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How to Compute Optimal Catalog Mailing Decisions

Author

Listed:
  • Füsun F. Gönül

    (401 E. Waldheim Road, Pittsburgh, Pennsylvania 15215-1936)

  • Frenkel Ter Hofstede

    (Department of Marketing, McCombs School of Business, University of Texas at Austin, CBA 7.234/B6700, Austin, Texas 78712)

Abstract

We develop, estimate, and test a response model of order timing and order volume decisions of catalog customers and derive a Bayes rule for optimal mailing strategies. The model integrates the and components of the response; incorporates the of the firm; and uses a Bayesian framework to determine the optimal mailing rule for each catalog customer. The we propose for optimal mailing strategy allows for a broad set of objectives to be realized across the time horizon, such as profit maximization, customer retention, and utility maximization with or without risk aversion. We find that optimizing the objective function over multiple periods as opposed to a single period leads to higher expected profits and expected utility. Our results indicate that the cataloguer is well advised to send fewer catalogs than its current practice in order to maximize expected profits and utility.

Suggested Citation

  • Füsun F. Gönül & Frenkel Ter Hofstede, 2006. "How to Compute Optimal Catalog Mailing Decisions," Marketing Science, INFORMS, vol. 25(1), pages 65-74, 01-02.
    • Handle: RePEc:inm:ormksc:v:25:y:2006:i:1:p:65-74
    DOI: 10.1287/mksc.1050.0136
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    Cited by:

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    2. Thomas, Suman Ann & Feng, Shanfei & Krishnan, Trichy V., 2015. "To retain? To upgrade? The effects of direct mail on regular donation behavior," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 48-63.
    3. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    4. Makoto Abe, 2009. "“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 28(3), pages 541-553, 05-06.
    5. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    6. Gázquez-Abad, Juan Carlos & Canniére, Marie Hélène De & Martínez-López, Francisco J., 2011. "Dynamics of Customer Response to Promotional and Relational Direct Mailings from an Apparel Retailer: The Moderating Role of Relationship Strength," Journal of Retailing, Elsevier, vol. 87(2), pages 166-181.
    7. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    8. Sarkar, Mainak & De Bruyn, Arnaud, 2021. "LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 80-95.
    9. Karsten Hansen & Vishal Singh, 2009. "Market Structure Across Retail Formats," Marketing Science, INFORMS, vol. 28(4), pages 656-673, 07-08.
    10. Feld, Sebastian & Frenzen, Heiko & Krafft, Manfred & Peters, Kay & Verhoef, Peter C., 2013. "The effects of mailing design characteristics on direct mail campaign performance," International Journal of Research in Marketing, Elsevier, vol. 30(2), pages 143-159.
    11. Damgaard, Mette Trier & Gravert, Christina, 2018. "The hidden costs of nudging: Experimental evidence from reminders in fundraising," Journal of Public Economics, Elsevier, vol. 157(C), pages 15-26.
    12. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    13. George, Morris & Kumar, V. & Grewal, Dhruv, 2013. "Maximizing Profits for a Multi-Category Catalog Retailer," Journal of Retailing, Elsevier, vol. 89(4), pages 374-396.
    14. Kumar, V. & George, Morris & Pancras, Joseph, 2008. "Cross-buying in retailing: Drivers and consequences," Journal of Retailing, Elsevier, vol. 84(1), pages 15-27.
    15. Vafainia, Saeid & Breugelmans, Els & Bijmolt, Tammo, 2019. "Calling Customers to Take Action: The Impact of Incentive and Customer Characteristics on Direct Mailing Effectiveness," Journal of Interactive Marketing, Elsevier, vol. 45(C), pages 62-80.
    16. Lemieux, James & Peterson, Robert A., 2011. "Purchase deadline as a moderator of the effects of price uncertainty on search duration," Journal of Economic Psychology, Elsevier, vol. 32(1), pages 33-44, February.
    17. Malthouse, Edward C. & Derenthal, Kirstin M., 2008. "Improving predictive scoring models through model aggregation," Journal of Interactive Marketing, Elsevier, vol. 22(3), pages 51-68.
    18. Durango-Cohen, Elizabeth J. & Torres, Ramón L. & Durango-Cohen, Pablo L., 2013. "Donor Segmentation: When Summary Statistics Don't Tell the Whole Story," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 172-184.
    19. 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.

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