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Forecasting a telecommunications provider's market share

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  • Kanellos, Nikolaos
  • Katsianis, Dimitrios
  • Varoutas, Dimitrios

Abstract

Telecommunications providers' market share risks stem from uncertainties due to overall market performance and competition strategies adopted by providers. In this paper, a framework that allows risk-adjusted forecasting of a provider's market share is presented. Two different stochastic processes are deployed to model the effects of churn and attraction strategies, as well as market performance. Impact results are obtained through the application of Monte Carlo simulation. The proposed framework was verified and validated with the use of a typical test scenario. Application findings are consistent with relative churn and attraction management literature, indicating a best performer advantage and a minimum systematic risk impact on a provider's market share expectations. The proposed framework can help telecommunication providers to understand and adjust their strategies regarding churn management and new customer attraction and can be extended to include market structure analysis and forecasts as well.

Suggested Citation

  • Kanellos, Nikolaos & Katsianis, Dimitrios & Varoutas, Dimitrios, 2022. "Forecasting a telecommunications provider's market share," 31st European Regional ITS Conference, Gothenburg 2022: Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes 265639, International Telecommunications Society (ITS).
  • Handle: RePEc:zbw:itse22:265639
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    References listed on IDEAS

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    1. Gruca, TS & Klemz, BR, 1998. "Using Neural Networks to Identify Competitive Market Structures from Aggregate Market Response Data," Omega, Elsevier, vol. 26(1), pages 49-62, February.
    2. W. Edwards Deming & Gerald J. Glasser, 1968. "A Markovian Analysis of the Life of Newspaper Subscriptions," Management Science, INFORMS, vol. 14(6), pages 283-293, February.
    3. Fok, Dennis & Franses, Philip Hans, 2001. "Forecasting market shares from models for sales," International Journal of Forecasting, Elsevier, vol. 17(1), pages 121-128.
    4. Kumar, V., 1994. "Forecasting performance of market share models: an assessment, additional insights, and guidelines," International Journal of Forecasting, Elsevier, vol. 10(2), pages 295-312, September.
    5. Denisa Maria Melian & Andreea Dumitrache & Stelian Stancu & Alexandra Nastu, 2022. "Customer Churn Prediction in Telecommunication Industry. A Data Analysis Techniques Approach," Postmodern Openings, Editura Lumen, Department of Economics, vol. 13(1Sup1), pages 78-104, March.
    6. Alae Chouiekh & El Hassane Ibn El Haj, 2020. "Deep Convolutional Neural Networks for Customer Churn Prediction Analysis," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 14(1), pages 1-16, January.
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