IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v67y2016i9d10.1057_jors.2016.8.html
   My bibliography  Save this article

Predicting time-to-churn of prepaid mobile telephone customers using social network analysis

Author

Listed:
  • Aimée Backiel

    (Katholieke Universiteit Leuven)

  • Bart Baesens

    (Katholieke Universiteit Leuven
    University of Southampton
    Vlerick, Leuven-Gent Management School)

  • Gerda Claeskens

    (Katholieke Universiteit Leuven)

Abstract

Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.

Suggested Citation

  • Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:9:d:10.1057_jors.2016.8
    DOI: 10.1057/jors.2016.8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/jors.2016.8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/jors.2016.8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    3. B Baesens & T Van Gestel & M Stepanova & D Van den Poel & J Vanthienen, 2005. "Neural network survival analysis for personal loan data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1089-1098, September.
    4. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
    5. Mertens, Frédéric & Saint-Charles, Johanne & Mergler, Donna, 2012. "Social communication network analysis of the role of participatory research in the adoption of new fish consumption behaviors," Social Science & Medicine, Elsevier, vol. 75(4), pages 643-650.
    6. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, September.
    7. K.W. de Bock & D. van den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Post-Print hal-00800160, HAL.
    8. J-K Im & D W Apley & C Qi & X Shan, 2012. "A time-dependent proportional hazards survival model for credit risk analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(3), pages 306-321, March.
    9. Aksoy, Lerzan & Buoye, Alexander & Aksoy, Pelin & Larivière, Bart & Keiningham, Timothy L., 2013. "A Cross-national Investigation of the Satisfaction and Loyalty Linkage for Mobile Telecommunications Services across Eight Countries," Journal of Interactive Marketing, Elsevier, vol. 27(1), pages 74-82.
    10. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shen, Ai-Zhong & Guo, Jin-Li & Wu, Guo-Lin & Jia, Shu-Wei, 2018. "The agglomeration phenomenon influence on the scaling law of the scientific collaboration system," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 461-467.
    2. Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.
    3. Haupt, Johannes & Lessmann, Stefan, 2020. "Targeting Cutsomers Under Response-Dependent Costs," IRTG 1792 Discussion Papers 2020-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Arturo Briseño & Bryan W. Husted & Jorge M. Rocha, 2019. "Methodological problems in research on the diffusion of management practices," Contaduría y Administración, Accounting and Management, vol. 64(1), pages 11-12, Enero-Mar.
    5. Zihayat, Morteza & Ayanso, Anteneh & Davoudi, Heidar & Kargar, Mehdi & Mengesha, Nigussie, 2021. "Leveraging non-respondent data in customer satisfaction modeling," Journal of Business Research, Elsevier, vol. 135(C), pages 112-126.
    6. Johannes Haupt & Stefan Lessmann, 2020. "Targeting customers under response-dependent costs," Papers 2003.06271, arXiv.org, revised Aug 2021.
    7. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    2. Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    3. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    4. Gary Mena & Kristof Coussement & Koen W. Bock & Arno Caigny & Stefan Lessmann, 2024. "Exploiting time-varying RFM measures for customer churn prediction with deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 765-787, August.
    5. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    6. Höppner, Sebastiaan & Stripling, Eugen & Baesens, Bart & Broucke, Seppe vanden & Verdonck, Tim, 2020. "Profit driven decision trees for churn prediction," European Journal of Operational Research, Elsevier, vol. 284(3), pages 920-933.
    7. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    8. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    9. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    10. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    11. Todor Krastevich, 2013. "Predicting Consumer Choices Through Analysis of Interactions in Social Networks," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 3, pages 24-40, September.
    12. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
    13. Szeląg, Marcin & Słowiński, Roman, 2024. "Explaining and predicting customer churn by monotonic rules induced from ordinal data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 414-424.
    14. Maldonado, Sebastián & López, Julio & Vairetti, Carla, 2020. "Profit-based churn prediction based on Minimax Probability Machines," European Journal of Operational Research, Elsevier, vol. 284(1), pages 273-284.
    15. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    16. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
    17. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    18. Muhammad Azeem & Muhammad Usman & A. C. M. Fong, 2017. "A churn prediction model for prepaid customers in telecom using fuzzy classifiers," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(4), pages 603-614, December.
    19. Aurélie Lemmens & Sunil Gupta, 2020. "Managing Churn to Maximize Profits," Marketing Science, INFORMS, vol. 39(5), pages 956-973, September.
    20. Maldonado, Sebastián & Domínguez, Gonzalo & Olaya, Diego & Verbeke, Wouter, 2021. "Profit-driven churn prediction for the mutual fund industry: A multisegment approach," Omega, Elsevier, vol. 100(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:67:y:2016:i:9:d:10.1057_jors.2016.8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.