Getting the Most Out of A/B Tests Using the Asymptotic Minimax-Regret Criteria
提出渐近最小最大遗憾准则，一种基于A/B测试选择营销行动的决策方法，通过优化决策阈值来最小化最大期望净损失，案例显示能有效提升收入。
Many firms conduct A/B tests to find a marketing action that improves a value of interest, such as revenue or profit. We develop the asymptotic minimax regret (AMMR) criterion, a practical decision-theoretic approach for choosing among binary marketing actions based on A/B tests. The AMMR is a general large-sample approximation of the minimax-regret criterion from a frequentist standpoint. Our method directly optimizes the decision-relevant metric, accounting for the product of the error probability and the associated magnitude of value loss. Implementing the AMMR decision rule is straightforward; it comprises simply comparing the standardized treatment-effect estimate to the AMMR-optimal decision threshold. The AMMR suggests selecting the treatment whenever the point estimate is positive, as this minimizes the maximum expected net loss from decision errors. A case study of a mobile game company’s A/B testing with Monte Carlo validation demonstrates that the AMMR decision rule effectively selects the optimal marketing action and improves revenue across various data-generating processes. This paper was accepted by Raphael Thomadsen, marketing. Funding: K. X. Chiong gratefully acknowledges the financial support from the NEC Foundation of America. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06590 .