K-Medoids Clustering Techniques in Predicting Customers Churn: A Case Study in the E-Commerce Industry

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2022 by IJCTT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Xiancheng Xiahou, Yoshio Harada
  10.14445/22312803/IJCTT-V70I2P104

MLA

MLA Style: 
Xiancheng Xiahou, and Yoshio Harada. "K-Medoids Clustering Techniques in Predicting Customers Churn: A Case Study in the E-Commerce Industry" International Journal of Computer Trends and Technology, vol. 70, no. 2, Feb. 2022, pp.22-28.  Crossref https://doi.org/10.14445/22312803/IJCTT-V70I2P104

APA Style:
Xiancheng Xiahou, & Yoshio Harada (2022). K-Medoids Clustering Techniques in Predicting Customers Churn: A Case Study in the E-Commerce Industry. International Journal of Computer Trends and Technology, 70(2), 22-28. https://doi.org/10.14445/22312803/IJCTT-V70I2P104

Abstract
      As the churn of customers can result in the decline of business performance, preventing customer churn becomes an important task for e-commerce enterprises. This paper studies customer churn by using shopping data sets of e-commerce customers and machine learning technology. Firstly, a k-medoids clustering algorithm is adopted to cluster customers and divide them into three types. Then we adopt Logistic Regression, Support Vector Machines and Adaboost models to predict the churn of these three types of customers. The results show that the k-medoids algorithm can accurately identify churn and non-churn customers and that the Adaboost model boasts good prediction performance. The research results can guide enterprises to enhance customer relationship management and formulate appropriate marketing strategies and provide a critical theoretical basis for the research of customer churn prediction.

Keywords
Customers churn, E-commerce industry, K-medoids clustering algorithm, Machine learning, Predicting technology.

