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Case Study
Agentic BI Tool
10–15% projected retention lift · SQL · Python · Power BI
Problem
Churn signals were buried in raw usage data. Retention teams lacked proactive alerts and a unified view of at-risk users.
Approach
- Built churn scoring model across 10K+ users
- Power BI dashboard with predictive retention layer
- Agentic alerts surfacing at-risk cohorts to stakeholders
Outcome
Insights informed retention strategies projected to improve retention by 10–15%, shifting reporting from reactive to proactive.