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Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing

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  • Chun, Young H.

Abstract

In direct marketing, customers are usually asked to take a specific action, and their responses are recorded over time and stored in a database. Based on the response data, we can estimate the number of customers who will ultimately respond, the number of responses anticipated to receive by a certain period of time, and the like. The goal of this article is to derive and propose several estimation methods and compare their performances in a Monte Carlo simulation. The response patterns can be described by a simple geometric function, which relates the number of responses to elapsed time. The “maximum likelihood” estimator appears to be the most effective method of estimating the parameters of this function. As we have more sample observations, the maximum likelihood estimates also converge to the true parameter values rapidly.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:3:p:673-678
    DOI: 10.1016/j.ejor.2011.10.008
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    Cited by:

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    2. Konstantin Kogan & Avi Herbon & Beatrice Venturi, 2020. "Direct marketing of an event under hazards of customer saturation and forgetting," Annals of Operations Research, Springer, vol. 295(1), pages 207-227, December.
    3. 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.
    4. Gang Chen & Shuaiyong Xiao & Chenghong Zhang & Huimin Zhao, 2023. "A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction," Information Systems Research, INFORMS, vol. 34(4), pages 1513-1532, December.
    5. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.

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