Skip to main content
SpringerLink
Log in

Preventing Churn in Telecommunications: The Forgotten Network

  • Conference paper
Advances in Intelligent Data Analysis XII (IDA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8207))

Included in the following conference series:

Abstract

This paper outlines an approach developed as a part of a company-wide churn management initiative of a major European telecom operator. We are focusing on explanatory churn model for the postpaid segment, assuming that the mobile telecom network, the key resource of operators, is also a churn driver in case it under delivers to customers’ expectations. Typically, insights generated by churn models are deployed in marketing campaigns; our model’s insights are used in network optimization in order to remove the key network related churn drivers and therefore prevent churn, rather than cure it. The insights generated by the model have caused a paradigm shift in managing the network with the operator where the research was conducted.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Verbeke, W., Martens, D., Mues, C., Baesens, B.: Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications (2010), doi:10.1016/j.eswa.2010.08.023

    Google Scholar 

  2. Ballings, M., Van den Poel, D.: The Relevant Length of Customer Event History for Churn Prediction: How long is long enough? Expert Systems with Applications (2012)

    Google Scholar 

  3. Hung, S., Yen, D.C., Wang, H.: Applying data mining to telecom churn management. Expert Systems with Applications 31(3), 515–524 (2006)

    Article  Google Scholar 

  4. Mozer, M., Wolniewicz, R., Johnson, E., Kaushansky, H.: Churn reduction in the wireless industry. In: Proceedings of the Neural Information Systems Conference (1999)

    Google Scholar 

  5. Wei, C., Chiu, I.: Turning telecommunications call details to churn prediction: A data mining approach. Expert Systems with Applications 23, 103–112 (2002)

    Article  Google Scholar 

  6. Au, W., Chan, K., Yao, X.: A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation 7(6), 532–545 (2003)

    Article  Google Scholar 

  7. Neslin, S., Gupta, S., Kamakura, W., Lu, J., Mason, C.: Detection defection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research 43(2), 204–211 (2006)

    Article  Google Scholar 

  8. Archaux, C., Martin, A., Khenchaf, A.: An SVM based churn detector in prepaid mobile telephony. In: International Conference on Information & Communication Technologies (ICTTA), pp. 19–23 (2004)

    Google Scholar 

  9. Lemmens, A., Croux, C.: Bagging and Boosting Classification Trees to Predict Churn. Journal of Marketing Research 43(2), 276–286 (2006)

    Article  Google Scholar 

  10. Idris, A., Khan, A., Lee, Y.S.: Intelligent churn prediction in telecom: Employing mRMR feature selection and RotBoost based ensemble classification. Applied Intelligence, 1–14 (2013)

    Google Scholar 

  11. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)

    Article  Google Scholar 

  12. Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A.: Social Ties and their Relevance to Churn in Mobile Telecom Networks. In: Proceedings of the 11th International Conference on Extending Database Technology, pp. 668–677 (2008)

    Google Scholar 

  13. Wang, Y., Cong, G., Song, G., Xie, K.: 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, pp. 1039–1048 (2010)

    Google Scholar 

  14. Richter, Y., Yom-Tov, E., Slonim, N.: Predicting customer churn in mobile networks through analysis of social groups. In: Proceedings of the SIAM International Conference on Data Mining, pp. 732–741 (2010)

    Google Scholar 

  15. Motahari, S., Mengshoel, O.J., Reuther, P., Appala, S., Zoia, L., Shah, J.: The Impact of Social Affinity on Phone Calling Patterns: Categorizing Social Ties from Call Data Records. In: Proc. of the Sixth Workshop on Social Network Mining and Analysis (2012)

    Google Scholar 

  16. Nanavati, A.A., Gurumurthy, S., Das, G., Chakraborty, D., Dasgupta, K., Mukherjea, S., Joshi, A.: On the structural properties of massive telecom call graphs: Findings and implications. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 435–444. ACM (2006)

