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Improving Targeting by Taking Long-Term Relationships into Account

  • Benedikt LindenbeckEmail author
  • Rainer Olbrich
Conference paper
Part of the Developments in Marketing Science: Proceedings of the Academy of Marketing Science book series (DMSPAMS)

Abstract

Direct marketing is characterized by its high practical relevance, and it requires decision-makers to consider a variety of success factors to ensure the success of campaigns. In particular, the choice of recipients has substantial importance, and this selection can be based on various types of information. Information that reflects the behavior of potential recipients may offer better forecasting quality than demographic data, but this common assumption has not been substantiated empirically. On the basis of empirical data, this article examines whether such data can produce improved forecasting quality. The data set consists of the customer base of a German insurance company. With path analysis, the authors reveal that behavioral data achieve better predictability than demographic data. The consideration of these aspects thus allows for economically more advantageous management of direct marketing campaigns.

Keywords

Direct marketing Direct mailing Targeting Services marketing Path analysis 

References

  1. Arora, N., Dreze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., et al. (2008). Putting one-to-one marketing to work. Personalization, customization, and choice. Marketing Letters, 19, 305–321.CrossRefGoogle Scholar
  2. Asllani, A., & Halstead, D. (2015). A multi-objective optimization approach using the RFM model in direct marketing. Academy of Marketing Studies Journal, 19(3), 49–62.Google Scholar
  3. Bickelhaupt, D. L. (1967). Trends and innovations in the marketing of insurance. Journal of Marketing, 31(3), 17–22.CrossRefGoogle Scholar
  4. Blocker, C. P., & Flint, D. (2007). Customer segments as moving targets. Integrating customer value dynamism into segment instability logic. Industrial Marketing Management, 36(6), 810–822.CrossRefGoogle Scholar
  5. Bose, I., & Chen, X. (2009). Quantitative models for direct marketing. A review from systems perspective. European Journal of Operational Research, 195, 1–16.CrossRefGoogle Scholar
  6. Böttcher, M., Spott, M., Nauck, D., & Kruse, R. (2009). Mining changing customer segments in dynamic markets. Expert Systems with Applications, 36(1), 155–164.CrossRefGoogle Scholar
  7. Chiu, C. (2002). A case-based customer classification approach for direct marketing. Expert Systems with Applications, 22(2), 163–168.CrossRefGoogle Scholar
  8. Coussement, K., van den Bossche, F. A. M., & de Bock, K. W. (2014). Data accuracy’s impact on segmentation performance. Benchmarking RFM analysis, logistic regression, and decision trees. Journal of Business Research, 67(1), 2751–2758.CrossRefGoogle Scholar
  9. von der Wense, B. (1980). Planning, list selection, copy, layout, timing, testing can make or break mail pieces. Marketing News, 14(10), 7.Google Scholar
  10. Dong, X., Manchanda, P., & Chintagunta, P. K. (2009). Quantifying the benefits of individual-level targeting in the presence of firm strategic behavior. Journal of Marketing Research, 46(2), 207–221.CrossRefGoogle Scholar
  11. Gaffny, T. (1985). Direct mail acquisition. Three strategic factors. Nonprofit World Report, 3(5), 11–28.Google Scholar
  12. Guido, G., Prete, I. M., Miraglia, S., & De Mare, I. (2011). Targeting direct marketing campaigns by neural networks. Journal of Marketing Management, 27(9–10), 992–1006.CrossRefGoogle Scholar
  13. Haag, N. C. (2010). Direktmarketing mit Kundendaten aus Bonusprogrammen. Datenschutzrechtliche Einwilligung als Nutzungslizenz. Wiesbaden: Springer.CrossRefGoogle Scholar
  14. Hassell, H. J. G., & Monson, J. Q. (2014). Campaign targets and messages in direct mail fundraising. Political Behavior, 36(2), 359–376.CrossRefGoogle Scholar
