Between death and life - a formal decision model to decide on customer recovery investments

  • Dominikus Kleindienst
  • Daniela Waldmann
Research Paper
Part of the following topical collections:
  1. Special Issue on "Digitization of the Individual"


As digitization supports customers in gaining increased market transparency (Desai Hastings Law Journal, 65(6), 1469–1482, 2014), migrating from one organization to another (“customer migration”) is becoming easier and more attractive. Thus, taking measures to regain customers who terminated their relationship (“customer recovery”) has become increasingly important for organizations. With the growing importance of customer recovery in present times, organizations face even more challenges pertaining to risk of making wrong investment decisions. Organizations can either mistakenly invest in customer relations that are “alive” or irretrievably “dead.” Furthermore, it has the risk of not investing in inactive customer relations that have a chance to be revived (“dying”). Consequently, it is necessary for organizations to consider the probability that a customer relation is “alive,” “dying,” or “dead” when deciding on customer recovery. Based on these probabilities, an economically reasonable decision has to be made on whether to invest in the recovery of an individual customer relationship. Accordingly, based on a comprehensive discussion of related work, we propose a formal decision model on whether to invest in customer relation recovery. To demonstrate the decision model’s applicability, an illustrative case with sample calculation is presented and expert interviews are conducted.


Customer data Customer recovery Digitization Decision model 

JEL classification


  1. Auh, S., & Johnson, M. D. (2005). Compatibility effects in evaluations of satisfaction and loyalty. Journal of Economic Psychology, 26(1), 35–57.CrossRefGoogle Scholar
  2. Beath, C., Becerra-Fernandez, I., Ross, J., & Short, J. (2012). Finding value in the information explosion. MIT Sloan Management Review, 53(4), 18–20.Google Scholar
  3. Bitran, G. R., & Mondschein, S. V. (1996). Mailing decisions in the catalog sales industry. Management Science, 42(9), 1364–1381.CrossRefGoogle Scholar
  4. Blattberg, R. C., & Deighton, J. (1996). Manage marketing by the customer equity test. Journal of Interactive Marketing, 74, 136–144.Google Scholar
  5. Blattberg, R. C., Getz, G., & Thomas, J. S. (2001). Customer equity: Building and managing relationships as valuable assets. Boston: Harvard Business School Press.Google Scholar
  6. Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4), 378–394.CrossRefGoogle Scholar
  7. Czarniewski, S. (2014). Changes in consumer behaviour in the market and the value of companies. European Journal of Research and Reflection in Management Sciences, 2(2), 61–68.Google Scholar
  8. Desai, D. R. (2014). The new steam: On digitization, decentralization, and disruption. Hastings Law Journal, 65(6), 1469–1482.Google Scholar
  9. Dwyer, R. F. (1989). Customer lifetime valuation to support marketing decision making. Journal of Direct Marketing, 3(4), 8–15.CrossRefGoogle Scholar
  10. Dwyer, R. F. (1997). Customer lifetime valuation to support marketing decision making. Journal of Direct Marketing, 11(4), 6–13.CrossRefGoogle Scholar
  11. Englbrecht, A. (2007). Kundenwertorientiertes Kampagnenmanagement im CRM. Hamburg: Kovač.Google Scholar
  12. Fader, P. S., & Hardie, B. G. S. (2009). Probability models for customer-base analysis. Journal of Interactive Marketing, 23, 61–69.CrossRefGoogle Scholar
  13. Fader, P. S., Hardie, B. G. S., & Lok Lee, K. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415–430.CrossRefGoogle Scholar
  14. Gartner (2016). Gartner IT Glossary: Digitalization. Accessed 28 July 2017.
  15. Glady, N., Baesens, B., & Croux, C. (2009). A modified Pareto/NBD approach for predicting customer lifetime value. Expert Systems with Applications, 36(2), 2062–2071.CrossRefGoogle Scholar
  16. Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. London: Aldine.Google Scholar
  17. Görz, Q. (2011). An economics-driven decision model for data quality improvement – A contribution to data currency. In Proceedings of the 17th Americas Conference on Information Systems, Detroit, Michigan, August 2011.Google Scholar
  18. Griffin, J., & Lowenstein, M. W. (2001). Customer winback: How to recapture lost customers and keep them loyal. San Francisco: Jossey-Bass.Google Scholar
  19. Hopmann, J., & Thede, A. (2005). Applicability of customer churn forecasts in a non-contractual setting. In D. Baier, & K. D. Wernecke (Eds.), Innovations in classification, data science, and information systems (pp. 330–337). Berlin: Springer-Verlag.CrossRefGoogle Scholar
