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Sequential Learning in Designing Marketing Campaigns for Market Entry

Author

Listed:
  • Somayeh Moazeni

    (School of Business, Stevens Institute of Technology, Hoboken, New Jersey 07030)

  • Boris Defourny

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Monika J. Wilczak

    (Accenture, New York, New York 10105)

Abstract

Developing marketing campaigns for a new product or a new target population is challenging because of the scarcity of relevant historical data. Building on dynamic Bayesian learning, a sequential optimization assists in creating new data points within a finite number of learning phases. This procedure identifies effective advertisement design elements as well as customer segments that maximize the expected outcome of the final marketing campaign. In this paper, the marketing campaign performance is modeled by a multiplicative advertising exposure model with Poisson arrivals. The intensity of the Poisson process is a function of the marketing campaign features. A forward-looking measurement policy is formulated to maximize the expected improvement in the value of information in each learning phase. A computationally efficient approach is proposed that consists of solving a sequence of mixed-integer linear optimization problems. The performance of the optimal learning policy over a set of benchmark policies is evaluated using examples inspired from the property and casualty insurance industry. Further extensions of the model are discussed.

Suggested Citation

  • Somayeh Moazeni & Boris Defourny & Monika J. Wilczak, 2020. "Sequential Learning in Designing Marketing Campaigns for Market Entry," Management Science, INFORMS, vol. 66(9), pages 4226-4245, September.
    • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:9:p:4226-4245
    DOI: 10.287/mnsc.2019.3384
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    References listed on IDEAS

    as
    1. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    2. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    3. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    4. Stephen E. Chick & Jürgen Branke & Christian Schmidt, 2010. "Sequential Sampling to Myopically Maximize the Expected Value of Information," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 71-80, February.
    5. repec:dau:papers:123456789/1906 is not listed on IDEAS
    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.
    7. Bin Han & Ilya O. Ryzhov & Boris Defourny, 2016. "Optimal Learning in Linear Regression with Combinatorial Feature Selection," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 721-735, November.
    8. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    9. Dimitris Bertsimas & Adam J. Mersereau, 2007. "A Learning Approach for Interactive Marketing to a Customer Segment," Operations Research, INFORMS, vol. 55(6), pages 1120-1135, December.
    10. Dominique M. Hanssens & Peter S. H. Leeflang & Dick R. Wittink, 2005. "Market response models and marketing practice," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(4‐5), pages 423-434, July.
    11. Klapdor, Sebastian & Anderl, Eva M. & von Wangenheim, Florian & Schumann, Jan H., 2014. "Finding the Right Words: The Influence of Keyword Characteristics on Performance of Paid Search Campaigns," Journal of Interactive Marketing, Elsevier, vol. 28(4), pages 285-301.
    12. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
    13. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    14. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
    15. K. Coussement & W. Buckinx, 2011. "A Probability-Mapping Algorithm for Calibrating the Posterior Probabilities: A Direct Marketing Application," Post-Print hal-00788068, HAL.
    16. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    17. Boris Defourny & Ilya O. Ryzhov & Warren B. Powell, 2015. "Optimal Information Blending with Measurements in the L 2 Sphere," Mathematics of Operations Research, INFORMS, vol. 40(4), pages 1060-1088, October.
    18. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
    19. Gerard J. Tellis & Philip Hans Franses, 2006. "Optimal Data Interval for Estimating Advertising Response," Marketing Science, INFORMS, vol. 25(3), pages 217-229, 05-06.
    20. repec:dau:papers:123456789/1908 is not listed on IDEAS
    21. Peter J. Danaher, 2007. "Modeling Page Views Across Multiple Websites with an Application to Internet Reach and Frequency Prediction," Marketing Science, INFORMS, vol. 26(3), pages 422-437, 05-06.
    Full references (including those not matched with items on IDEAS)

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    1. Jingwen Zhang & Yifang Chen & Amandeep Singh, 2022. "Causal Bandits: Online Decision-Making in Endogenous Settings," Papers 2211.08649, arXiv.org, revised Feb 2023.
    2. Peng, Yifeng & Tao, Xiangyang & Hong, Jingke & Sun, Lulu & Yuan, Xin, 2024. "Understanding the optimal strategy of carbon labelled product advertising delivery: A dynamic differential equation analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).

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