daangn Why did Karrot make (opens in new tab)
Daangn transitioned from manually calculating user activation metrics to a centralized "Activation Layer" built on DBT to solve inconsistencies and high operational overhead. By standardizing the definitions of user states and transitions, the team provides a reliable foundation for analyzing why active user counts fluctuate rather than just reporting the final numbers. This common data layer improves data reliability and cost-efficiency while allowing various teams to reuse the same logic for different core user behaviors. ### The Role of User Activation Analysis * While Active User counts show "what" happened, User Activation explains "why" by breaking users down into specific categories. * The system tracks **Activation States**, classifying users as New, Retained, Reactivated, or Inactive at any given time. * It monitors **State Transitions** to identify how users move between categories, such as "New to Retained" or "Reactivated to Inactive." * The layer provides granular behavioral metadata, including continuous activity streaks, the interval between visits, and the duration of churned periods. ### Ensuring Reliability via Fact Models * Raw event logs are often tied to specific UI elements and contain "noise" that makes them unreliable for direct activation analysis. * To ensure consistency, the Activation Layer uses **Fact Models** as its primary input, which are refined datasets where business logic and core behaviors are already defined. * A strict naming convention (`fact_name_activation_time_grain`) is enforced so that users can immediately identify which specific behavior is being analyzed. * This structure ensures that "Active" status is interpreted identically across the entire organization, regardless of which team is performing the analysis. ### Incremental Processing for Cost Efficiency * Calculating the entire history of user activity every day is computationally expensive and leads to high cloud infrastructure costs. * The architecture utilizes a **FirstLast model** to store only the essential metadata for each user: the date of their very first activity and their most recent activity. * By joining daily activity logs with this lightweight FirstLast table, the system can calculate new states and transitions incrementally. * This approach maintains data idempotency and ensures high performance even as the volume of user interaction data grows. ### Scaling with DBT Macros * To support various metrics—such as app visits, item sales, or community posts—the team encapsulated the complex transition logic into **DBT Macros**. * This abstraction allows data engineers to generate a new activation model by simply specifying the source Fact model and the desired time grain (daily, weekly, or monthly). * Centralizing the logic in macros ensures that any bug fixes or improvements to the activation calculation are automatically reflected across all related data models. * The standardized output format allows for the creation of universal dashboards and analysis templates that work for any tracked behavior. Centralizing User Activation logic into a common data layer allows organizations to move beyond surface-level vanity metrics and gain deep, actionable behavioral insights. By combining DBT’s macro capabilities with incremental modeling, teams can maintain high data quality and operational efficiency even as the variety of tracked user behaviors expands.