  20. Hosseini, S., Oberländer, A., Röglinger, M., & Wolf, T. (2015). Rethinking multichannel management in a digital world – A decision model for service providers. In Proceedings of the 12th International Conference on Wirtschaftsinformatik (WI), Osnabrück, Germany, March 2015.Google Scholar
  21. Johnson, M. D., Anderson, E. W., & Fornell, C. (1995). Rational and adaptive performance expectations in a customer satisfaction framework. Journal of Customer Research, 21, 128–140.Google Scholar
  22. Kotler, P. (2004). Ten deadly marketing sins: Signs and solutions. Hoboken: Wiley.Google Scholar
  23. Kramp, M. K. (2004). Exploring life and experience through narrative inquiry. In K. de Marrais & S. D. Lapan (Eds.), Foundations for research: Methods in education and the social sciences (pp. 103–121). Mahwah: Erlbaum.Google Scholar
  24. Kumar, V., & Reinartz, W. (2012). Customer relationship management: Concept, strategy, and tools. Berlin: Springer.CrossRefGoogle Scholar
  25. Lervik, O. L., & Johnson, M. D. (2003). Service equity, satisfaction and loyalty: From transaction-specific to cumulative evaluations. Journal of Service Research, 5, 184–195.CrossRefGoogle Scholar
  26. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt.Google Scholar
  27. Neslin, S. A., Taylor, G. A., Grantham, K. D., & McNeil, K. R. (2013). Overcoming the “recency trap” in customer relationship management. Journal of the Academy of Marketing Science, 41, 320–337.CrossRefGoogle Scholar
  28. Qi, J., Shu, H., & Li, H. (2006). Study on purchase probability model in CRM systems. In A. M. Tjoa, L. Xu, & S. S. Chaudhry (Eds.), Research and practical issues of Enterprise information systems (pp. 643–647). Boston: Springer.CrossRefGoogle Scholar
  29. Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4), 17–35.CrossRefGoogle Scholar
  30. Reinartz, W. J., & Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing, 67(1), 79–99.CrossRefGoogle Scholar
  31. Rezabakhsh, B., Bornemann, D., Hansen, U., & Schrader, U. (2006). Consumer power: A comparison of the old economy and the internet economy. Journal of Consumer Policy, 29, 3–16.CrossRefGoogle Scholar
  32. Rhee, S., & McIntyre, S. (2008). Including the effects of prior and recent contact effort in a customer scoring model for database marketing. Journal of the Academy of Marketing Science, 36(4), 538–551.CrossRefGoogle Scholar
  33. Rust, R. T., Zahorik, A. J., & Keiningham, T. L. (1995). Return on quality (ROQ): Making service quality financially accountable. The Journal of Marketing, 59, 58–70.CrossRefGoogle Scholar
  34. Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109–127.CrossRefGoogle Scholar
  35. Schmittlein, D. C., & Peterson, R. A. (1994). Customer base analysis: An industrial purchase process application. Marketing Science, 13(1), 41–67.CrossRefGoogle Scholar
  36. Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers: Who are they and what will they do next? Management Science, 33(1), 1–24.CrossRefGoogle Scholar
  37. Sonnenberg, C., & Vom Brocke, J. (2012). Evaluations in the science of the artificial – Reconsidering the build-evaluate pattern in design science research. In K. Peffers, M. Rothenberger, & B. Kuechler (Eds.), Design science research in information systems (pp. 381–397). Berlin: Springer.Google Scholar
  38. Stauss, B., & Seidel, W. (2004). Complaint management: The heart of CRM. Mason: Thompson/South-Western.Google Scholar
  39. Strauss, B., & Friege, C. (1999). Regaining service customers: Cost and benefits of regain management. Journal of Service Research, 1(4), 347–361.CrossRefGoogle Scholar
  40. Tesch, R. (1990). Qualitative research: Analysis types and software. London: Falmer press.Google Scholar
  41. Thomas, J. S., Blattberg, R. C., & Fox, E. J. (2004). Recapturing lost customers. Journal of Marketing Research, 41(1), 31–45.CrossRefGoogle Scholar
  42. Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4), 106–125.CrossRefGoogle Scholar
  43. Wübben, M., & Wangenheim, F. (2008). Instant customer base analysis: Managerial heuristics often 'get it right'. Journal of Marketing, 72, 82–93.CrossRefGoogle Scholar
  44. Zitzlsperger, D. F., Robbert, T., & Roth, S. (2007). Forecasting customer buying behaviour: The impact of "one-time buyer". In Proceedings of the ANZMAC 2007 Conference, University of Otago, Dunedin, New Zealand, December 2007.Google Scholar

Copyright information

© Institute of Applied Informatics at University of Leipzig 2018

Authors and Affiliations

  1. 1.FIM Research CenterAugsburgGermany
  2. 2.Fraunhofer FIT - Project Group Business and Information Systems EngineeringAugsburgGermany

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