Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression

TL;DR

This paper introduces a mixed-penalty logistic regression method for customer churn prediction, effectively handling big data and feature heterogeneity to improve classification accuracy and robustness.

Contribution

It proposes a novel penalized logistic regression model with mixed penalties that prevents overfitting and balances regularization, enhancing churn prediction in CRM data.

Findings

Improves classification accuracy, precision, and recall.

Effectively reduces the impact of irrelevant features.

Handles class imbalance and feature heterogeneity efficiently.

Abstract

Using big data to analyze consumer behavior can provide effective decision-making tools for preventing customer attrition (churn) in customer relationship management (CRM). Focusing on a CRM dataset with several different categories of factors that impact customer heterogeneity (i.e., usage of self-care service channels, duration of service, and responsiveness to marketing actions), we provide new predictive analytics of customer churn rate based on a machine learning method that enhances the classification of logistic regression by adding a mixed penalty term. The proposed penalized logistic regression can prevent overfitting when dealing with big data and minimize the loss function when balancing the cost from the median (absolute value) and mean (squared value) regularization. We show the analytical properties of the proposed method and its computational advantage in this research. In addition, we investigate the performance of the proposed method with a CRM data set (that has a large number of features) under different settings by efficiently eliminating the disturbance of (1) least important features and (2) sensitivity from the minority (churn) class. Our empirical results confirm the expected performance of the proposed method in full compliance with the common classification criteria (i.e., accuracy, precision, and recall) for evaluating machine learning methods.