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Abstract

For highly competitive markets such as mobile carriers, one of the main concerns is to preserve and expand their customer base. Developing marketing strategies to expand total number of customers is a top priority as it is their main source of revenue. It can also act as a representative of the brand, attracting new customers via their social networks and preventing peers from switching operators. Mobile carriers may choose to either bring new users by increasing attractiveness of their brand, or reduce switching customers by improving service quality, and identifying and managing potential leaving clients. These two commonly used strategies to managing a customer base are also combinable. However this chapter will only be focusing on the latter.

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References

  1. More than One-Quarter of Wireless Subscribers Switched to Their Current Carrier to Gain Better Network Coverage. (January 16, 2007). Retrieved from https://www.comscore.com/Insights/Press-Releases/2007/01/Wireless-Subscribers-Switch-Carriers.

  2. Richter, Yossi, Elad Yom-Tov, and Noam Slonim (2010), “Predicting customer churn in mobile networks through analysis of social groups.” SDM 732–741.

    Google Scholar 

  3. Christakis, Nicholas A, James H Fowler. 2007. The spread of obesity in a large social network over 32 years. New England journal of medicine 357(4) 370–379.

    Article  Google Scholar 

  4. Dasgupta, Koustuv, Rahul Singh, Balaji Viswanathan, et al. (2008), “Social ties and their relevance to churn in mobile telecom networks.” EDBT’08: Proceedings of the 11th International Conference on Extending Database Technology, 668–677.

    Google Scholar 

  5. Aral, Sinan, and Dylan Walker (2014), “Tie strength, embeddedness, and social influence: A large-scale networked experiment.” Management Science 60, no. 6: 1352–1370.

    Article  Google Scholar 

  6. Van den Bulte, Christophe, and Gary L. Lilien (2001), “Medical innovation revisited: Social contagion versus marketing effort.” American Journal of Sociology 106, no. 5: 1409–1435.

    Article  Google Scholar 

  7. Van den Bulte, Christophe, and Stefan Stremersch (2004), “Social contagion and income heterogeneity in new product diffusion: A meta-analytic test.” Marketing Science 23, no. 4: 530–544.

    Article  Google Scholar 

  8. Van den Bulte, Christophe, and Yogesh V. Joshi (2007), “New product diffusion with influentials and imitators.” Marketing Science 26, no. 3: 400–421.

    Article  Google Scholar 

  9. Manchanda, Puneet, Ying Xie, and Nara Youn (2008), “The role of targeted communication and contagion in product adoption.” Marketing Science, 27, no. 6: 961–976.

    Article  Google Scholar 

  10. Iyengar, Raghuram, Christophe Van den Bulte, and Thomas W. Valente (2011), “Opinion leadership and social contagion in new product diffusion.” Marketing Science 30, no. 2: 195–212.

    Article  Google Scholar 

  11. Iyengar, Raghuram, Christophe Van den Bulte, and Jae Young Lee (2015), “Social contagion in new product trial and repeat." Marketing Science 34, no. 3: 408–429.

    Article  Google Scholar 

  12. Easley, David, and Jon Kleinberg (2010), Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge, U.K.: Cambridge University Press.

    Book  Google Scholar 

  13. Moretti, Enrico (2011), “Social learning and peer effects in consumption: Evidence from movie sales.” The Review of Economic Studies 78, no. 1: 356–393.

    Article  Google Scholar 

  14. Chandrasekhar, Arun, Horacio Larreguy, and Juan Pablo Xandri (2012), “Testing models of social learning on networks: Evidence from a framed field experiment.” Work. Pap., Mass. Inst. Technol., Cambridge, MA.

    Google Scholar 

  15. Zhang, Juanjuan (2010), “The sound of silence: Observational learning in the US kidney market.” Marketing Science 29, no. 2: 315–335.

    Article  Google Scholar 

  16. Cai, Hongbin, Yuyu Chen, and Hanming Fang (2009), “Observational learning: Evidence from a randomized natural field experiment." American Economic Review 99, no. 3: 864–882.

    Article  Google Scholar 

  17. Katz, Michael, and Carl Shapiro (1985), “Network externalities, competition and compatibility,” The American Economic Review, 75, no. 3: 424–440.

    Google Scholar 

  18. Ching, Andrew T. (2010), “Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration.” International Journal of Industrial Organization 28, no. 6: 619–638.

    Article  Google Scholar 

  19. Narayan, Vishal, Vithala R. Rao, and Carolyne Saunders (2011), “How peer influence affects attribute preferences: a Bayesian updating mechanism.” Marketing Science 30, no. 2: 368–384.

    Article  Google Scholar 

  20. Chan, Tat, Chakravarthi Narasimhan, and Ying Xie (2013), “Treatment effectiveness and side effects: A model of physician learning.” Management Science 59, no. 6: 1309–1325.

