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Forward-Looking Behavior in Mobile Data Consumption and Targeted Promotion Design: A Dynamic Structural Model

Author

Listed:
  • Lizhen Xu

    (Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308;)

  • Jason A. Duan

    (McCombs School of Business, University of Texas at Austin, Austin, Texas 78712;)

  • Yu Jeffrey Hu

    (Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308;)

  • Yuan Cheng

    (School of Economics and Management, Tsinghua University, 100084 Beijing, China)

  • Yan Zhu

    (School of Economics and Management, Tsinghua University, 100084 Beijing, China)

Abstract

This paper examines the dynamic consumption behavior of individual mobile data users by employing a unique data set on individual-level daily usage over multiple months. Whether and which individual mobile data users are forward looking by dynamically balancing present and future usage and how to design profitable promotions targeting these users are questions of both academic and managerial interest. By developing a dynamic structural model and formally proving its theoretical properties, we discover distinct temporal usage patterns that can distinguish forward-looking users from myopic ones. An empirical test is constructed to test for individual forward-looking behavior by matching the observed usage patterns with the theoretical results. We find a considerable proportion of users (about 40%) are indeed forward looking and also find empirical evidence of individual consumer myopia. Our approach enables us to apply the dynamic model only to those exhibiting forward-looking behavior. It hence serves as a feasible option to control for consumer myopia in estimating dynamic structural models given the inherent limitation that individual discount factors are generally unidentifiable. Our structural model is shown to accurately capture the dynamic trends observed in the actual usage data. It enables sophisticated counterfactual simulations incorporating various factors (e.g., consumer anticipation, plan switch) to deliver rich implications for targeted promotion design. As we find, promotions targeting only forward-looking consumers could be significantly more profitable than blanket promotions uniformly applied to all. Properly designed end-of-month promotions targeting forward-looking users could help mobile carriers fully utilize the otherwise excess network bandwidth and increase revenue at little extra cost. The online appendix is available at https://doi.org/10.1287/isre.2018.0820 .

Suggested Citation

  • Lizhen Xu & Jason A. Duan & Yu Jeffrey Hu & Yuan Cheng & Yan Zhu, 2019. "Forward-Looking Behavior in Mobile Data Consumption and Targeted Promotion Design: A Dynamic Structural Model," Information Systems Research, INFORMS, vol. 30(2), pages 616-635, June.
  • Handle: RePEc:inm:orisre:v:30:y:2019:i:2:p:616-635
    DOI: 10.1287/isre.2018.0820
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    as
    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Doug J. Chung & Thomas Steenburgh & K. Sudhir, 2014. "Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans," Marketing Science, INFORMS, vol. 33(2), pages 165-187, March.
    3. Hoch, Stephen J & Loewenstein, George F, 1991. "Time-Inconsistent Preferences and Consumer Self-Control," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 492-507, March.
    4. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, September.
    5. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
    6. Meghan R. Busse & Christopher R. Knittel & Florian Zettelmeyer, 2013. "Are Consumers Myopic? Evidence from New and Used Car Purchases," American Economic Review, American Economic Association, vol. 103(1), pages 220-256, February.
    7. Sanjog Misra & Harikesh Nair, 2011. "A structural model of sales-force compensation dynamics: Estimation and field implementation," Quantitative Marketing and Economics (QME), Springer, vol. 9(3), pages 211-257, September.
    8. Thierry Magnac & David Thesmar, 2002. "Identifying Dynamic Discrete Decision Processes," Econometrica, Econometric Society, vol. 70(2), pages 801-816, March.
    9. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 497-529.
    10. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    11. Youngsoo Kim & Rahul Telang & William B. Vogt & Ramayya Krishnan, 2010. "An Empirical Analysis of Mobile Voice Service and SMS: A Structural Model," Management Science, INFORMS, vol. 56(2), pages 234-252, February.
    12. Xavier Gabaix & David Laibson, 2018. "Shrouded attributes, consumer myopia and information suppression in competitive markets," Chapters, in: Victor J. Tremblay & Elizabeth Schroeder & Carol Horton Tremblay (ed.), Handbook of Behavioral Industrial Organization, chapter 3, pages 40-74, Edward Elgar Publishing.
    13. Peter Arcidiacono & Robert A. Miller, 2011. "Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 79(6), pages 1823-1867, November.
    14. Judith Chevalier & Austan Goolsbee, 2009. "Are Durable Goods Consumers Forward-Looking? Evidence from College Textbooks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1853-1884.
    15. Anindya Ghose & Sang Pil Han, 2011. "An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet," Management Science, INFORMS, vol. 57(9), pages 1671-1691, September.
    16. Marius F. Niculescu & Seungjin Whang, 2012. "Research Note ---Codiffusion of Wireless Voice and Data Services: An Empirical Analysis of the Japanese Mobile Telecommunications Market," Information Systems Research, INFORMS, vol. 23(1), pages 260-279, March.
    17. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    18. Shane Frederick & George Loewenstein & Ted O'Donoghue, 2002. "Time Discounting and Time Preference: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 40(2), pages 351-401, June.
    19. Manski, Charles F., 1993. "Dynamic choice in social settings : Learning from the experiences of others," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 121-136, July.
    20. Pashardes, Panos, 1986. "Myopic and Forward Looking Behavior in a Dynamic Demand System," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 27(2), pages 387-397, June.
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