Machine-Learning Techniques for Customer Retention: A Comparative Study

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), Naive 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%, naive Bayesian 88% and finally logistic regression and Linear Discriminant Analysis (LDA) with accuracy 86.7%.

[1]  Yeon Hur,et al.  Customer Churning Prediction Using Support Vector Machines in Online Auto Insurance Service , 2005, ISNN.

[2]  Dirk Van den Poel,et al.  Predicting customer retention and profitability by using random forests and regression forests techniques , 2005, Expert Syst. Appl..

[3]  Yong Shi,et al.  Prediction of Customer Attrition of Commercial Banks based on SVM Model , 2014, ITQM.

[4]  Xiaojun Wu,et al.  E-commerce customer churn prediction based on improved SMOTE and AdaBoost , 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM).

[5]  Euiho Suh,et al.  An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry , 2004, Expert Syst. Appl..

[6]  Xiu Li,et al.  Churn prediction with Linear Discriminant Boosting algorithm , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[7]  Kaizhu Huang,et al.  Customer churn prediction in the telecommunication sector using a rough set approach , 2017, Neurocomputing.

[8]  Samira Khodabandehlou,et al.  Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior , 2017, J. Syst. Inf. Technol..

[9]  Luo Bin,et al.  Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service , 2007, 2007 International Conference on Service Systems and Service Management.

[10]  Adnan Amin,et al.  Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example , 2014, ENIC.

[11]  Abbas Keramati,et al.  Developing a prediction model for customer churn from electronic banking services using data mining , 2016, Financial Innovation.

[12]  Eric Johnson,et al.  Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry , 2000, IEEE Trans. Neural Networks Learn. Syst..

[13]  Manpreet Kaur,et al.  Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining , 2016 .

[14]  Witold R. Rudnicki,et al.  Boruta - A System for Feature Selection , 2010, Fundam. Informaticae.

[15]  Miguel A. P. M. Lejeune,et al.  Measuring the impact of data mining on churn management , 2001, Internet Res..

[16]  Peter L. Hammer,et al.  A new imputation method for incomplete binary data , 2011, ISAIM.

[17]  Sudipto Guha,et al.  Clustering data streams , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[18]  Catalin Cimpoeru,et al.  Predicting Customers Churn in a Relational Database , 2014 .

[19]  Shaomin Li,et al.  Applying and Evaluating Models to Predict Customer Attrition Using Data Mining Techniques , 2003 .

[20]  Marley M. B. R. Vellasco,et al.  Data Mining Techniques on the Evaluation of Wireless Churn , 2004, ESANN.

[21]  Kristof Coussement,et al.  Improved marketing decision making in a customer churn prediction context using generalized additive models , 2010, Expert Syst. Appl..

[22]  Rahul J. Jadhav,et al.  Churn Prediction in Telecommunication Using Data Mining Technology , 2011 .

[23]  You-Shyang Chen,et al.  Classifying the segmentation of customer value via RFM model and RS theory , 2009, Expert Syst. Appl..

[24]  Stefan Lessmann,et al.  A reference model for customer-centric data mining with support vector machines , 2009, Eur. J. Oper. Res..

[25]  M. Tahar Kechadi,et al.  Customer churn prediction in telecommunications , 2012, Expert Syst. Appl..

[26]  D. W.,et al.  CUSTOMER LIFETIME VALUE: MARKETING MODELS AND APPLICATIONS , 1998 .

[27]  Andrew H. Karp.  USING LOGISTIC REGRESSION TO PREDICT CUSTOMER RETENTION , 1998 .

[28]  Mohammad Mehdi Sepehri,et al.  Applying Data Mining to Customer Churn Prediction in an Internet Service Provider , 2010 .

[29]  Michel Ballings,et al.  Customer event history for churn prediction: How long is long enough? , 2012, Expert Syst. Appl..

[30]  Carlos Soares,et al.  Customer segmentation in a large database of an online customized fashion business , 2015 .

[31]  Yong Shi,et al.  Credit card churn forecasting by logistic regression and decision tree , 2011, Expert Syst. Appl..

[32]  Vicent Giner-Bosch,et al.  Assessing classification methods for churn prediction by composite indicators , 2012 .

[33]  Helen Sebastian,et al.  Churn Analysis in Telecommunication Using Logistic Regression , 2017 .

[34]  Theodore B. Trafalis,et al.  Examining Churn and Loyalty Using Support Vector Machine , 2012 .

[35]  Gavril TODEREAN,et al.  PREDICTING CHURN IN MOBILE TELECOMMUNICATIONS INDUSTRY , 2013 .

[36]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[37]  Asifullah Khan,et al.  Genetic Programming and Adaboosting based churn prediction for Telecom , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[38]  A. Keramati,et al.  Churn analysis for an Iranian mobile operator , 2011 .

[39]  Dirk Van den Poel,et al.  Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models , 2006, Eur. J. Oper. Res..

[40]  Asifullah Khan,et al.  Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies , 2012, Comput. Electr. Eng..

[41]  Li Hong,et al.  Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining , 2013 .

[42]  S. Jafari,et al.  (Case Study: Solico Food Industries Group) , 2013, The 5th Conference on Information and Knowledge Technology.

[43]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[44]  A. Kazemi,et al.  Modelling Customer Attraction Prediction in Customer Relation Management using Decision Tree: A Data Mining Approach , 2011 .

[45]  Hyunbo Cho,et al.  Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model , 2011, Decis. Support Syst..

[46]  ESCORT (Enterprise Services Cross-sell Optimization Using Rigorous Tests of Association) , 2017 .

[47]  J. Orbach.  Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. , 1962 .

[48]  Praveen Asthana.  A comparison of machine learning techniques for customer churn prediction , 2018 .

[49]  Hsin-Hung Wu,et al.  A case study of applying data mining techniques in an outfitter's customer value analysis , 2009, Expert Syst. Appl..

[50]  Sudipto Guha,et al.  Clustering Data Streams , 2000, FOCS.

[51]  Sunil Gupta,et al.  Managing Churn to Maximize Profits , 2020, Mark. Sci..

[52]  M. Pardo,et al.  Classification of electronic nose data with support vector machines , 2005 .

[53]  Swarnim Narayan,et al.  Enterprise Subscription Churn Prediction , 2015 .

[54]  Rahul Kumar,et al.  Churn Prediction Model Using Linear Discriminant Analysis (LDA) , 2016 .

[55]  Rebecca Grant,et al.  Forecasting and the Role of Churn in Software-as-a-Service Business Models , 2013 .

[56]  Kristof Coussement,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin , 2022 .

[57]  Eric W. T. Ngai,et al.  Customer churn prediction using improved balanced random forests , 2009, Expert Syst. Appl..

[58]  Li Xiu,et al.  The Application ofAdaBoost in Customer Churn Prediction , 2007, 2007 International Conference on Service Systems and Service Management.

[59]  Donald E. Brown,et al.  Customer churn analysis for a software-as-a-service company , 2017, 2017 Systems and Information Engineering Design Symposium (SIEDS).

[60]  Abbas Keramati,et al.  Improved churn prediction in telecommunication industry using data mining techniques , 2014, Appl. Soft Comput..

[61]  J. Carpenter,et al.  Practice of Epidemiology Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study , 2014 .

[62]  R. Bakare,et al.  Modelling telecom customer attrition using logistic regression , 2012 .

[64]  Robert E. Schapire,et al.  Explaining AdaBoost , 2013, Empirical Inference.