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Handbook of Social Network Technologies and Applications pp 185–220Cite as

Churn in Social Networks

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Abstract

In the past, churn has been identified as an issue across most industry sectors. In its most general sense it refers to the rate of loss of customers from a company’s customer base. There is a simple reason for the attention churn attracts: churning customers mean a loss of revenue. Emerging from business spaces like telecommunications (telcom) and broadcast providers, where churn is a major issue, it is also regarded as a crucial problem in many other businesses, such as online games creators, but also online social networks and discussion sites. Companies aim at identifying the risk of churn in its early stages, as it is usually much cheaper to retain a customer than to try to win him or her back. If this risk can be accurately predicted, marketing departments can target customers efficiently with tailored incentives to prevent them from leaving.

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Notes

  1. 1.

    http://yro.slashdot.org/story/09/08/01/1946208/IBM-Uses-Call-Detail-Records-To-Identify-Friends.

  2. 2.

    http://www.vodacom.com/reports/ar_2009/pdf/full.pdf, http://www.vodafone.com/annual_report09/downloads/VF_Annual_Report_2009.pdf

  3. 3.

    http://insidemr.blogspot.com/2006/06/comparison-of-churn-rates.html

  4. 4.

    http://boards.ie

References

  1. J.-H. Ahn, S.-P. Han, and Y.-S. Lee. Customer churn analysis: Churn determinants and mediation effects of partial defection in the korean mobile telecommunications service industry. Telecommunications Policy, 30(10–11):552–568, 2006

    Google Scholar 

  2. W.-H. Au, K. C. C. Chan, and X. Yao. A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Trans. Evolutionary Computation, 7(6):532–545, 2003

    Google Scholar 

  3. A. O. B. Briscoe and B. Tilly. Metcalfe’s law is wrong, July 2006

    Google Scholar 

  4. F. M. Bass. A Dynamic Model of Market Share and Sales Behavior. In Winter Conference American Marketing Association, 1963

    Google Scholar 

  5. D. Birke. Diffusion on networks: Modelling the spread of innovations and customer churn over social networks. In GI Jahrestagung (2), pages 480–488, 2006

    Google Scholar 

  6. W. Buckinx and D. V. den Poel. Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1):252–268, 2005

    MATH  Google Scholar 

  7. J. Burez and D. V. den Poel. CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Syst. Appl, 32(2):277–288, 2007

    Google Scholar 

  8. J. Burez and D. V. den Poel. Handling class imbalance in customer churn prediction. Expert Syst. Appl, 36(3):4626–4636, 2009

    Google Scholar 

  9. J. Chan, E. M. Daly, and C. Hayes. Decomposing discussion forums and boards using user roles. In AAAI Conference on Weblogs and Social Media, pages 215–218, 2010

    Google Scholar 

  10. C. Chen, A. Liaw, and L. Breiman. Using Random Forest to Learn Imbalanced Data. Technical report, University of California at Berkley, 2004

    Google Scholar 

  11. D. Constant, L. Sproull, and S. Kiesler. The kindness of strangers: The usefulness of electronic weak ties for technical advice. Organization Science, 7(2):119–135, 1996

    Google Scholar 

  12. K. Coussement and D. V. den Poel. Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Syst. Appl, 34(1):313–327, 2008

    Google Scholar 

  13. K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea, A. A. Nanavati, and A. Joshi. Social ties and their relevance to churn in mobile telecom networks. In EDBT ’08, pages 668–677, 2008

    Google Scholar 

  14. P. Datta, B. M. Masand, D. R. Mani, and B. Li. Automated cellular modeling and prediction on a large scale. Artif. Intell. Rev., 14(6):485–502, 2000

    MATH  Google Scholar 

  15. J. Davis and M. Goadrich. The relationship between Precision-Recall and ROC curves. In ICML ’06, pages 233–240, 2006

    Google Scholar 

  16. D. V. den Poel and B. Larivière. Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157(1):196–217, 2004

    MATH  Google Scholar 

  17. J. Ferreira, M. B. R. Vellasco, M. A. C. Pacheco, and C. R. H. Barbosa. Data mining techniques on the evaluation of wireless churn. In ESANN, pages 483–488, 2004

    Google Scholar 

  18. Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 4:933–969, 2003

    MathSciNet  MATH  Google Scholar 

  19. J. H. Friedman. Stochastic gradient boosting. Comput. Stat. Data Anal., 38(4):367–378, 2002

    MathSciNet  MATH  Google Scholar 

  20. L. Getoor. Link mining: a new data mining challenge. SIGKDD Explor. Newsl., 5(1):84–89, 2003

    Google Scholar 

  21. J. Goldenberg, B. Libai, and E. Muller. Using Complex Systems Analysis to Advance Marketing Theory Development: Modeling Heterogeneity Effects on New Product Growth Through Stochastic Cellular Automata. In Academy of Marketing Science Review, 2001

