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    A neural networks approach to predict call center calls of an internet service provider
    (IOS Press, 2022) Dağ, Özge Hüsniye Namlı; Yanık, Seda; Nouri, Faranak; Şengör, Neslihan Serap; Koyuncu, Yusuf Mertkan; Uçar, Ömer Berk
    n 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.
  • [ X ]
    Öğe
    A neural networks approach to predict call center calls of an internet service provider
    (IOS Press BV, 2022) Namli, Özge H.; Yanik, Seda; Nouri, Faranak; Serap Şengör, N.; Koyuncu, Yusuf Mertkan; Uçar, Ömer Berk
    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. © 2022 - IOS Press. All rights reserved.
  • Küçük Resim
    Öğe
    Improving customer experience for an internet service provider: a neural networks approach
    (Springer, 2021) Dağ, Özge Hüsniye Namlı; Yanık, Seda; Nouri, Faranak; Şengör, N. Serap; Koyuncu, Yusuf Mertkan; Küçükali, İrem
    Today one of the challenges of companies is to decrease call center costs while improving the customer experience. In this study, we make prediction and proactively take action in order to solve customer problems before they reach the customer call center. We use machine learning techniques and train models with a dataset of an internet service provider’s several different systems. We first use supervised techniques to classify the customers having slow internet connection problems and normal internet connection. We apply two classification approaches, multi perceptron neural networks and radial basis neural networks. Then, we cluster the same dataset using unsupervised techniques, namely Kohonnen’s neural networks and Adaptive Resonance Theory neural networks. We evaluate the classification and clustering results using measures such as recall, accuracy and Davies-Bouldin index, respectively. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.