Deep Learning for Customer Churn Prediction in E-Commerce Decision Support
Keywords:Churn prediction, deep learning, machine learning, e-commerce, decision support
Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.
E-COMERCE Homepage. https://media.pl.cushmanwakefield.com.pl/pr/444970/deweloperzy-magazynowi-i-operatorzy-logistyczni-sa-zgodni-e-commerce-r. Accessed 2021 January 04.
Bhattacharya CB. When Customers Are Members: Customer Retention in Paid Membership Contexts. Journal of the Academy of Marketing Science. 1998 01 01;26(1):31-44. https://doi.org/10.1177/0092070398261004
Kotler P, Keller KL. Marketing Management. 14th Edition. Pearson; 2012.
Gallo A. The Value of Keeping the Right Customers. Harvard Business Review (https://hbr.org/2014/10/the-value-of-keeping-the-right-customers). 2014;
Lu J. Predicting Customer Churn in the Telecommunications Industry –– An Application of Survival Analysis Modeling Using SAS . SUGI 27., Paper 114-27.
Chen D, Sain SL, Guo K. Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management. 2012 08 27;19(3):197-208. https://doi.org/10.1057/dbm.2012.17
Li X, Li Z. A Hybrid Prediction Model for E-Commerce Customer Churn Based on Logistic Regression and Extreme Gradient Boosting Algorithm. Ingénierie des systèmes d information. 2019 Nov 26;24(5):525-530. https://doi.org/10.18280/isi.240510
Gordini N, Veglio V. 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. 2017 04;62:100-107. https://doi.org/10.1016/j.indmarman.2016.08.003
Oskarsdottir M, Bravo C, Verbeke W, Sarraute C, Baesens B, Vanthienen J. A comparative study of social network classifiers for predicting churn in the telecommunication industry. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2016 08. https://doi.org/10.1109/asonam.2016.7752384
Amin A, Anwar S, Adnan A, Khan MA, Iqbal Z. Classification of cyber attacks based on rough set theory. 2015 First International Conference on Anti-Cybercrime (ICACC). 2015 First International Conference on Anti-Cybercrime (ICACC). 2015 Nov. https://doi.org/10.1109/anti-cybercrime.2015.7351952
Ekinci Y, Uray N, Ülengin F. A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing. 2014 04 08;48(3/4):761-784. https://doi.org/10.1108/ejm-12-2011-0714
Ngai E, Xiu L, Chau D. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications. 2009 03;36(2):2592-2602. https://doi.org/10.1016/j.eswa.2008.02.021
Amin A, Al-Obeidat F, Shah B, Adnan A, Loo J, Anwar S. Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research. 2019 01;94:290-301. https://doi.org/10.1016/j.jbusres.2018.03.003
Gordini N. Market-Driven Management: A Critical Literature Review. Symphonya. Emerging Issues in Management. 2010 Dec 01;(2). https://doi.org/10.4468/2010.2.08gordini
Çelik Ö, Usame OO. Comparing to techniques used in customer churn analysis. Journal of Multidisciplinary Developments. 2019;4(1).
Burez J, Van den Poel D. CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications. 2007 02;32(2):277-288. https://doi.org/10.1016/j.eswa.2005.11.037
Gordini N, Veglio V. Using neural networks for customer churn predictionmodeling: preliminary findings from the italian electricity industry. Proceedings de X° Convegno Annuale della Società Italiana Marketing: “Smart Life. Dall'Innovazione Tecnologica al Mercato", Università degli Studi di Milano-Bicocca, Italy. 2013, 1–13.
Gordini N, Veglio V. Customer relationship management and data mining: A classification decision tree to predict customer purchasing behavior in global market. In: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications. Vol. 1-2. 2013:1–40.