    Article  Google Scholar 

  21. Chintagunta, Pradeep K., Renna Jiang, and Ginger Z. Jin (2009), “Information, learning, and drug diffusion: The case of Cox-2 inhibitors.” QME 7, no. 4: 399–443.

    Google Scholar 

  22. Zhao, Yi, Sha Yang, Vishal Narayan, and Ying Zhao (2013), “Modeling consumer learning from online product reviews.” Marketing Science 32, no. 1: 153–169.

    Article  Google Scholar 

  23. Jackson, Matthew O (2008), Social and economic networks. Vol. 3. Princeton, N.J.: Princeton University Press.

    MATH  Google Scholar 

  24. Mobius, Markus, and Tanya Rosenblat (2014), “Social learning in economics.” Annual Reviews of Economics 6, no. 1: 827–847.

    Article  Google Scholar 

  25. Goldenberg, Jacob, Barak Libai, and Eitan Muller (2010), “The chilling effects of network externalities.” International Journal of Research in Marketing 27, no. 1: 4–15.

    Article  Google Scholar 

  26. Tucker, Catherine (2008), “Identifying formal and informal influence in technology adoption with network externalities.” Management Science 54, no. 12: 2024–2038.

    Article  Google Scholar 

  27. Ryan, Stephen P., and Catherine Tucker (2012), “Heterogeneity and the dynamics of technology adoption.” Quantitative Marketing and Economics 10, no. 1: 63–109.

    Article  Google Scholar 

  28. Goolsbee, Austan, and Peter Klenow (2002), “Evidence on learning and network externalities in the diffusion of home computers,” Journal of Law and Economics, 45, no. 2: 317–343.

    Article  Google Scholar 

  29. Iyengar, Raghuram, Asim Ansari, and Sunil Gupta (2007), “A model of consumer learning for service quality and usage,” Journal of Marketing Research, 44, no. 4, 529–544.

    Article  Google Scholar 

  30. Eagle, Nathan, Alex Sandy Pentland, David Lazer. 2009. Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106(36) 15274–15278.

    Article  Google Scholar 

  31. Chen, Xinlei, Yuxin Chen, Ping Xiao. 2013. The impact of sampling and network topology on the estimation of social intercorrelations. Journal of Marketing Research 50(1) 95–110.

    Article  Google Scholar 

  32. Blondel, Vincent D, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008(10) P10008.

    Article  Google Scholar 

  33. Godinho de Matos, Miguel, Pedro Ferreira, David Krackhardt. 2014. Peer influence in the diffusion of the iphone 3g over a large social network. Management Information Systems Quarterly (Forthcoming).

    Google Scholar 

  34. Newman, Mark EJ, Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical review E 69(2) 026113.

    Article  Google Scholar 

  35. Erdem, T. and M. P. Keane (1996), “Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets.” Marketing Science 15:1–20.

    Article  Google Scholar 

  36. Lemon, Katherine, Tiffany White, and Russell Winer (2002), “Dynamic customer relationship management: Incorporating future considerations into the service retention decision,” Journal of Marketing, 66(1): 1–14.

    Article  Google Scholar 

  37. Pakes, Ariel, Michael Ostrovsky, and Steven Berry (2007), “Simple estimators for the parameters of discrete dynamic games (with entry/exit examples),” RAND Journal of Economics, 38, no. 2: 373–399.

    Article  Google Scholar 

  38. Crawford, G. S., and M. Shum (2005), “Uncertainty and learning in pharmaceutical demand.” Econometrica 73:1137–1173.

    Article  MathSciNet  Google Scholar 

  39. Dunne, Timothy, Shawn D. Klimek, Mark J. Roberts, and Daniel Yi Xu (2013), “Entry, exit, and the determinants of market structure.” The RAND Journal of Economics 44, no. 3: 462–487.

    Article  Google Scholar 

  40. DeGroot, Morris H (2005), Optimal statistical decisions. Vol. 82. John Wiley & Sons.

    Google Scholar 

  41. Bajari, Patrick, Lanier Benkard, and Jonathan Levin (2007), “Estimating dynamic models of imperfect competition.” Econometrica 75, 1331–1370.

    Article  MathSciNet  Google Scholar 

  42. Aguirregabiria, Victor, and Pedro Mira (2007), “Sequential estimation of dynamic discrete games.” Econometrica 78(2) 1–53.

    Article  MathSciNet  Google Scholar 

  43. Keane, Michael P., and Kenneth I. Wolpin (1997), “The career decisions of young men.” Journal of Political Economy, 105, no. 3: 473–522.

    Article  Google Scholar 

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Ouyang, Y., Hu, M., Huet, A., Li, Z. (2018). Contagious Churn. In: Mining Over Air: Wireless Communication Networks Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-92312-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-92312-3_10

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