Abstract
The social influence of people on their peers in the selection of products and services is frequently modeled as a diffusion process. Recently, such processes have been successfully applied as a tool for predicting customer turnover, or churn, in mobile communication carriers. These predictions are most accurate when specific social ties are used in the diffusion process, and are primarily useful when they provide a long forecast horizon, so as to enable a service provider to take mitigating actions. Here, we investigate several measures of social affinity and compare their performances for churn prediction, using data from two large mobile phone carriers. Our analysis demonstrates that the various measures of social ties capture different calling and texting patterns, and that a significant improvement in the accuracy of prediction is reached by combining them. We study the predictive horizon of diffusion processes and show that it deteriorates significantly as the horizon increases. Our findings underline the usefulness of diffusion processes for enhancing churn prediction while providing insights to their limitations.
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References
Adamic LA, Adar E (2001) Friends and neighbors on the web. Soc Netw 25:211–230
Birke D (2006) Diffusion on networks: modelling the spread of innovations and customer churn over social networks. Workshop on Formation of Social Networks in Social Software Applications
Coussement K, Van den Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst Appl 34(1):313–327
Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 160–168
Dasgupta K, Singh R, Viswanathan B, Chakraborty D, Mukherjea S, Nanavati AA, Joshi A (2008) Social ties and their relevance to churn in mobile telecom networks. In: EDBT ’08: Proceedings of the 11th international conference on extending database technology
Datta P, Masand B, Mani DR, Li B (2000) Automated cellular modeling and prediction on a large scale. Artif Intell Rev 14(6):485–502
de Oliveira Lima E (2009) Domain knowledge integration in data mining for churn and customer lifetime value modelling: new approaches and applications. PhD thesis, School of Management, University of Southampton
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Dierkes T, Bichler M, Krishnan R (2011) Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with markov logic networks. Decis Support Syst 51(3):361–371
Doyle S, Barn S (2007) The role of social networks in marketing. J Database Mark Customer Strategy Manag 15(1):60–64
Gopal RK, Meher SK (2008) Customer churn time prediction in mobile telecommunication industry using ordinal regression. In: Lecture Notes in Computer Science, vol 5012, Springer, pp 884–889
Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 13th international conference on World Wide Web, ACM, WWW ’04, pp 491–501
Hadden J, Tiwari A, Roy R, Ruta D (2007) Computer assisted customer churn management: State-of-the-art and future trends. Comput Oper Res 34(10):2902–2917
Huang B, Kechadi MT, Buckley B (2012) Customer churn prediction in telecommunications. Expert Syst Appl 39(1):1414–1425
Idris A, Rizwan M, Khan A (2012) Churn prediction in telecom using random forest and pso based data balancing in combination with various feature selection strategies. Comput Electr Eng 38(6):1808–1819
Iyengar R, Van den Bulte C, Valente TW (2011) Opinion leadership and social contagion in new product diffusion. Mark Sci 30(2):195–212
Jadhav RJ, Pawar UT (2011) Churn prediction in telecommunication using data mining technology. Int J Adv Comput Sci Appl 2(2):17–19
Karnstedt M, Hennessy T, Chan J, Hayes C (2010) Churn in social networks: a discussion boards case study. In: IEEE Second international conference on social computing (SocialCom), 2010, pp 233–240
Kawale J, Pal A, Srivastava J (2009) Churn prediction in mmorpgs: a social influence based approach. In: IEEE international conference on computational science and engineering, vol 4, pp 423–428
Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’03
Kossinets G, Watts DJ (2009) Origins of homophily in an evolving social network1. Am J Sociol 115(2):405–450
Lu N, Lin H, Lu J, Zhang G (2012) A customer churn prediction model in telecom industry using boosting. Industrial Informatics, IEEE Transactions on PP(99):1–1.
Ma H, Yang H, Lyu MR, King I (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM conference on information and knowledge management, ACM, New York, NY, USA, CIKM 2008, pp 233–242
Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH (2006) Defection detection: measuring and understanding the predictive accuracy of customer churn models. J Mark Res 43(2):204–211
Nitzan I, Libai B (2010) Social effects on customer retention. Marketing Science Institute (MSI) Working Paper
Pendharkar PC (2009) Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services. Expert Syst Appl 36(3):6714–6720
Peres R, Mahajan V, Eitan M (2010) Innovation diffusion and new product growth: a critical review and research directions. Int J Res Mark 1:91–106
Richter Y, Yom-Tov E, Slonim N (2010) Predicting customer churn in mobile networks through analysis of social groups. In: SDM, pp 732–741
Sarkar P, Chakrabarti D, Moore A (2010) Theoretical justification of popular link prediction heuristics. In: 23st annual conference on learning theory, COLT 2010
Song G, Yang D, Wu L, Wang T, Tang S (2006) A mixed process neural network and its application to churn prediction in mobile communications. In: Proceedings of the sixth IEEE international conference on data mining workshops (ICDM Workshops), pp 798–802
Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(2):72–101
Toledano H, Yom-Tov E, Pelleg D, Pednault E, Natarajan R (2008) Support vector machine solvers: large-scale, accurate, and fast (pick any two). Tech Rep H-0260, IBM Research
Wang Y, Cong G, Song G, Xie K (2010) Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD 2010, pp 1039–1048
Yang J, He X, Lee H (2007) Social reference group influence on mobile phone purchasing behaviour: a cross-nation comparative study. Int J Mob Commun 5(3):319–338
Acknowledgments
We thank Amit A. Nanavati for clarifying some implementation details of Dasgupta et al. (2008).
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Baras, D., Ronen, A. & Yom-Tov, E. The effect of social affinity and predictive horizon on churn prediction using diffusion modeling. Soc. Netw. Anal. Min. 4, 232 (2014). https://doi.org/10.1007/s13278-014-0232-2
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DOI: https://doi.org/10.1007/s13278-014-0232-2