IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v62y2016i4d10.1007_s11235-015-0106-6.html
   My bibliography  Save this article

Modelling revenue generation in a dynamically priced mobile telephony service

Author

Listed:
  • Han Wang

    (National University of Ireland Galway, University Road)

  • Damien Fay

    (Bournemouth University)

  • Kenneth N. Brown

    (University College Cork)

  • Liam Kilmartin

    (National University of Ireland Galway, University Road)

Abstract

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.

Suggested Citation

  • Han Wang & Damien Fay & Kenneth N. Brown & Liam Kilmartin, 2016. "Modelling revenue generation in a dynamically priced mobile telephony service," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 62(4), pages 711-734, August.
    • Handle: RePEc:spr:telsys:v:62:y:2016:i:4:d:10.1007_s11235-015-0106-6
    DOI: 10.1007/s11235-015-0106-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-015-0106-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-015-0106-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    2. Zhang, Tao & Zhang, David, 2007. "Agent-based simulation of consumer purchase decision-making and the decoy effect," Journal of Business Research, Elsevier, vol. 60(8), pages 912-922, August.
    3. Yuri Levin & Jeff McGill & Mikhail Nediak, 2007. "Price Guarantees in Dynamic Pricing and Revenue Management," Operations Research, INFORMS, vol. 55(1), pages 75-97, February.
    4. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    5. Ahn, Jae-Hyeon & Han, Sang-Pil & Lee, Yung-Seop, 0. "Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry," Telecommunications Policy, Elsevier, vol. 30(10-11), pages 552-568, November.
    6. Anastasios Noulas & Salvatore Scellato & Renaud Lambiotte & Massimiliano Pontil & Cecilia Mascolo, 2012. "A Tale of Many Cities: Universal Patterns in Human Urban Mobility," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-10, May.
    7. Faruqui, Ahmad & George, Stephen, 2005. "Quantifying Customer Response to Dynamic Pricing," The Electricity Journal, Elsevier, vol. 18(4), pages 53-63, May.
    8. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    9. Hidalgo, Cesar A. & Rodriguez-Sickert, C., 2008. "The dynamics of a mobile phone network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 3017-3024.
    10. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    11. Ioana Popescu & Yaozhong Wu, 2007. "Dynamic Pricing Strategies with Reference Effects," Operations Research, INFORMS, vol. 55(3), pages 413-429, June.
    12. Han Wang & Liam Kilmartin, 2014. "Comparing Rural and Urban Social and Economic Behavior in Uganda: Insights from Mobile Voice Service Usage," Journal of Urban Technology, Taylor & Francis Journals, vol. 21(2), pages 61-89, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    2. Maxime Lenormand & Miguel Picornell & Oliva G Cantú-Ros & Antònia Tugores & Thomas Louail & Ricardo Herranz & Marc Barthelemy & Enrique Frías-Martínez & José J Ramasco, 2014. "Cross-Checking Different Sources of Mobility Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    3. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.
    4. Chaogui Kang & Yu Liu & Diansheng Guo & Kun Qin, 2015. "A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-11, November.
    5. Li, Feng & Du, Timon C. & Wei, Ying, 2020. "Enhancing supply chain decisions with consumers’ behavioral factors: An illustration of decoy effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    6. Tingliang Huang & Zhe Yin & Ying-Ju Chen, 2017. "Managing Posterior Price Matching: The Role of Customer Boundedly Rational Expectations," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 385-402, July.
    7. Robinson, Scott A. & Rai, Varun, 2015. "Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach," Applied Energy, Elsevier, vol. 151(C), pages 273-284.
    8. Saberi, Meead & Ghamami, Mehrnaz & Gu, Yi & Shojaei, Mohammad Hossein (Sam) & Fishman, Elliot, 2018. "Understanding the impacts of a public transit disruption on bicycle sharing mobility patterns: A case of Tube strike in London," Journal of Transport Geography, Elsevier, vol. 66(C), pages 154-166.
    9. Biggar, Darryl R. & Hesamzadeh, Mohammad Reza, 2024. "Optimal retail contracts with contractible uncertainty," Energy Economics, Elsevier, vol. 136(C).
    10. Zhenhua Chen & Laurie Schintler, 2015. "Sensitivity of location-sharing services data: evidence from American travel pattern," Transportation, Springer, vol. 42(4), pages 669-682, July.
    11. Gerardo Iñiguez & Carlos Pineda & Carlos Gershenson & Albert-László Barabási, 2022. "Dynamics of ranking," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    12. Luca Fraccascia & Ilaria Giannoccaro & Vito Albino, 2017. "Efficacy of Landfill Tax and Subsidy Policies for the Emergence of Industrial Symbiosis Networks: An Agent-Based Simulation Study," Sustainability, MDPI, vol. 9(4), pages 1-18, March.
    13. Takanori Ida & Wenjie Wang, 2014. "A Field Experiment on Dynamic Electricity Pricing in Los Alamos:Opt-in Versus Opt-out," Discussion papers e-14-010, Graduate School of Economics Project Center, Kyoto University.
    14. Maxime Lenormand & Antònia Tugores & Pere Colet & José J Ramasco, 2014. "Tweets on the Road," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    15. Raja Jurdak, 2013. "The Impact of Cost and Network Topology on Urban Mobility: A Study of Public Bicycle Usage in 2 U.S. Cities," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-6, November.
    16. Parrish, Bryony & Heptonstall, Phil & Gross, Rob & Sovacool, Benjamin K., 2020. "A systematic review of motivations, enablers and barriers for consumer engagement with residential demand response," Energy Policy, Elsevier, vol. 138(C).
    17. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    18. Heshmati, Almas, 2012. "Survey of Models on Demand, Customer Base-Line and Demand Response and Their Relationships in the Power Market," IZA Discussion Papers 6637, Institute of Labor Economics (IZA).
    19. Nikolaos Iliopoulos & Motoharu Onuki & Miguel Esteban, 2021. "Shedding Light on the Factors That Influence Residential Demand Response in Japan," Energies, MDPI, vol. 14(10), pages 1-23, May.
    20. Almas Heshmati, 2014. "Demand, Customer Base-Line And Demand Response In The Electricity Market: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 28(5), pages 862-888, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:telsys:v:62:y:2016:i:4:d:10.1007_s11235-015-0106-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.