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A Model of Adaptive Control of Promotional Spending

Author

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  • John D. C. Little

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

Companies try to conduct their marketing operations so as to respond to changing market conditions. A model of such a process is studied for the case of setting promotion rate. Company sales are functions of promotional spending, but the relation changes with time. An adaptive system is devised that works as follows: Information about sales response is collected by performing an experiment. The experimental results are used to update a sales response model. Promotion rate is chosen to maximize expected profit in the next time period. The cycle is repeated. In designing the experiment, sample size is chosen to minimize the cost of imperfect information plus the cost of experimentation. The model employs a quadratic sales response function with a parameter that, changes according to a first order, autoregressive process. The optimal adaptive system turns out to involve exponential smoothing of the experimental results. A numerical example is studied analytically and by simulation. The adaptive system is found to work better than various other policies. In a sensitivity analysis, an adaptive system derived for one underlying model of the market is found to perform well even when certain other models actually apply.

Suggested Citation

  • John D. C. Little, 1966. "A Model of Adaptive Control of Promotional Spending," Operations Research, INFORMS, vol. 14(6), pages 1075-1097, December.
    • Handle: RePEc:inm:oropre:v:14:y:1966:i:6:p:1075-1097
    DOI: 10.1287/opre.14.6.1075
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    Cited by:

    1. John R. Hauser & Steven M. Shugan, 2008. "Defensive Marketing Strategies," Marketing Science, INFORMS, vol. 27(1), pages 88-110, 01-02.
    2. John D. C. Little, 2004. "Models and Managers: The Concept of a Decision Calculus," Management Science, INFORMS, vol. 50(12_supple), pages 1841-1853, December.
    3. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
    4. Günter J. Hitsch, 2006. "An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty," Marketing Science, INFORMS, vol. 25(1), pages 25-50, 01-02.
    5. Steven M. Shugan, 2007. "—It's the Findings, Stupid, Not the Assumptions," Marketing Science, INFORMS, vol. 26(4), pages 449-459, 07-08.
    6. Boutselis, Petros & McNaught, Ken, 2014. "Finite-Time Horizon Logistics Decision Making Problems: Consideration of a Wider Set of Factors," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Innovative Methods in Logistics and Supply Chain Management: Current Issues and Emerging Practices. Proceedings of the Hamburg International Conferenc, volume 19, pages 249-274, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. Markus Christen, 2005. "Research Note---Cost Uncertainty Is Bliss: The Effect of Competition on the Acquisition of Cost Information for Pricing New Products," Management Science, INFORMS, vol. 51(4), pages 668-676, April.
    8. Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    9. John R. Hauser & Guilherme (Gui) Liberali & Glen L. Urban, 2014. "Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph," Management Science, INFORMS, vol. 60(6), pages 1594-1616, June.
    10. Steven M. Shugan & Debanjan Mitra, 2014. "A Theory for Market Growth or Decline," Marketing Science, INFORMS, vol. 33(1), pages 47-65, January.
    11. Markus Christen & William Boulding & Richard Staelin, 2009. "Optimal Market Intelligence Strategy When Management Attention Is Scarce," Management Science, INFORMS, vol. 55(4), pages 526-538, April.
    12. Steven M. Shugan, 2004. "Endogeneity in Marketing Decision Models," Marketing Science, INFORMS, vol. 23(1), pages 1-3.
    13. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    14. Roland T. Rust & J. Jeffrey Inman & Jianmin Jia & Anthony Zahorik, 1999. "What You Know About Customer-Perceived Quality: The Role of Customer Expectation Distributions," Marketing Science, INFORMS, vol. 18(1), pages 77-92.
    15. Omar Besbes & Costis Maglaras, 2009. "Revenue Optimization for a Make-to-Order Queue in an Uncertain Market Environment," Operations Research, INFORMS, vol. 57(6), pages 1438-1450, December.
    16. Nicholas C. Petruzzi & Maqbool Dada, 2001. "Information and Inventory Recourse for a Two-Market, Price-Setting Retailer," Manufacturing & Service Operations Management, INFORMS, vol. 3(3), pages 242-263, October.
    17. Han-Kuang Tien, 2017. "How Much Should Managers Pay for Celebrity Endorsements?," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 9(2), pages 68-77, April.

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