Assistant Professor of Decision Sciences
Targeting; Field Experiments; Machine Learning; Counterfactual Policy Logging; Policy Evaluation;
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.The authors 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. The authors illustrate the advantages of the experimental design and estimation approach using data from an actual field experiment. They also demonstrate that the grouping of customers, which is the foundation of their estimation approach, can help to improve the training of new targeting policies.