Efficiently Evaluating Targeting Policies: Improving Upon Champion vs. Challenger Experiments

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. Joint with Duncan Simester and Artem Timoshenk.

doi.org/10.1287/mnsc.2019.3379

Biography

Spyros Zoumpoulis is an assistant professor of Decision Sciences at INSEAD. His research is on using analytics and machine learning to optimize marketing and operations decisions. His current focus is on investigating how to use data from field experiments to make optimal decisions, such as targeting decisions in marketing. More generally, he is broadly interested in problems at the interface of networks, learning with data, and decision making. His research has appeared in leading management academic journals such as Management Science and Operations Research.