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International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.11, No.6, Jun. 2019

Combined Appetency and Upselling Prediction Scheme in Telecommunication Sector Using Support Vector Machines

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Author(s)

Lian-Ying Zhou, Daniel M. Amoh, Louis K. Boateng, Andrews A. Okine

Index Terms

Customer Relations Management;Telecommunication;Churn prediction; Appetency prediction;Up-selling prediction;Support Vector Machines; classification

Abstract

Customer Relations Management (CRM) is an essential marketing approach which telecommunication companies use to interact with current and prospective customers. In recent years, researchers and practitioners have investigated customer churn prediction (CCP) as a CRM approach to differentiate churn from non-churn customers. CCP helps businesses to design better retention measures to retain and attract customers. However, a review of the telecommunication sector revealed little to no research works on appetency (i.e. customers likely to purchase new product) and up-selling (i.e. customers likely to buy upgrades) customers. In this paper, a novel up-selling and appetency prediction scheme is presented based on support vector machine (SVM) algorithm using linear and polynomial kernel functions. This study also investigated how using different sample sizes (i.e. training to test sets) impacted the classification performance. Our findings demonstrated that the polynomial kernel function obtained the highest accuracy and the least minimum error in the first three sample sizes (i.e. 80:20, 77:23, 75:25) %. The proposed model is effective in predicting appetency and up-sell customers from a publicly available dataset.   

Cite This Paper

Lian-Ying Zhou, Daniel M. Amoh, Louis K. Boateng, Andrews A. Okine, "Combined Appetency and Upselling Prediction Scheme in Telecommunication Sector Using Support Vector Machines", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.6, pp. 1-7, 2019.DOI: 10.5815/ijmecs.2019.06.01

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