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.

Prediction of customer churn has been one of the most interesting and challenging tasks facing most telecommunication companies recently. For the past decade, researchers together with practitioners have been working and designing models that can correctly make more accurate customer churn predictions (CCP). However, most of these models have less accuracy than expected which is hugely affecting the intended purpose. Consequently, most of these CCP models add little or nothing to the revenue growth of telecommunication industries. This work aims at improving the reliability of CCP in the telecommunication sector. To achieve this objective, a new attribute to be factored in CCP, known as the regional churn rate (RCR), is presented. Basically, RCR gives an idea about the rate of churning in a particular locality or region. Thus, a prediction model with a more accurate CCP using a support vector machine (SVM) classifier is proposed. The performance of the proposed model is critically evaluated using five metrics i.e. misclassification error, precision, recall, specificity and f-measure. At the same time, the performance of the proposed classifier (CCP with RCR) is compared with another SVM classifier which doesn’t consider RCR (CCP without RCR). Results show that the proposed model which considers the RCR of a customer’s location gives the highest accuracies for four performance metrics i.e. precision, recall, misclassification error and f-measure. Therefore, the proposed SVM-based CCP model gives a more clear indication as to whether a customer is a potential churner or not.