Reference

[1] A.D.Rachid, A.Abdellah, B.Belaid And L.Rachid, Clustering Prediction Techniques In Defining and Predicting Customers Defection: The Case Of E-Commerce Context, International Journal of Electrical and Computer Engineering.8(4) (2018) 2367-2383.
[2] J.Hadden, A.Tiwari, R.Roy and D.Ruta, Computer-Assisted Customer Churn Management: State-of-the-Art and Future Trends, Computers & Operations Research. 34(10) ( 2007) 2902-2917.
[3] R.C.Blattberg, B.D.Kim and S.A. Neslin, Database Marketing: Analyzing and Managing Customers, New York: Springer. (2008) 495-514.
[4] W.Koen, B.De and A.D.Caigny, Spline-Rule Ensemble Classifiers with Structured Sparsity Regularization for Interpretable Customer Churn Modelling, Decision Support Systems.150 (2021)1-14.
[5] A.D.Caigny, K.Coussement And K.W.D. Bock, A New Hybrid Classification Algorithm for Customer Churn Prediction Based on Logistic Regression and Decision Trees, European Journal of Operational Research. 269 (2018) 760–772.
[6] H.Mohammed, T.Ali, E.Tariq And ATM Saeed, Customer Churn In Mobile Markets: A Comparison Of Techniques, International Business Research. 8(6) (2015) 224-237.
[7] A. Q.Ammar, Ahmed and D. Maheswari Churn Prediction on Huge Telecom Data using Hybrid Firefly Based Classification, Egyptian Informatics Journal. 18 (2017) 215–220.
[8] N.Alboukaey, A.Joukhadar And N.Ghneim, Dynamic Behaviour-Based Churn Prediction in Mobile Telecom, Expert Systems with Applications. 162 (2020)1-17.
[9] J.Kozak, K.Kania, P.Juszczuk And M.Mitręga, Swarm Intelligence Goal-Oriented Approach to Data-Driven Innovation in Customer Churn Management, International Journal of Information Management.60 (2021)1-16.
[10] N.N.Y.Vo, S.Liu, X.Li and G.Xu, Leveraging Unstructured Call Log Data for Customer Churn Prediction, Knowledge-Based Systems. 212 (2021) 1-15.
[11] B.Larivière and D.V.D.Poel, Investigating the Role of Product Features in Preventing Customer Churn, By using Survival Analysis and Choice Modelling: the Case of Financial Services. Expert Systems with Applications, 27(2) ( 2004)277-285.
[12] G.Nie, W.Rowe and L.L.Zhang, Credit Card Churn Forecasting by Logistic Regression and Decision Tree, Expert Systems with Applications, 38(12) (2011)15273-15285.
[13] Y. L. Deng And Q.Y.Gao, A Study on E-Commerce Customer Segmentation Management Based on Improved K-Means Algorithm, Information Systems and E-Business Management.18 (2020) 497-510.
[14] Y.F.Qiu and C.Li, Research on E-Commerce user Churn Prediction Based on Logistic Regression, In Proc. Of IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017), (2017) 12-15.
[15] M.O.Pratama, R.Meiyanti,H.Morrison, A.Ramadhan, A.N.Hidayanto, Influencing Factors of Consumer Purchase Intention Based on Social Commerce Paradigm, In Proc. International Conference on Advanced Computer Science and Information Systems (ICACSIS),(2017) 73-79.
[16] M. Zhao, Q. J. Zeng, M. Chang, Q. Tong and J.F.Su, A Prediction Model of Customer Churn Considering Customer Value: An Empirical Research of Telecom Industry in China, Discrete Dynamics in Nature and Society.5 (2021) 1-12.
[17] K. Coussement, S. Lessmann and G.Verstraeten, A Comparative Analysis of Data Preparation Algorithms for Customer Churn Prediction: A Case Study in the Telecommunication Industry, Decision Support Systems. 95 (2017) 27-36.
[18] A.T. Jahromi, S.Stakhovych and M.Ewing, Managing B2B Customer Churn, Retention and Profitability, Industrial Marketing Management. 43(7) ( 2014) 1258-1268.
[19] J.Hadden, A.Tiwari, R.Roy and D.Ruta, Computer-Assisted Customer Churn Management: State-Of-the-Art and Future Trends, Computers & Operations Research.34(10) ( 2007) 2902-2917.
[20] A.Prinzie And D.V.D.Poel, Incorporating Sequential Information Into Traditional Classification Models By using an Element/Position-Sensitive SAM, Decision Support Systems. 42 (2006) 508-526.
[21] T.G. Itschert And U.W.Thonemann, How Training On Multiple Time Slices Improves Performance in Churn Prediction, European Journal of Operational Research. 295(2021) 664-674.
[22] Y.C.Zhen, P.F.Zhi And M.Sun, A Hierarchical Multiple Kernel Support Vector Machine for Customer Churn Prediction using Longitudinal Behavioural Data, European Journal of Operational Research.223(2012) 461-472.
[23] N.Gordini And V.Veglio, Customers, Churn Prediction And Marketing Retention Strategies, An Application Of Support Vector Machines Based on the AUC Parameter-Selection Technique in B2B E-Commerce Industry. Industrial Marketing Management.62 (2016) 100-107.
[24] X.B.Yu, S.S.Guo, J.Guo and X.R.Huang, An Extended Support Vector Machine Forecasting Framework for Customer Churn in E-Commerce. Expert Systems with Applications.38(3) (2011) 1425-1430.
[25] A.T.Jahromi, S.Stakhovych and M.Ewing, Managing B2B Customer Chum, Retention and Profitability, Industrial Marketing Management. 43(7) (2014) 1258-1268.
[26] A.D.Caigny, K.Coussement, W.Verbeke, K.Idbenjra And M.Phan, Uplift Modelling and its Implications for B2B Customer Churn Prediction: A Segmentation-Based Modelling Approach, Industrial Marketing Management.99 (2021) 28-39.
[27] S. E. Schaeffer and S.V.R.Sanchez, Forecasting Client Retention-A Machine-Learning Approach, Journal of Retailing And Consumer Services.52 (2020) 101918.
[28] N.Gordini And V.Veglio, Customers, Churn Prediction And Marketing Retention Strategies. An Application of Support Vector Machines Based on the AUC Parameter-Selection Technique in B2B E-Commerce Industry, Industrial Marketing Management.62 (2017) 100-107.
[29] C. Kristof, L.Stefan and V.Geert, A Comparative Analysis of Data Preparation Algorithms for Customer Churn Prediction: A Case Study on the Telecommunication Industry, Decision Support Systems. 95 (2017) 27-36.
[30] K.W.D. Bock and D.V.D.Poe, An Empirical Evaluation of Rotation Based Ensemble Classifiers for Customer Churn Prediction, Expert Systems with Applications. 38(10) (2011) 12293-12301.
[31] S.Neslin, S.Gupta, WA.Kamakura, L.Liu and C.Mason, Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Model, Journal Of Marketing Research. 43(2) (2006) 204-211.
[32] N.Holtrop, J.E.Wieringa, M. J. Gijsenberg and P. C.Verhoef, No Future Without the Past? Predicting Churn in the Face if Customer Privacy, International Journal if Research in Marketing. 34(1) (2016) 154-172.
[33] E.Lima, C.Mues And B.Baesen, Monitoring and Backtesting Churn Models, Expert Systems with Applications. 38(1) (2011) 975-982.
[34] A.T.Jahromi, S.Stakhovych and M.Ewing, Managing B2B Customer Chum, Retention and Profitability, Industrial Marketing Management. 43(7) (2014) 1258-1268.
[35] A. Amin, S.Anwar, A. Adnan, M.Nawaz, K. Alawfi, A. Hussain and K.Huang, Customer Churn Prediction in the Telecommunication Sector using a Rough Set Approach. Neurocomputing. 237(10) (2017) 242-254.
[36] (2021) Available Tianchi (Online): Https://Tianchi.Aliyun.Com/Datase (Accessed On 17 December 2021).
[37] L.Breiman, Bagging Predictors: Machine Learning.24 (1996) 123-140.
[38] B.A.Goldstein, E. C.Polley And F.B.S.Briggs, Random Forests for Genetic Association Studies, Statistical Applications in Genetics And Molecular Biology.10 (2011) 32.
[39] X . Fan and T.Ke, Enhanced Maximum AUC Linear Classifier, In Proc. if the International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), (2010) 1540-1544.