Who leaves? What signals do they leave behind?
Telecom data to understand who cancels and why.
Pandas · Cohorts · Tableau
What changes.
Objective
Reduce churn in telecom by identifying critical variables and prioritizing retention and operational efficiency initiatives.
Results
Identified monthly contracts as the main driver of churn.
How it was built.Decisions, evidence and details
Objective
Reduce churn in telecom by identifying critical variables and prioritizing retention and operational efficiency initiatives.
Methodology
- Data cleaning and preparation: standardizing contracts, payments, and services; deriving contract duration.
- EDA and cohorts: comparing active vs. churned customers by contract type and payment method.
- Metrics: churn rate, average monthly charges, tenure, and service segmentation.
- Visualization and insights: comparative charts and correlations for cancellation drivers.
Key visualizations
- Churn by contract type (monthly vs. annual) and payment method.
- Retention curves and monthly churn rates.
- Comparison of monthly charges between churned and active customers.
Results
- Identified monthly contracts as the main driver of churn.
- Recommended shifting clients toward annual plans and proactive onboarding.
- Cohorts ready to track retention after implemented actions.