document: Experimental design for solicitation campaigns Data mining techniques are routinely used by fundraisers to select those prospects from a large pool of candidates who are most likely to make a financial contribution . These techniques often rely on statistical models based on trial performance data . This trial performance data is typically obtained by soliciting a smaller sample of the possible prospect pool . Collecting this trial data involves a cost ; therefore the fundraiser is interested in keeping the trial size small while still collecting enough data to build a reliable statistical model that will be used to evaluate the remainder of the prospects . We describe an experimental design approach to optimally choose the trial prospects from an existing large pool of prospects . Prospects are clustered to render the problem practically tractable . We modify the standard D-optimality algorithm to prevent repeated selection of the same prospect cluster , since each prospect can only be solicited at most once . We assess the benefits of this approach on the KDD-98 data set by comparing the performance of the model based on the optimal trial data set with that of a model based on a randomly selected trial data set of equal size .
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