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In database marketing, data mining has been used extensively to find the optimal customer targets so as to maximize return on investment. In particular, using marketing campaign data, models are typically developed to identify characteristics of customers who are most likely to respond. While these models are helpful in identifying the likely responders, they may be targeting customers who have decided to take the desirable action or not regardless of whether they receive the campaign contact (e.g. mail, call). Based on many years of business experience, we identify the appropriate business objective and its associated mathematical objective function. We point out that the current approach is not directly designed to solve the appropriate business objective. We then propose a new methodology to identify the customers whose decisions will be positively influenced by campaigns. The proposed methodology is easy to implement and can be used in conjunction with most commonly used supervised learning algorithms. An example using simulated data is used to illustrate the proposed methodology. This paper may provide the database marketing industry with a simple but significant methodological improvement and open a new area for further research and development. KeywordsDatabase marketing, data mining, knowledge discovery, predictive modeling, response modeling, customer relationship management, customer development, upselling and cross-selling, treatment effect, true lift, interaction effect Y j P(X 3 J 4 J 3 J 3 J

SUMMARY To predict ordering probabilities of a multiple‐entry competition (e.g. a horse‐race), two models have been proposed. Harville proposed a simple and convenient model that can easily be used in practice. Henery proposed a more sophisticated model but it has no closed form solution. In this paper, we empirically compare the two models by using a series of logit models applied to horse‐racing data. In horse‐racing, many previous studies claimed that the win bet fraction is a reasonable estimate of the winning probability. to consider complicated bet types which involve more than one position, ordering probabilities (e.g. p(horse i wins and horse j finishes 2nd)) are required. The Harville and Henery models assume different running time distributions and produce different sets of ordering probabilities. This paper illustrates that the Harville model is not always as good as the Henery model in predicting ordering probabilities. The theoretical result concludes that, if the running time of every horse is normally distributed, the probabilities produced by the Harville model have a systematic bias for the strongest and weakest horses. We concentrate on the horse‐racing case but the methodology can be applied to other multiple‐entry competitions.