Efficiently Evaluating Targeting Policies: Improving on Champion vs. Challenger Experiments
针对冠军与挑战者实验评估定向策略的不足，提出一种新的实验设计和估计方法，通过随机分配营销行动并识别策略无差异的客户群，实现更精准高效的策略比较，对营销和决策科学研究者有参考价值。
Champion versus challenger field experiments are widely used to compare the performance of different targeting policies. These experiments randomly assign customers to receive marketing actions recommended by either the existing (champion) policy or the new (challenger) policy, and then compare the aggregate outcomes. We recommend an alternative experimental design and propose an alternative estimation approach to improve the evaluation of targeting policies. The recommended experimental design randomly assigns customers to marketing actions. This allows evaluation of any targeting policy without requiring an additional experiment, including policies designed after the experiment is implemented. The proposed estimation approach identifies customers for whom different policies recommend the same action and recognizes that for these customers there is no difference in performance. This allows for a more precise comparison of the policies. We illustrate the advantages of the experimental design and estimation approach using data from an actual field experiment. We also demonstrate that the grouping of customers, which is the foundation of our estimation approach, can help to improve the training of new targeting policies. This paper was accepted by Matthew Shum, marketing.