    Google Scholar 

  22. S. Gopal, R.K. Meher. Customer churn time prediction in mobile telecommunication industry using ordinal regression. Lecture Notes in Computer Science, 884–889(5012):252–268, 2008

    Google Scholar 

  23. A. Goyal, F. Bonchi, and L. V. Lakshmanan. Discovering leaders from community actions. In CIKM ’08, pages 499–508, ACM, NY, 2008

    Google Scholar 

  24. M. Granovetter. The Strength of Weak Ties. American Journal of Sociology, 78(6):1360–1380, 1973

    Google Scholar 

  25. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In WWW ’04, pages 491–501, 2004

    Google Scholar 

  26. A. Gustafsson, M. Johnson, and I. Roos. The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention. Journal of Marketing, 69(4): 210–218, 2005

    Google Scholar 

  27. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR ’99, pages 230–237, ACM, NY, 1999

    Google Scholar 

  28. O. Herrera and T. Znati. Modeling churn in P2P networks. In Annual Simulation Symposium, pages 33–40. IEEE Computer Society, 2007

    Google Scholar 

  29. S. Hill, F. Provost, and C. Volinsky. Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 22:2006, 2006

    MathSciNet  MATH  Google Scholar 

  30. B. Huang, B. Buckley, and T. M. Kechadi. Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications. Expert Syst. Appl., 37(5):3638–3646, 2010

    Google Scholar 

  31. B. Q. Huang, M. T. Kechadi, and B. Buckley. Customer churn prediction for broadband internet services. In DaWaK, volume 5691 of Lecture Notes in Computer Science, pages 229–243, Springer, Berlin, 2009

    Google Scholar 

  32. S.-Y. Hung, D. C. Yen, and H.-Y. Wang. Applying data mining to telecom churn management. Expert Syst. Appl, 31(3):515–524, 2006

    Google Scholar 

  33. H. Hwang, T. Jung, and E. Suh. An ltv model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Syst. Appl., 26(2):181–188, 2004

    Google Scholar 

  34. J. Kawale, A. Pal, and J. Srivastava. Churn Prediction in MMORPGs: A Social Influence Based Approach. In CSE ’09, pages 423–428, 2009

    Google Scholar 

  35. S. M. Keaveney. Customer switching behavior in service industries: An exploratory study. The Journal of Marketing, 59(2):71–82, 1995

    Google Scholar 

  36. W. O. Kermack and A. G. McKendrick. A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772):700–721, 1927

    Google Scholar 

  37. M. Kim, M. Park, and D. Jeong. The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications Policy, 28(2):145–160, 2004

    Google Scholar 

  38. J. M. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604–632, 1999

    MathSciNet  MATH  Google Scholar 

  39. P. Kollock. The Economies of Online Cooperation: Gifts and Public Goods in Cyberspace. Routledge, London, 1999

    Google Scholar 

  40. C. X. Ling and C. Li. Data Mining for Direct Marketing: Problems and Solutions. In KDD, pages 73–79, 1998

    Google Scholar 

  41. J. Lu. Predicting customer churn in the telecommunications industry – an application of survival analysis modeling using sas. In SAS Proceedings, SUGI 27, pages 114–127, 2002

    Google Scholar 

  42. H. Ma, H. Yang, M. R. Lyu, and I. King. Mining social networks using heat diffusion processes for marketing candidates selection. In CIKM ’08, pages 233–242, 2008

    Google Scholar 

  43. J. P. Maicas, Y. Polo, and F. J. Sese. Reducing the level of switching costs in mobile communications: The case of mobile number portability. Telecommunications Policy, 33(9):544 – 554, 2009

    Google Scholar 

  44. B. M. Masand, P. Datta, D. R. Mani, and B. Li. CHAMP: A prototype for automated cellular churn prediction. Data Min. Knowl. Discov, 3(2):219–225, 1999

    Google Scholar 

  45. M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27:415–444, 2001

    Google Scholar 

  46. B. Metcalfe. Metcalfes law: A network becomes more valuable as it reaches more users, 1995

    Google Scholar 

  47. A. Meyer-Bäse and R. Watzel. Transformation radial basis neural network for relevant feature selection. Pattern Recognition Letters, 19(14):1301–1306, 1998

    MATH  Google Scholar 

  48. K. Morik and H. Kpcke. Analysing customer churn in insurance data – a case study. In PKDD ’04, pages 325–336, 2004

    Google Scholar 

  49. M. Mozer, R. Wolniewicz, D. Grimes, E. Johnson, and H. Kaushansky. Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Transactions on Neural Networks, 11(3):690–696, 2000

