Customer Churn Analysis
Identifying high-risk customers and uncovering behavioral patterns to reduce churn across global markets.
Problem
High customer churn was eroding revenue, but the business lacked visibility into which customers were at risk and why — making retention efforts broad and ineffective.
Solution
We built a behavioral segmentation model and an interactive Tableau dashboard that classified customers by churn risk tier and surfaced the patterns driving attrition.
Outcome
Delivered a segmentation model and interactive dashboard surfacing high-risk customers by behavior tier, enabling targeted retention campaigns across global markets.
Type
Data Analytics
Role
Data Analyst
Timeline
2025
Problem
The business had no systematic way to identify which customers were likely to churn. Retention efforts were reactive and undifferentiated — targeting everyone equally, which wasted resources and missed the customers most likely to leave.
Process
Coming soon.
Takeaways
Coming soon.
Parts of this work are under NDA.
Reach out — let's chat about the details.