | user | date |
user_id | product_event | string |
user_id | user | string |
Example SQL (Postgres)
SELECT (CAST((SELECT COUNT(DISTINCT user_id) FROM user WHERE snapshot_date = :period_end AND core_features_adopted = TRUE) 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 core_feature_adoption From an agent: with the GASP MCP server, call
lookup_metric({ "name": "Core Feature Adoption" }) or generate_query({ "metric": "Core Feature Adoption", "adapter": "<your adapter JSON>" }).
Benchmarks
- Below 30%: Users not fully utilizing product
- 30-50%: Moderate adoption
- 50-70%: Good
- Above 70%: Strong full-product adoption