Efficient learning of donor retention strategies for the American

The American Red Cross (ARC) and many other disaster relief organizations often encounter the problem of retaining one-time disaster donorspeople who have given their first donation in response to a major disaster. Only 30 % of these donors return to give a second time after their initial contribution. A critical problem for the ARC is to motivate disaster donors to contribute to long-term operations and post-disaster programs such as community disaster preparation or emergency response training. In a typical disaster-donor retention campaign, the ARC sends an appeal for funds (usually a mailed letter) to a segment of disaster donors. The appeal is designed using a combination of features related to the letter format and contents, donor and campaign type, and the use of various gift items. The number of possible designs is exponential in the number of features. The outcome is evaluated using the success rate, defined as the proportion of mailings in a campaign that elicited a donation. The goal is to find the best set of campaign attributes that maximize the success rate as quickly as possible, subject to constraints on the number of campaigns that can be conducted. Linear regression based on historical data has been used to estimate the effects of the campaign attributes on the success rate (Ryzhov et al. 2013). The results of the estimation, however, are subject to change afte