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Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management
International Journal on Data Science and Technology
Volume 6, Issue 2, June 2020, Pages: 56-59
Received: Jun. 17, 2020; Accepted: Jul. 2, 2020; Published: Aug. 20, 2020
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Ayodeji Ibitoye, Computer Science Department, Bowen University, Iwo, Nigeria
Olufade Onifade, Computer Science Department, Bowen University, Iwo, Nigeria; Computer Science Department, University of Ibadan, Ibadan, Nigeria
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The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.
Churn Prediction, Fuzzy-weight, Ensemble Machine Learning, Customer Segmentation, Customers’ Behavioural Management
To cite this article
Ayodeji Ibitoye, Olufade Onifade, Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management, International Journal on Data Science and Technology. Vol. 6, No. 2, 2020, pp. 56-59. doi: 10.11648/j.ijdst.20200602.12
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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