  15. Holland, H. (2016). Dialogmarketing. In Offline- und online-marketing, mobile- und social-media marketing. Munich: Vahlen.CrossRefGoogle Scholar
  16. Kidiyoor, G. H. (2010). Key success strategies for marketing of high technology products. A review. Drishtikon—A Management Journal, 1(2), 37–64.Google Scholar
  17. Kim, Y., Street, W. N., Russell, G. J., & Menczer, F. (2005). Customer targeting. A neural network approach guided by genetic algorithms. Management Science, 51(2), 264–276.CrossRefGoogle Scholar
  18. Klitsch, J. (1997). Databases put the direct in direct marketing. Marketing Health Services, 17(1), 4–7.Google Scholar
  19. Lindenbeck, B. (2018). Steuerung von Dialogmarketingkampagnen. In R. Olbrich (Ed.), Schriftenreihe Marketing und Marktorientierte Unternehmensführung. Basel: Springer.Google Scholar
  20. Lu, X., Song, J., Liu, M., & Wu, X. (2011). A new two-group-forecast-and-selection method for direct marketing. Communications in Statistics, Simulation and Computation, 40(10), 1627–1636.CrossRefGoogle Scholar
  21. Ma, S., Hou, L., Yao, W., & Lee, B. (2016). A nonhomogeneous hidden Markov model of response dynamics and mailing optimization in direct marketing. European Journal of Operational Research, 253(2), 514–523.CrossRefGoogle Scholar
  22. Mann, A. (2007). Dialogmarketing-Kompetenz von Unternehmen. konzeptionelle Überlegungen und empirische Befunde. ZfB—Zeitschrift für Betriebswirtschaft, 77(3), 1–28.Google Scholar
  23. Mogos, R. I., & Acatrinei, C. (2015). Designing email marketing campaigns. Data mining approach based on consumer preferences. Annales Universitatis Apulensis Series Oeconomica, 17(19), 15–30.Google Scholar
  24. Morimoto, M., & Chang, S. (2006). Consumers’ attitudes toward unsolicited commercial e-mail and postal direct mail marketing methods. Intrusiveness, perceived loss of control, and irritation. Journal of Interactive Advertising, 7(1), 8–20.CrossRefGoogle Scholar
  25. 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(3), 320–337.CrossRefGoogle Scholar
  26. Oestreicher, K. G. (2011). Segmentation & the jobs-to-be-done theory. A conceptual approach to explaining product failure. Journal of Marketing Development & Competitivness, 5(2), 103–121.Google Scholar
  27. Olbrich, R., Lindenbeck, B. (2016). Targeting direct marketing campaigns with a view to continuous obligations. In: Proceedings of the 23rd International Conference on Recent Advances in Retailing and Services Science. Edinburgh, UK.Google Scholar
  28. Scovotti, C., & Spiller, L. D. (2006). Revisiting the conceptual definition of direct marketing. Perspectives from practitioners and scholars. Marketing Management Journal, 16(2), 188–202.Google Scholar
  29. Shankar, V., & Balasubramanian, S. (2009). Mobile marketing. A synthesis and prognosis. Journal of Interactive Marketing, 23(2), 118–129.CrossRefGoogle Scholar
  30. Stone, B., & Jacobs, R. (2008). Successful direct marketing methods. Interactive, database, and customer-based marketing for digital age (8th ed.). New York: McGraw-Hill.Google Scholar
  31. Tyagi, R. K. (1999). On the relationship between product substitutability and tacit collusion. Managerial and Decision Economics, 20(6), 293–298.CrossRefGoogle Scholar
  32. Vlasic, G., & Kesic, T. (2007). Analysis of consumers’ attitudes toward interactivity and relationship personalization as contemporary developments in interactive marketing communication. Journal of Marketing Communications, 13(2), 109–129.CrossRefGoogle Scholar
  33. Vriens, M., van der Scheer, H. R., Hoekstra, J. C., Bult, J. R. (1998). Conjoint experiments for direct mail response optimization. European Journal of Marketing, 32(¾), 323–339.CrossRefGoogle Scholar

Copyright information

© Academy of Marketing Science 2019

Authors and Affiliations

  1. 1.University of HagenHagenGermany

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