Customer Journey Analytics & Personalization Engine
Turning fragmented data into actionable retention insights across onboarding, usage, and support touchpoints.
Role: Cross-functional lead across Product, Data, and Client Success
Team: Data engineers, analytics, CS ops, product managers
Goal: Build a 360° journey analytics engine and surface friction points to improve activation, retention, and CLV
+18% cross-sell uplift across core segments
+12% improvement in retention
Time-to-insight reduced from 10 days to 1 day
Context
Customer engagement and renewal rates were stagnating. Data across onboarding, product usage, and support was siloed—blocking unified attribution, churn risk visibility, and customer lifetime value tracking.
Approach
- Designed journey schema linking usage, support, and CRM data (dbt models; AWS Glue jobs).
- Implemented event tracking & standardized IDs for cross-system joins and cohorting.
- Defined Activation %, FCR, NPS correlation, and churn probability as north-star metrics.
- Built funnel analytics and cohort views in Power BI with Python-based data prep.
- Ran A/B tests for tutorials, nudges, and UI prompts to improve activation & retention.
- Exposed churn risk and growth levers to execs via automated dashboards and alerting.
Outcome & Impact
- Achieved 18% uplift in cross-sell and 12% improvement in retention across core segments.
- Reduced time to insight from 10 days to 1 day via automated journey dashboards.
- Provided executives with clear, data-driven views of churn risk and growth levers.
Next Steps / Learning
Integrate an ML-based churn predictor and connect to marketing automation to trigger personalized retention campaigns at the right moment in the journey.