Customer Churn Prediction Model using Explainable Machine Learning

• URL: http://arxiv.org/abs/2303.00960v1
• Date: Thu, 2 Mar 2023 04:45:57 GMT
• Title: Customer Churn Prediction Model using Explainable Machine Learning
• Authors: Jitendra Maan, Harsh Maan
• Abstract summary: 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. We evaluated and analyzed the performance of various tree-based machine learning approaches and algorithms. In order to improve Model explainability and transparency, paper proposed a novel approach to calculate Shapley values for possible combination of features.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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