A European bank with account holders in France, Spain, and Germany has had many customers churn recently and would like to decrease this churn rate.
Goals
Understand what is driving customers and how this can be predicted in the data gathered on the customers before they churn.
Create a predictive model to find customers who might churn so that special offers could be made to them to entice them to stay.
Task
Train a decision tree binary classifier to create a predictive model of customer churn.
Insights
The decision tree achieved the metrics of Accuracy 80.1%, sensitivity 83.1%, and specificity 68.6%.
According to the decision tree, the top two variables that predicted churn were customer age and number of products with the bank.
Indeed, 48% of the customers in the 45-54 age bracket had churned in this dataset. Also, 82% of the customers that had 3 products were in the churning group.
Recommendations
The decision tree approach shows promise for prediction of churning customers.
Customers that are predicted to churn could be targeted with offers to entice them to stay.
Technical Procedures
Imbalanced classes in the training data were a problem (80%/20% split between non-churners and churners)
To combat the problems caused by imbalanced classes, the minority class (churners) were oversampled with a SMOTE algorithm.
The decision tree was trained using using the tidymodels packages in R.