    Google Scholar 

  17. Ngonmang, B., Viennet, E., Tchuente, M.: Churn prediction in a real online social network using local community analysis. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 282–288. IEEE (2012)

    Google Scholar 

  18. Polepally, A., Mohan, S.: Behavior Analysis of Telecom Data Using Social Networks Analysis. In: Behavior Computing, pp. 291–303. Springer, London (2012)

    Chapter  Google Scholar 

  19. Saravanan, M., Raajaa, G.V.: A Graph-Based Churn Prediction Model for Mobile Telecom Networks. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS, vol. 7713, pp. 367–382. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  20. Kusuma, P.D., Radosavljevik, D., Takes, F.W., van der Putten, P.: Combining Customer Attribute and Social Network Mining for Prepaid Mobile Churn Prediction. In: Proceedings of the 23rd Annual Belgian Dutch Conference on Machine Learning (Benelearn), pp. 50–58 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  22. Malhotra, A., Malhotra, C.K.: Exploring switching behavior of US mobile service customers. Journal of Services Marketing 27(1), 13–24 (2013)

    Article  MathSciNet  Google Scholar 

  23. Lima, E., Mues, C., Baesens, B.: Domain knowledge integration in data mining using decision tables: Case studies in churn prediction. Journal of the Operational Research Society 60, 1096–1106 (2009)

    Article  MATH  Google Scholar 

  24. Buckinx, W., Van den Poel, D.: 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)

    Article  MATH  Google Scholar 

  25. Anil Kumar, D., Ravi, V.: Predicting credit card customer churn in banks using data mining. International Journal of Data Analysis Techniques and Strategies 1(1), 4–28 (2008)

    Article  Google Scholar 

  26. Birke, D., Swann, G.P.: Network effects and the choice of mobile phone operator. Journal of Evolutionary Economics 16(1-2), 65–84 (2006)

    Article  Google Scholar 

  27. Min, D., Wan, L.: Switching factors of mobile customers in Korea. Journal of Service Science 1(1), 105–120 (2009)

    Article  Google Scholar 

  28. Seo, D., Ranganathan, C., Babad, Y.: Two-level model of customer retention in the US mobile telecommunications service market. Telecommunications Policy 32(3), 182–196 (2008)

    Article  Google Scholar 

  29. Radosavljevik, D., van der Putten, P., Kyllesbech Larsen, K.: The impact of experimental setup in prepaid churn prediction for mobile telecommunications: what to predict, for whom and does the customer experience matter. Trans. Mach. Learn. Data Min. 3(2), 80–99 (2010)

    Google Scholar 

  30. Oracle.: Oracle Database Documentation Library, http://www.oracle.com/pls/db102/homepage

  31. Pegasystems: Predictive Analytics Director (Version CDM 6.3) (Software). Pegasystems, Inc. (2008), http://www.pega.com/products/decision-management

  32. Kendall, M.: A New Measure of Rank Correlation. Biometrika 30(1-2), 81–89 (1938)

    Article  MathSciNet  MATH  Google Scholar 

  33. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Technique, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIACS, Leiden University, P.O. Box 9512, 2300 RA, Leiden, The Netherlands

    Dejan Radosavljevik & Peter van der Putten

Authors
  1. Dejan Radosavljevik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter van der Putten
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Information Systems, Computing and Mathematics, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK

    Allan Tucker  & Stephen Swift  & 

  2. Faculty of Computer Science/IT, Ostfalia University of Applied Sciences, Am Exer 2, 38302, WolfenbĂĽttel, Germany

    Frank Höppner

  3. Faculty of Science, Department of Information and Computing Science, Buys Ballot Laboratory, Universiteit Utrecht, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Arno Siebes

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Radosavljevik, D., van der Putten, P. (2013). Preventing Churn in Telecommunications: The Forgotten Network. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41398-8_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41397-1

  • Online ISBN: 978-3-642-41398-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation