Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry
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ORIGINAL ARTICLE
Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry Ruholla Jafari-Marandi1 • Joshua Denton2 • Adnan Idris3 • Brian K. Smith4 • Abbas Keramati5,6 Received: 11 March 2019 / Accepted: 14 March 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Knowledge-based churn prediction and decision making is invaluable for telecom companies due to highly competitive markets. The comprehensiveness and action ability of a data-driven churn prediction system depend on the effective extraction of hidden patterns from the data. Generally, data analytics is employed to extrapolate the extracted patterns from the training dataset to the test set. In this study, one more step is taken; the improved prediction performance is attained by capturing the individuality of each customer while discovering the hidden pattern from the train set and then applying all the knowledge to the test set. The proposed churn prediction system is developed using artificial neural networks that take advantage of both self-organizing and error-driven learning approaches (ChP-SOEDNN). We are introducing a new dimension to the study of churn prediction in telecom industry: a systematic and profit-driven churn decision-making framework. The comparison of the ChP-SOEDNN with other techniques shows its superiority regarding both accuracy and misclassification cost. Misclassification cost is a realistic criterion this article introduces to measure the success of a method in finding the best set of decisions that leads to the minimum possible loss of profit. Moreover, ChP-SOEDNN shows capability in devising a cost-efficient retention strategy for each cluster of customers, in addition to strength in dealing with the typical issue of imbalanced class distribution that is common in churn prediction problems. Keywords Artificial neural networks (ANNs) Profit-driven churn prediction Self-organizing map (SOM) Self-organizing error-driven ANN (SOEDANN)
1 Introduction Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00521-020-04850-6) contains supplementary material, which is available to authorized users. & Ruholla Jafari-Marandi [email protected] 1
Industrial and Manufacturing Engineering Department, Cal Poly, San Luis Obispo, CA 93407, USA
2
Department of Marketing, Quantitative Analysis, and Business Law, Mississippi State University, Starkville, MS 39759, USA
3
Department of Computer Sciences and IT, University of Poonch, Rawalakot, Pakistan
4
Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS 39759, USA
5
Ted Rogers School of Information Technology Management, Ryerson University, Toronto, ON, Canada
6
School of Industrial and Systems Engineering, University of Tehran, Tehran, Iran
Subscription-based firms prefer retaining customers to acquiring new ones due to the relatively higher cost of customer acquisition [18]. Te
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