Artificial Intelligence Based Predictive Analysis of Customer Churn

Vaani Gupta; Aman Jatain

doi:10.55927/fjcis.v2i1.3926

Authors

Vaani Gupta Amity University Gurugram
Aman Jatain Amity University Gurugram

DOI:

https://doi.org/10.55927/fjcis.v2i1.3926

Keywords:

Artificial Intelligence Based Predictive Analysis of Customer Churn, Comparative Graphs, Deep Learning, Heroku, Machine Learning

Abstract

Customer churn, also known as attrition, occurs when subscribers or customers stop doing business with an enterprise or organization by unsubscribing to a service, discontinuing membership or simply stopping payment. Churn is a critical metric because it is more cost-effective to retain existing customers than it is to acquire new ones. Since churning impedes growth, companies usually use a defined method for calculating customer churn in a given period. By monitoring churn rate and the various factors affecting it, organizations determine their customer retention success rates and identify strategies for improvement. The model, “Artificial Intelligence based Predictive Analysis of Customer Churn” is aimed at predicting a customer’s likelihood of discontinuing the usage of the products or services extended by a company, using ML & DL algorithms. It is built for the example of a US based telephone service provider’s services. This model uses machine learning’s capability to identify patterns and build prediction algorithms using provided data, blended with the adoption of complex algorithms of deep neural networks, which form deep learning. Accuracy tests, comparative graphs and illustrations equip this project with higher efficiency and good performance by estimating which is the best model from the suite of models created

Downloads

Download data is not yet available.

References

A.H. Farquad, Vadlamani Ravi, and S. Bapi Raju 2014, Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing 19: 31–40.

He Y, He Z, and Zhang D. 2009, A study on prediction of customer churn in fixed communication network based on data mining. Sixth international conference on fuzzy systems and knowledge discovery, vol. 1: 92–4.

Huang F, Zhu M, Yuan K, and Deng EO. 2015, Telco churn prediction with big data. ACM SIGMOD international conference on management of data: 607–18.

H. Lee, Y. Lee, H. Cho, K. Im, and Y.S. Kim 2011, Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model. Decision Support System 52: 207–216.

Brandusoiu I, Toderean G, and Ha B. 2016, Methods for churn prediction in the prepaid mobile telecommunications industry. International conference on communications: 97–100.

Burez D, and den Poel V. 2019, Handling class imbalance in customer churn prediction. Expert Syst Appl. 36(3): 4626–36.

T. Verbraken, W. Verbeke, and B. Baesens 2013, A novel profit maximizing metric for measuring classification performance of customer churn prediction models. IEEE Transaction on Knowledge and Data Engineering 25: 961–973.

Koen W. De Bock, and Dirk Van den Poel 2012, Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. Expert Systems with Applications 39: 6816–6826.

Idris A, Khan A, and Lee YS. 2012, Genetic programming and adaboosting based churn prediction for telecom. IEEE international conference on systems, man, and cybernetics (SMC): 1328–32.

Makhtar M, Nafis S, Mohamed M, Awang M, Rahman M, and Deris M. 2017, Churn classification model for local telecommunication company based on rough set theory. J Fundam Appl Sci. 9(6): 854–68.

Amin A, Anwar S, Adnan A, Nawaz M, Howard N, Qadir J, Hawalah A, and Hussain A. 2016, Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study. IEEE Access 4: 7940–57.

L. Ning, L. Hua, L. Jie, and Z. Guangquan 2014, A customer churn prediction model in telecom industry using boosting. IEEE Trans. Ind. Inform. 10: 1659–1665.

Ahmad A.K., Jafar A., and Aljoumaa K. 2019, Customer churn prediction in telecom using machine learning in big data platform. J Big Data. 6, 28.

A. Saran and D. Chandrakala. 2016, A Survey on Customer Churn Prediction using Machine Learning Techniques. International Journal of Computer Applications. 154(10): 13-16.

Vadakattu, B. Panda, S. Narayan, and H. Godhia Nov 2015, Enterprise subscription churn prediction. Proc. IEEE Int. Conf. Big Data: 1317-1321.

Ascarza E, Iyengar R, and Schleicher M. 2016, The perils of proactive churn prevention using plan recommendations: evidence from a field experiment. J Market Res. 53(1): 46–60.

Chih-Fong Tsai, and Yu-Hsin Lu 2009, Customer churn prediction by hybrid neural networks. Expert Systems with Applications 36: 12547–12553.