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Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)

Author

Listed:
  • Ralf Elsner

    (Rhenania Group, 56061 Koblenz, Germany)

  • Manfred Krafft

    (Westfälische Wilhelms-Universität Münster, Institut für Marketing, Am Stadtgraben 13-15, 48143 Münster, Germany)

  • Arnd Huchzermeier

    (Wissenschaftliche Hochschule für Unternehmensführung (WHU), Otto-Beisheim Graduate School of Management, Burgplatz 2, 56179 Vallendar, Germany)

Abstract

Rhenania, a German direct mail-order company, turned its catalog mailing practices around within one year and consequently moved up in market position from number 5 to number 2. A dynamic multilevel modeling (DMLM) approach uses elasticities to determine the optimal frequency of catalog mailings, a customer-segmentation approach allows for optimization of mailings, and a recency, frequency, monetary-value (RFM) segmentation in combination with a chi-square automatic interaction detection (CHAID) algorithm determines when customers should receive a reactivation package—as opposed to a catalog—to optimize mailing efficiency further. The DMLM approach was so effective that Rhenania acquired two competitors (one a subdivision of Springer Verlag).

Suggested Citation

  • Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2003. "Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)," Interfaces, INFORMS, vol. 33(1), pages 50-66, February.
    • Handle: RePEc:inm:orinte:v:33:y:2003:i:1:p:50-66
    DOI: 10.1287/inte.33.1.50.12719
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    References listed on IDEAS

    as
    1. Bitran, Gabriel & Mondschein, Susana, 1997. "A comparative analysis of decision making procedures in the catalog sales industry," European Management Journal, Elsevier, vol. 15(2), pages 105-116, April.
    2. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
    3. Deb Campbell & Randy Erdahl & Doug Johnson & Eric Bibelnieks & Michael Haydock & Mark Bullock & Harlan Crowder, 2001. "Optimizing Customer Mail Streams at Fingerhut," Interfaces, INFORMS, vol. 31(1), pages 77-90, February.
    4. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
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    Cited by:

    1. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    2. Duncan I. Simester & Peng Sun & John N. Tsitsiklis, 2006. "Dynamic Catalog Mailing Policies," Management Science, INFORMS, vol. 52(5), pages 683-696, May.
    3. Duncan Simester & Yu (Jeffrey) Hu & Erik Brynjolfsson & Eric T. Anderson, 2009. "Dynamics Of Retail Advertising: Evidence From A Field Experiment," Economic Inquiry, Western Economic Association International, vol. 47(3), pages 482-499, July.
    4. Scott A. Neslin & Thomas P. Novak & Kenneth R. Baker & Donna L. Hoffman, 2009. "An Optimal Contact Model for Maximizing Online Panel Response Rates," Management Science, INFORMS, vol. 55(5), pages 727-737, May.
    5. Hayk Manucharyan, 2020. "How do managers actually choose suppliers? Evidence from revealed preference data," Working Papers 2020-12, Faculty of Economic Sciences, University of Warsaw.
    6. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.

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