    Google Scholar 

  50. M. Mozer, R. H. Wolniewicz, D. B. Grimes, E. Johnson, and H. Kaushansky. Churn reduction in the wireless industry. In NIPS, pages 935–941, 1999

    Google Scholar 

  51. T. Mutanen. Customer churn analysis - a case study. Technical report, Helsinki University of Technology, System Analysis Laboratory, 2006

    Google Scholar 

  52. K. Ng and H. Liu. Customer retention via data mining. Artif. Intell. Rev., 14(6):569–590, 2000

    MATH  Google Scholar 

  53. G. Nie, G. Wang, P. Zhang, Y. Tian, and Y. Shi. Finding the hidden pattern of credit card holder’s churn: A case of china. In ICCS ’09, pages 561–569, Springer, Heidelberg, 2009

    Google Scholar 

  54. L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford, 1998

    Google Scholar 

  55. Plc-Vodafone-Group. Vodafone annual report for the year ended 31 March 2009: Principal risk factors and uncertainties, 2009. http://www.vodafone.com/annual_report09/downloads/VF_Annual_Report_2009.pdf

  56. A. Prinzie and D. V. den Poel. Investigating purchasing-sequence patterns for financial services using markov, mtd and mtdg models. European Journal of Operational Research, 170(3):710–734, 2006

    MATH  Google Scholar 

  57. J. Qi, Y. Zhang, and H. Shu. Churn prediction with limited information in fixed-line telecommunication. In Symposium on Communication Systems Networks and Digital Signal Processing, pages 423–426, Springer, Berlin, 2006

    Google Scholar 

  58. H. Rheingold. The Virtual Community: Homesteading on the Electronic Frontier. MIT Press, MA, 2000

    Google Scholar 

  59. B. Ryan and N. C. Gross. The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology, 8(1):15–24, 1943

    Google Scholar 

  60. G. Salton and C. Buckley. On the use of spreading activation methods in automatic information. In SIGIR ’88, pages 147–160, 1988

    Google Scholar 

  61. T. Strufe. Profile Popularity in a Business-oriented Online Social Network. In Social Network Systems, EuroSys, 2010

    Google Scholar 

  62. D. Stutzbach and R. Rejaie. Understanding churn in peer-to-peer networks. In ACM SIGCOMM conference on Internet measurement, page 202. ACM, NY, 2006

    Google Scholar 

  63. Z. Sun, G. Bebis, and R. Miller. Object detection using feature subset selection. Pattern Recognition, 37:2165–2176, 2004

    Google Scholar 

  64. J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In SIGKDD ’09, pages 807–816, 2009

    Google Scholar 

  65. C.-F. Tsai and Y.-H. Lu. Customer churn prediction by hybrid neural networks. Expert Syst. Appl., 36(10):12547–12553, 2009

    Google Scholar 

  66. C.-P. Wei and I.-T. Chiu. Turning telecommunications call details to churn prediction: a data mining approach. Expert Syst. Appl, 23(2):103–112, 2002

    Google Scholar 

  67. G. M. Weiss. Mining with rarity: a unifying framework. SIGKDD Explor. Newsl., 6(1):7–19, 2004

    MathSciNet  Google Scholar 

  68. G. M. Weiss and F. Provost. Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Int. Res., 19(1):315–354, 2003

    MATH  Google Scholar 

  69. Y. Xie, X. Li, E. W. T. Ngai, and W. Ying. Customer churn prediction using improved balanced random forests. Expert Syst. Appl., 36(3):5445–5449, 2009

    Google Scholar 

  70. L. Yan, R. H. Wolniewicz, and R. Dodier. Predicting customer behavior in telecommunications. IEEE Intelligent Systems, 19(2):50–58, 2004

    Google Scholar 

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Acknowledgements

This work was carried out in part in the CLIQUE Strategic Research Cluster, which is funded by Science Foundation Ireland (SFI) under grant number 08/SRC/I1407, and under partial funding of ETRI and DFG FOR 733 (“QuaP2P”).

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  1. Digital Enterprise Research Institute (DERI), NUI Galway, Galway, Ireland

    Marcel Karnstedt

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  2. Tara Hennessy
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  4. Partha Basuchowdhuri
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Correspondence to Marcel Karnstedt .

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  1. Dept. of Comp. & Elect. Engin. and, Florida Atlantic University, Glades Road 777, Boca Raton, 33431, Florida, USA

    Borko Furht

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Karnstedt, M., Hennessy, T., Chan, J., Basuchowdhuri, P., Hayes, C., Strufe, T. (2010). Churn in Social Networks. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_9

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