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Optimizing Customer Mail Streams at Fingerhut

Author

Listed:
  • Deb Campbell

    (Fingerhut Companies, Inc. 4400 Baker Road, Minnetonka, Minnesota 55343)

  • Randy Erdahl

    (Fingerhut Companies, Inc.)

  • Doug Johnson

    (Fingerhut Companies, Inc.)

  • Eric Bibelnieks

    (IBM Global Services, 650 Third Avenue South, Minneapolis, Minnesota 55402)

  • Michael Haydock

    (IBM Global Services)

  • Mark Bullock

    (Level (3) Communications 1035 El Dorado Blvd., Broomfield, Colorado 80021)

  • Harlan Crowder

    (Hewlett-Packard Laboratories 1501 Page Mill Road, Palo Alto, California 94304)

Abstract

Fingerhut mails up to 120 catalogs per year to each of its 7 million customers. With this dense mail plan and mailing decisions made independently for each catalog, many customers were receiving redundant and unproductive catalogs. To identify and eliminate this excessive operational expense, IBM and Fingerhut together developed an optimization system that selects the most profitable sequence of catalogs, called a mail stream, for each customer. With mail streams, Fingerhut makes better mailing decisions at the customer level, resulting in increased profits. Today, Fingerhut runs this application weekly to find the most profitable mail stream for each customer.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:31:y:2001:i:1:p:77-90
    DOI: 10.1287/inte.31.1.77.9691
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    Citations

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    Cited by:

    1. Schröder, Nadine & Hruschka, Harald, 2016. "Investigating the effects of mailing variables and endogeneity on mailing decisions," European Journal of Operational Research, Elsevier, vol. 250(2), pages 579-589.
    2. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2004. "The 2003 ISMS Practice Prize Winner: Optimizing Rhenania's Direct Marketing Business Through Dynamic Multilevel Modeling (DMLM) in a Multicatalog-Brand Environment," Marketing Science, INFORMS, vol. 23(2), pages 192-206, June.
    3. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    4. Blattberg, Robert C. & Malthouse, Edward C. & Neslin, Scott A., 2009. "Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 157-168.
    5. Balaji Padmanabhan & Alexander Tuzhilin, 2003. "On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities," Management Science, INFORMS, vol. 49(10), pages 1327-1343, October.
    6. 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.
    7. Verhoef, Peter C. & Venkatesan, Rajkumar & McAlister, Leigh & Malthouse, Edward C. & Krafft, Manfred & Ganesan, Shankar, 2010. "CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 121-137.
    8. Alina Ferecatu & Arnaud Bruyn & Prithwiraj Mukherjee, 2024. "Silently killing your panelists one email at a time: The true cost of email solicitations," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 1216-1239, July.

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