Decision makers involved in list selection and purchase for direct marketing campaigns typically use sample response rates as a barometer of future success for a rollout effort. In some instances, decision makers may be convinced that sample response rates are not indicative of the true response rates for a given mailing list. Bayesian statistics present a methodology for revising test sample results using prior assessments and opinions on the likelihood of a particular response rate for given lists. Bayes estimation for updating response probabilities and an illustration of Bayes’ technique in direct marketing are presented.
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References
1.
DeGrootMorrisOptimal Statistical Decisions1970McGraw-HillNew York
2.
EhrmanChaim M.Estimating the Probability of Lists’ Rollout Success From Test ResultsJournal of Direct Marketing Research11986No. 1
3.
Ehrman, Chaim M. and Klaus J. Miescke (1987), “Toward a Structured Methodology for Evaluating and Selecting Mailing Lists in Direct Marketing,” unpublished manuscript.
4.
GreenPaul E.TullDonald S.Research for Marketing Decisions1978Prentice-HallEnglewood Cliffs, N.J
5.
WinklerRobert C.An Introduction to Bayesian Inference and Decision1972Holt, Rinehart and WinstonNew York
