AI BriefingStandardizing User Activation: How Karrot Built a Shared Data Layer
Key point
Karrot built an organization-wide shared data layer to ensure consistency and reliability in user activation metrics.
Details
Active User metrics such as DAU, WAU, and MAU only show the phenomenon, but have limits in explaining the causes of change. To solve this, a User Activation framework is needed that analyzes users' states and the transitions between states.
User Activation classifies users into Activity States (New, Retained, Reactivated, Inactive, etc.) and analyzes State Transitions, which show how users move between these states over time. This makes it possible to concretely grasp the paths of user acquisition, retention, and churn, beyond simple numerical changes.
Previously, each team calculated activation metrics individually, causing a lack of data consistency and low operational reliability. To solve this, Karrot decided to build an Activation Layer that applies unified, organization-wide standards.
This layer aims to increase data reusability and reliability by providing per-user information such as Activity State, State Transition, Continuity, Return Interval, and Inactivity Period.
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