Verbeke W, Martens D, Mues C, Baesens B. Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications. 2011 03;38(3):2354-2364. https://doi.org/10.1016/j.eswa.2010.08.023
Verbeke W, Dejaeger K, Martens D, Hur J, Baesens B. New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research. 2012 04;218(1):211-229. https://doi.org/10.1016/j.ejor.2011.09.031
De Caigny A, Coussement K, De Bock KW. A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research. 2018 09;269(2):760-772. https://doi.org/10.1016/j.ejor.2018.02.009
Deng Z, Lu Y, Wei KK, Zhang J. Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. International Journal of Information Management. 2010 08;30(4):289-300. https://doi.org/10.1016/j.ijinfomgt.2009.10.001
Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH. Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models. Journal of Marketing Research. 2006 05;43(2):204-211. https://doi.org/10.1509/jmkr.43.2.204
Xu S, Lai S, Qiu M. Privacy preserving churn prediction. Proceedings of the 2009 ACM symposium on Applied Computing - SAC '09. the 2009 ACM symposium. 2009. https://doi.org/10.1145/1529282.1529643
Sharma A, Kumar Panigrahi P. A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services. International Journal of Computer Applications. 2011 08 31;27(11):26-31. https://doi.org/10.5120/3344-4605
Buckinx W, Van den Poel D. Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research. 2005 07;164(1):252-268. https://doi.org/10.1016/j.ejor.2003.12.010
Coussement K, Van den Poel D. Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications. 2008 01;34(1):313-327. https://doi.org/10.1016/j.eswa.2006.09.038
Xie Y, Li X, Ngai E, Ying W. Customer churn prediction using improved balanced random forests. Expert Systems with Applications. 2009 04;36(3):5445-5449. https://doi.org/10.1016/j.eswa.2008.06.121
Ahmed AA, Maheswari D. Churn prediction on huge telecom data using hybrid firefly based classification. Egyptian Informatics Journal. 2017 Nov;18(3):215-220. https://doi.org/10.1016/j.eij.2017.02.002
Abbasimehr H, Setak M, Tarokh MJ. A Neuro-Fuzzy Classifier for Customer Churn Prediction. International Journal of Computer Applications. 2011 Apr;19(8):35-41.
Yu X, Guo S, Guo J, Huang X. An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications. 2011 03;38(3):1425-1430. https://doi.org/10.1016/j.eswa.2010.07.049
Huang Y, Kechadi T. An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications. 2013 Oct;40(14):5635-5647. https://doi.org/10.1016/j.eswa.2013.04.020
Pełka M, Rybicka A. Identification of factors that can cause mobile phone customer churn with application of symbolic interval-valued logistic regression and conjoint analysis. The 13th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena. 2019, 187–195.
Tamaddoni Jahromi A, Stakhovych S, Ewing M. Managing B2B customer churn, retention and profitability. Industrial Marketing Management. 2014 Oct;43(7):1258-1268. https://doi.org/10.1016/j.indmarman.2014.06.016
Owczarczuk M. Churn models for prepaid customers in the cellular telecommunication industry using large data marts. Expert Systems with Applications. 2010 06;37(6):4710-4712. https://doi.org/10.1016/j.eswa.2009.11.083
Hur Y, Lim S. Customer Churning Prediction Using Support Vector Machines in Online Auto Insurance Service. In: Wang J, Liao XF, Yi Z, eds. Advances in Neural Networks – ISNN 2005. 3497. Berlin, Heidelberg: Springer; 2005. https://doi.org/https://doi.org/10.1007/11427445_149
Miguéis V, Van den Poel D, Camanho A, Falcão e Cunha J. Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Systems with Applications. 2012 09;39(12):11250-11256. https://doi.org/10.1016/j.eswa.2012.03.073
Farquad M, Ravi V, Raju SB. Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing. 2014 06;19:31-40. https://doi.org/10.1016/j.asoc.2014.01.031
Slof D, Frasincar F, Matsiiako V. A competing risks model based on latent Dirichlet Allocation for predicting churn reasons. Decision Support Systems. 2021 07;146:113541. https://doi.org/10.1016/j.dss.2021.113541
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