  It becomes a significant challenge to predict customer behavior and retain an
existing customer with the rapid growth of digitization which opens up more
opportunities for customers to choose from subscription-based products and
services model. Since the cost of acquiring a new customer is five-times higher
than retaining an existing customer, henceforth, there is a need to address the
customer churn problem which is a major threat across the Industries.
Considering direct impact on revenues, companies identify the factors that
increases the customer churn rate. Here, key objective of the paper is to
develop a unique Customer churn prediction model which can help to predict
potential customers who are most likely to churn and such early warnings can
help to take corrective measures to retain them. Here, we evaluated and
analyzed the performance of various tree-based machine learning approaches and
algorithms and identified the Extreme Gradient Boosting XGBOOST Classifier as
the most optimal solution to Customer churn problem. To deal with such
real-world problems, Paper emphasize the Model interpretability which is an
important metric to help customers to understand how Churn Prediction Model is
making predictions. In order to improve Model explainability and transparency,
paper proposed a novel approach to calculate Shapley values for possible
combination of features to explain which features are the most
important/relevant features for a model to become highly interpretable,
transparent and explainable to potential customers.
