Market share modelling and forecasting using Markov chains and alternative models

Chan, Ka Ching (2015) Market share modelling and forecasting using Markov chains and alternative models. International Journal of Innovative Computing, Information and Control, 11 (4). pp. 1205-1218. ISSN 1349-4198

Abstract

Markov chain has been a popular approach for market share modelling and forecasting in many industries. This paper presents four mathematical models for the same market share problem based on different underlying assumptions. The four models include a homogeneous Markov model, a time-varying Markov model, a new extended time-varying Markov model, and a novel non-Markov model. A numerical example in the telecommunications industry is included to illustrate that all four models can be used, for market share forecasting. Although Markov models are popular, forecasters should be cautious in choosing Markov or the alternative models for their problems in hand. In order to achieve the best forecasting results, forecasters should, have in-depth understanding of the industries, market conditions, and trends, then make appropriate assumptions, and apply or even develop the most suitable models.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Restricted access to published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 08 May 2018 05:55
Last Modified: 03 Sep 2018 03:48
Uncontrolled Keywords: Markov chain, market share, forecasting, brand switching, competition
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
15 Commerce, Management, Tourism and Services > 1503 Business and Management > 150399 Business and Management not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970115 Expanding Knowledge in Commerce, Management, Tourism and Services
E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/34043

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