Customer Churn Prediction in B2B Contexts

  • Iris FigalistEmail author
  • Christoph Elsner
  • Jan Bosch
  • Helena Holmström Olsson
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 370)


While business-to-customer (B2C) companies, in the telecom sector for instance, have been making use of customer churn prediction for many years, churn prediction in the business-to-business (B2B) domain receives much less attention in existing literature. Nevertheless, B2B-specific characteristics, such as a lower number of customers with much higher transactional values, indicate the importance of identifying potentially churning customers. To achieve this, we implemented a prediction model for customer churn within a B2B software product and derived a model based on the results. For one, we present an approach that enables the mapping of customer- and end-user-data based on “customer phases” which allows the prediction model to take all critical influencing factors into consideration. In addition to that, we introduce a B2B customer churn prediction process based on the proposed data mapping.


Customer churn prediction B2B Data analysis 


  1. 1.
    Chang, H., Tsay, S.: Integrating of SOM and K-mean in data mining clustering: an empirical study of CRM and profitability evaluation (2004)Google Scholar
  2. 2.
    Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)CrossRefGoogle Scholar
  3. 3.
    Figalist, I., Elsner, C., Bosch, J., Olsson, H.H.: Business as unusual: a model for continuous real-time business insights based on low level metrics. In: Proceedings of SEAA. IEEE (2019)Google Scholar
  4. 4.
    Figalist, I., Elsner, C., Bosch, J., Olsson, H.H.: Scaling agile beyond organizational boundaries: coordination challenges in software ecosystems. In: Kruchten, P., Fraser, S., Coallier, F. (eds.) XP 2019. LNBIP, vol. 355, pp. 189–206. Springer, Cham (2019). Scholar
  5. 5.
    Goddard, W., Melville, S.: Research Methodology: An Introduction. Juta and Company Ltd., Cape Town (2004)Google Scholar
  6. 6.
    Hughes, A.M.: Strategic Database Marketing. McGraw-Hill Pub, Co., New York (2005)Google Scholar
  7. 7.
    Jahromi, A.T., Stakhovych, S., Ewing, M.: Managing B2B customer churn, retention and profitability. Ind. Mark. Manag. 43(7), 1258–1268 (2014)CrossRefGoogle Scholar
  8. 8.
    Kandeil, D.A., Saad, A.A., Youssef, S.M.: A two-phase clustering analysis for B2B customer segmentation. In: Proceedings of INCoS, pp. 221–228. IEEE (2014)Google Scholar
  9. 9.
    Mencarelli, R., Riviere, A.: Perceived value in B2B and B2C: a comparative approach and cross-fertilization. Mark. Theory 15(2), 201–220 (2015)CrossRefGoogle Scholar
  10. 10.
    Rauyruen, P., Miller, K.E.: Relationship quality as a predictor of B2B customer loyalty. J. Bus. Res. 60(1), 21–31 (2007)CrossRefGoogle Scholar
  11. 11.
    Sajjadi, S.: Introduction to churn prediction in Python (2018). Accessed 11 Sept 2019
  12. 12.
    Stevens, R.P.: B-to-B customer retention: seven strategies for keeping your customers. White Paper (2005).
  13. 13.
    Ullah, I., Raza, B., Malik, A.K., Imran, M., Islam, S.U., Kim, S.W.: A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector. IEEE Access 7, 60134–60149 (2019)CrossRefGoogle Scholar
  14. 14.
    Verbeke, W., Martens, D., Mues, C., Baesens, B.: Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert. Syst. Appl. 38(3), 2354–2364 (2011)CrossRefGoogle Scholar
  15. 15.
    Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., Kennedy, P.J.: Training deep neural networks on imbalanced data sets. In: Proceedings of IJCNN, pp. 4368–4374. IEEE (2016)Google Scholar
  16. 16.
    Yan, L., Wolniewicz, R.H., Dodier, R.: Predicting customer behavior in telecommunications. IEEE Intell. Syst. 19(2), 50–58 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Iris Figalist
    • 1
    Email author
  • Christoph Elsner
    • 1
  • Jan Bosch
    • 2
  • Helena Holmström Olsson
    • 3
  1. 1.Corporate Technology, Siemens AGMunichGermany
  2. 2.Department of Computer Science and EngineeringChalmers University of TechnologyGöteborgSweden
  3. 3.Department of Computer Science and Media TechnologyMalmö UniversityMalmöSweden

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