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Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) pp 181–192Cite as

A Computational Intelligence based Approach to Telecom Customer Classification for Value Added Services

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 201)

Abstract

Customer classification is an imperative task for any organization catering to different market segments. In telecom industry it becomes even more important to identify which value added services (VAS) would be successful with a given customer segment. VAS provide a flexible revenue model that can be customized to different customer segments based on several attributes such as usage and preferences. Selecting and customizing VAS provides a wide canvas to the operators for maximizing their returns on the customer portfolio. Computational intelligence techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been successfully used for data mining and machine learning. These techniques provide a mathematical framework for identifying customers profiles and patterns in large datasets that representing the customers’ data and their preferences. In this paper, we propose a methodology using SVM and ANN techniques to classify telecom customer data and identify the VAS best suited for the customer segment. We test our results with the SVM yielding high prediction accuracy for the unknown public test data with Radial Basis Function (RBF) Kernel using grid search technique.

Keywords

  • Customer data classification
  • Support vector machine
  • Artificial neural network
  • Value added services

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  • DOI: 10.1007/978-81-322-1038-2_16
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Authors and Affiliations

  1. Bharti School of Telecommunication Technology and Management, Indian Institute of Technology Delhi, Delhi, India

    Abhay Bhadani

  2. Department of Management Studies, Indian Institute of Technology Delhi, Delhi, India

    Ravi Shankar

  3. Institute for Systems Studies and Analyses, Defence Research and Development Organization (DRDO), Metcalfe House, Delhi, India

    D. Vijay Rao

Authors
  1. Abhay Bhadani
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  2. Ravi Shankar
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  3. D. Vijay Rao
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Corresponding author

Correspondence to Abhay Bhadani .

Editor information

Editors and Affiliations

  1. South Asian University, Chankya Puri, New Delhi, 110021, India

    Jagdish Chand Bansal

  2. ABV - IIITM, Gwalior, Gwalior, 474015, Madhya Pradesh, India

    Pramod Kumar Singh

  3. , Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Kusum Deep

  4. , Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Roorkee, India

    Millie Pant

  5. , Department of Computer Science, Liverpool Hope University, Office: FML 412, Liverpool, Liverpool, L16 9JD, United Kingdom

    Atulya K. Nagar

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Bhadani, A., Shankar, R., Vijay Rao, D. (2013). A Computational Intelligence based Approach to Telecom Customer Classification for Value Added Services. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_16

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  • DOI: https://doi.org/10.1007/978-81-322-1038-2_16

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