Machine-Learning Techniques for Customer Retention: A Comparative Study

Abstract

Nowadays, customers have become more interested in the quality of service (QoS) that organizations can provide them. Services provided by different vendors are not highly distinguished which increases competition between organizations to maintain and increase their QoS. Customer Relationship Management systems are used to enable organizations to acquire new customers, establish a continuous relationship with them and increase customer retention for more profitability. CRM systems use machine-learning models to analyze customers' personal and behavioral data to give organization a competitive advantage by increasing customer retention rate. Those models can predict customers who are expected to churn and reasons of churn. Predictions are used to design targeted marketing plans and service offers. This paper tries to compare and analyze the performance of different machine-learning techniques that are used for churn prediction problem. Ten analytical techniques that belong to different categories of learning are chosen for this study. The chosen techniques include Discriminant Analysis, Decision Trees (CART), instance-based learning (k-nearest neighbors), Support Vector Machines, Logistic Regression, ensemble-based learning techniques (Random Forest, Ada Boosting trees and Stochastic Gradient Boosting), Naïve Bayesian, and Multi-layer perceptron. Models were applied on a dataset of telecommunication that contains 3333 records. Results show that both random forest and ADA boost outperform all other techniques with almost the same accuracy 96%. Both Multi-layer perceptron and Support vector machine can be recommended as well with 94% accuracy. Decision tree achieved 90%, naïve Bayesian 88% and finally logistic regression and Linear Discriminant Analysis (LDA) with accuracy 86.7%.

Machine-Learning Based Prediction of Generator Runtime: A predictive maintenance solution for a telecommunications site

“…The study found that decision trees achieved the highest accuracy, followed by naïve Bayesian, logistic regression, and linear discriminant analysis. These findings provide insights into the suitability of different machine learning techniques for predictive maintenance in the telecommunications industry [9].…”

Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector

“…According to Sabbeh [22], machine learning techniques were used to overcome customer churn challenges in the banking sector. The authors in [23] described a churn prediction model in the banking sector using Classification and Regression Trees (CART) and C5.0, and the results showed that the prediction success rate of the churn class by CART was higher than that of C 5.0.…”

Implementation of Machine Learning Algorithms for Customer Churn Prediction

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