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Joint Optimization of Customer Segmentation and Marketing Policy to Maximize Long-Term Profitability

  • J.-J. JONKER


With the advent of one-to-one marketing media, e.g. targeted direct mail or internet marketing, the opportunities to develop targeted marketing (customer relationship management) campaigns are enhanced in such a way that it is now both organizationally and economically feasible to profitably support a substantially larger number of marketing segments. However, the problem of what segments to distinguish, and what actions to take towards the different segments increases substantially in such an environment. A systematic analytic procedure optimizing both steps would be very welcome. In this study, we present a joint optimization approach addressing two issues: (1) the segmentation of customers into homogeneous groups of customers, (2) determining the optimal policy (i.e., what action to take from a set of available actions) towards each segment. We implement this joint optimization framework in a direct-mail setting for a charitable organization. Many previous studies in this area highlighted the importance of the following variables: R(ecency), F(requency), and M(onetary value). We use these variables to segment customers. In a second step, we determine which marketing policy is optimal using markov decision processes, following similar previous applications. The attractiveness of this stochastic dynamic programming procedure is based on the long-run maximization of expected average profit. Our contribution lies in the combination of both steps into one optimization framework to obtain an optimal allocation of marketing expenditures. Moreover, we control segment stability and policy performance by a bootstrap procedure. Our framework is illustrated by a real-life application. The results show that the proposed model outperforms a CHAID segmentation.

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Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 03/214.

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Length: 25 pages
Date of creation: Dec 2003
Date of revision:
Handle: RePEc:rug:rugwps:03/214
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  1. Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
  2. P. V. (Sundar) Balakrishnan & Varghese S. Jacob, 1996. "Genetic Algorithms for Product Design," Management Science, INFORMS, vol. 42(8), pages 1105-1117, August.
  3. Maurice W. Sasieni, 1989. "Optimal Advertising Strategies," Marketing Science, INFORMS, vol. 8(4), pages 358-370.
  4. Füsun Gönül & Meng Ze Shi, 1998. "Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models," Management Science, INFORMS, vol. 44(9), pages 1249-1262, September.
  5. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
  6. Hruschka, Harald, 2002. "Market share analysis using semi-parametric attraction models," European Journal of Operational Research, Elsevier, vol. 138(1), pages 212-225, April.
  7. Frank M. Bass & Jerry Wind, 1995. "Introduction to the Special Issue: Empirical Generalizations in Marketing," Marketing Science, INFORMS, vol. 14(3_supplem), pages G1-G5.
  8. Richard Paap & Philip Hans Franses & Bas Donkers & Jedid-Jah Jonker, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562.
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