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Getting the Most Out of A/B Tests Using the Asymptotic Minimax-Regret Criteria

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The asymptotic minimax regret criterion improves decision-making for A/B tests in marketing, suggesting optimal actions based on revenue outcomes.

The AMMR criterion reduces maximum expected loss from decision errors in A/B tests.
By optimizing the treatment-effect estimate against a threshold, the AMMR effectively guides marketing decisions.
A case study demonstrated improved revenue for a mobile game using the AMMR decision rule with Monte Carlo validation.
The AMMR approach allows firms to make data-driven decisions with practical implementation ease.

Joo et al. (2025) studied this question.

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The asymptotic minimax regret criterion improves decision-making for A/B tests in marketing, suggesting optimal actions based on revenue outcomes.