Case Study (Anonymized)
Retention Improvement: Increasing from 16% to 37%
Challenge
The operator had strong acquisition volume, but too many players dropped off after the first sessions. Retention was tracked at a high level, but the team lacked clarity on which cohorts were churning, why they were leaving, and what operational levers had the fastest impact.
Results
Improved retention from 16% to 37%
Clear identification of the biggest retention leaks and the cohorts driving them
Faster iteration cycles based on segment performance and behavior-driven insight
What Kavya did
Book DemoImplemented a Retention Dashboard with cohort views and consistent retention definitions
Deployed Retention Intelligence to highlight churn concentration by GEO, channel, product, and segment
Used Segment Studio to create and monitor behavioral cohorts (high-intent vs low-intent, bonus-sensitive, payment-friction groups, etc.)
Leveraged Player 360 to analyze player-level behavior shifts and identify early churn signals
Enabled Kavya Alerts for retention drops and “cohort health” changes, plus Kavya Brief for daily executive focus
Key takeaway
Retention improves fastest when you stop treating players as averages. With cohort and player-level intelligence, teams can identify the real drivers of churn and build a repeatable retention improvement loop.