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Effect of Social Media Interactions on CLV in Telecommunications

Author

Listed:
  • Oğuzhan Kivrak

    (#x2020;Bandırma Vocational School, Bandirma Onyedi Eylul University, Bandırma – Balıkesir, Turkey)

  • Cüneyt Akar

    (#x2021;FEAS - Bandirma Onyedi Eylul University, Bandırma – Balıkesir, Turkey)

Abstract

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

Suggested Citation

  • Oğuzhan Kivrak & Cüneyt Akar, 2020. "Effect of Social Media Interactions on CLV in Telecommunications," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 447-468, March.
    • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:02:n:s0219622020500030
    DOI: 10.1142/S0219622020500030
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. Kumar, V., 2010. "A Customer Lifetime Value-Based Approach to Marketing in the Multichannel, Multimedia Retailing Environment," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 71-85.
    3. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    4. Teck-Hua Ho & Young-Hoon Park & Yong-Pin Zhou, 2006. "Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value," Marketing Science, INFORMS, vol. 25(3), pages 260-277, 05-06.
    5. Brent A. Gloy & Jay T. Akridge & Paul V. Preckel, 1997. "Customer Lifetime Value: An application in the rural petroleum market," Agribusiness, John Wiley & Sons, Ltd., vol. 13(3), pages 335-347.
    6. Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
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