Reduced Customer churn rate using Churn Analytics for a logistics vendor

Increasing customer churn rates

Understand factors leading to churn

Predict potential customers who are likely to churn

Roll out retention measures to keep customers with them and increase count of loyal customers

Gained understanding of churn objectives from the client’s perspective

Identified and extracted relevant attributes

Performed Exploratory data analysis to evaluate effect of data fields leading to customer churn

Built churn prediction model using Classification and Regression Tree (CART)

The predictive churn model built attained 91% accuracy

Identified critical areas that sought more focus to reduce churn rate

Predicted customers who were about to defect

Rolled out proactive measures to reduce customer churn

Converted low-revenue customers into profitable ones

Tableau

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