Abstract
Network operators are merging their services, such as fixed or wireless telephony, internet or television, into single offers, called bundles. It is essential to understand consumers’ preferences to define the most profitable bundles, with their associated prices, especially in the fierce competitive current market. We start by defining a random linear utility model and then, analyze the competition between an integrated operator and new entrants proposing substitutable services. Each operator ignores the consumers’ reservation prices for his offers and has to deal with uncertainties about the marketing strategies of competitors, due to potential different size and cost structure. A two-level game is introduced and solved by backward induction. In the second level, the operators determine their optimal offer prices for each possible combination of marketing strategies while the consumers select their most profitable purchasing processes; the natural framework is that of Bayesian game theory. Finally at the top level, knowing the outcome of the other level, the operators identify which marketing strategy to use between market share expansion, segment targeting or multi-level price discrimination, to maximize their expected utilities conditionally to their private informations.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Akerlof, G. (1970). The market for lemons: Quality uncertainty and the market mechanism. Quaterly Journal of Economics, 83, 488–500.
Audestadt, J.-A., Gaivoronski, A., & Werner, A. (2006). Extending the stochastic programming framework for the modeling of several decision makers: Pricing and competition in the telecommunication sector. Annals of Operation Research, 142, 19–39.
Aydin, G., & Ryan, J. K. (2000). Product line selection and pricing under the multinomial logit choice model. In Proceedings of the 2000 MSOM conference. University of Michigan, Ann. Arbor, MI.
Berry, S., & Pakes, A. (2007). The pure characteristics discrete choice model with application to price indices. International Economic Review, 48, 1193–1228.
Cai, G., & Wurman, P. (2003). Monte-Carlo approximation in incomplete information sequental auction games. Decision Support Systems, 39, 153–168.
Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, learning, and games. Cambridge: Cambridge University Press.
Chung, J., & Rao, R. (2003). A general choice model for bundles with multiple-category product: Application to market segmentation and optimal pricing for bundles. Journal of Marketing Research, 11, 115–130.
Häggström, O. (2002). Finite Markov chains and algorithmic applications. University Press.
Hoffer, G., & Pratt, M. (1987). Used vehicles, lemons markets, and used car rules: Some empirical evidence. Journal of Consumer Policy, 10, 409–414.
Holenstein, R. (2005). Using sampling to compute bayes-nash equilibrium in auction games. Course Project, Departement of Computer Science, University of British Columbia.
Jeididi, K., Jagpal, S., & Manchanda, P. (2003). Measuring heteogeneous reservation prices for product bundles. Marketing Science, 22, 107–130.
Kephart, J. O., Brooks, C. H., Das, R., Mackie-Mason, J. K., Gazzale, R., & Durfee, E. H. (2001). Pricing information bundles in a dynamic environment. ACM Conference on Electronic Commerce.
Khouja, M., & Robbins, S. (2005). Optimal pricing and quantity of products with two offerings. European Journal of Operational Research, 163, 530–544.
Le Cadre, H., Bouhtou, M., & Tuffin, B. (2008). Consumers’ preference modeling to price bundle offers in the telecommunication industry: Study for a single provider. Orange Labs/R&D/CORE/MCN/09-R2O/HL V1.0.
Le Cadre, H., Bouhtou, M., & Tuffin, B. (2009). A Pricing model for a Mobile Network Operator sharing limited resource with a Mobile Virtual Network Operator. In 6-th international workshop on advanced charging and qos technology (ICQT’09). Lecture notes in computer science.
Leleno, J., & Sherali, H. (1992). A leader-follower model and analysis for a two-stage network of oligopolies. Annals of Operations Research, 34, 37–72.
Myerson, R. (2004). Game theory, analysis of conflict (6th ed.). Cambridge: Harvard University Press.
Poirier, D. (1996). A Bayesian analysis of nested logit models. Journal of Econometrics, 75, 163–181.
Robert, C. (1996). Markov chain Monte Carlo methods. Economica, Statistique Mathématique et Probabilités.
Tallury, K., & Van Ryzin, G. (2004). Revenue management under general discrete choice model of consumer behavior. Management Science, 50, 15–33.
Train, K. (2002). Discrete choice methods with simulation. Cambridge: Cambridge University Press.
Villalobos-Arias, M., Coello-Coello, C., & Hernandez-Lerma, O. (2006). Asymptotic convergence of a simulated annealing algorithm for multiobjective optimization problems. Mathematical Methods of Operations Research, 64, 353–363.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Le Cadre, H., Bouhtou, M. & Tuffin, B. Consumers’ preference modeling to price bundle offers in the telecommunications industry: a game with competition among operators. Netnomics 10, 171–208 (2009). https://doi.org/10.1007/s11066-009-9044-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11066-009-9044-3