Telecommunication Systems

, Volume 62, Issue 4, pp 711–734 | Cite as

Modelling revenue generation in a dynamically priced mobile telephony service

  • Han Wang
  • Damien Fay
  • Kenneth N. Brown
  • Liam Kilmartin


Dynamic pricing has been used extensively in specific markets for many years but recent years have seen an interest in the utilization of this approach for the deployment of novel and attractive tariff structures for mobile communication services. This paper describes the development and operation of an agent based model (ABM) for subscriber behavior in a dynamically priced mobile telephony network. The design of the ABM was based on an analysis of real call detail records recorded in a Uganda mobile telephony network in which dynamic pricing was deployed. The ABM includes components which simulate subscriber calling behavior, mobility within the network and social linkages. Using this model, this paper reports on an investigation of a number of alternative strategies for the dynamic pricing algorithm which indicate that the network operator will likely experience revenue losses ranging from a 5 %, when the pricing algorithm is based on offering high value subscriber cohort enhanced random discounts compared to a lower value subscriber cohort, to 30 %, when the priding algorithm results in the discount on offer in a cell being inversely proportional to the contemporary cell load. Additionally, the model appears to suggest that the use of optimization algorithms to control the level of discount offered in cells would likely result in discount simply converging to a “no-discount” scenario. Finally, commentary is offered on additional factors which need to be considered when interpreting the results of this work such as the impact of subscriber churn on the size of the subscriber base and the technical and marketing challenges of deploying the various dynamic pricing algorithms which have been investigated.


Agent-based model Revenue optimization Dynamic pricing Mobile network services 



This research is funded under the Enterprise Partnership Program of the Irish Research Council (IRC) with co-funding from Tango Telecom Limited. The authors would also like to thank the World Bank for providing them with access to data and images used in this research.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Electrical & Electronic Engineering, College of Engineering and InformaticsNational University of Ireland Galway, University RoadGalwayIreland
  2. 2.School of ComputingBournemouth UniversityDorsetUK
  3. 3.Insight Centre for Data Analytics, Department of Computer ScienceUniversity College CorkCorkIreland

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