Affiliations: [a] Department of Industrial Engineering, Faculty of Engineering, Turkish-German University, Beykoz, Istanbul, Turkey
| [b] Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Macka, Istanbul, Turkey
| [c] Turkcell İletişim Hizmetleri AŞ, Maltepe, Istanbul, Turkey
Correspondence:
[*]
Corresponding author. Seda Yanık, Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Macka, Istanbul, Turkey. E-mail: [email protected].
Note: [1] This work is supported by The Scientific and Technological Research Council of Turkey (TUBITAK-TEYDEB) [Project Nr.: 5190002].
Abstract: In today’s competitive business environment, companies are striving to reduce costs and workload of call centers while improving customer satisfaction. In this study, a framework is presented that predicts and encourages taking proactive actions to solve customer problems before they lead to a call to the call center. Machine learning techniques are implemented and models are trained with a dataset which is collected from an internet service provider’s systems in order to detect internet connection problems of the customers proactively. Firstly, two classification techniques which are multi perceptron neural networks and radial basis neural networks are applied as supervised techniques to classify whether the internet connection of customers is problematic or not. Then, by using unsupervised techniques, namely Kohonnen’s neural networks and Adaptive Resonance Theory neural networks, the same data set is clustered and the clusters are used for the customer problem prediction. The methods are then integrated with an ensemble technique bagging. Each method is implemented with bagging in order to obtain improvement on the estimation error and variation of the accuracy. Finally, the results of the methods applied for classification and clustering with and without bagging are evaluated with performance measures such as recall, accuracy and Davies-Bouldin index, respectively.
Keywords: Call center problem prediction, classification, clustering, artificial
neural networks, bagging
