Asemic makes it easy to use daily tracking metrics, like sessions per day, in cohort analysis to observe behavior over time. For example, you can track how the number of sessions changes for users right after registration, a week later, or even a month later, providing valuable insights into user behavior at different stages of their journey.
Asemic’s Semantic Layer allows for simple, yet powerful, definitions of lifetime User Properties. You can easily track cumulative metrics like total revenue per user, the total number of active days in the app, or other long-term behaviors. These lifetime properties can then be seamlessly incorporated into cohort analysis, enabling advanced metrics that are difficult or even impossible to construct in other product analytics tools.
The true power of cohort analysis lies in understa nding how user behavior evolves. Asemic’s segmentation and KPI tools help you identify patterns that may not be immediately visible through standard day-to-day tracking. Are users highly engaged in their first week and then taper off? Do they return consistently after long periods of inactivity? Asemic makes it easy to uncover these patterns and respond proactively.
Deeper analysis of cohort behavior can offer valuable insights to both product and marketing teams. Product teams can use cohort analysis to identify which features drive long-term engagement and which moments are critical for retaining users. Marketing teams, on the other hand, can track how different campaigns impact various cohorts over time—seeing which messages resonate with new users versus existing users. By understanding how behavior shifts across user segments, both teams can refine their strategies to better meet user needs and drive growth.
