Anya Shchetkina

Targeting and personalization policies can be used to improve outcomes beyond the uniform policy that assigns the best performing treatment in an A/B test to everyone. Personalization relies on the presence of heterogeneity of treatment effects, yet, as we show in this paper, heterogeneity alone is not sufficient for personalization to be successful. We develop a statistical model to quantify "actionable heterogeneity,"" or the conditions when personalization is likely to outperform the best uniform policy. We show that actionable heterogeneity can be visualized as crossover interactions in outcomes across treatments and depends on three population-level parameters: within-treatment heterogeneity, cross-treatment correlation, and the variation in average responses. Our model can be used to predict the expected gain from personalization prior to running an experiment and also allows for sensitivity analysis, providing guidance on how changing treatments can affect the personalization gain. To validate our model, we apply five common personalization approaches to two large-scale field experiments with many interventions that encouraged flu vaccination. We find an 18% gain from personalization in one and a more modest 4% gain in the other, which is consistent with our model. Counterfactual analysis shows that this difference in the gains from personalization is driven by a drastic difference in within-treatment heterogeneity. However, reducing cross-treatment correlation holds a larger potential to further increase personalization gains. Our findings provide a framework for assessing the potential from personalization and offer practical recommendations for improving gains from targeting in multi-intervention settings.

Recent years have seen a resurgence in interest in marketing mix models (MMMs), which are aggregate-level models of marketing effectiveness. Often these models incorporate nonlinear effects, and either implicitly or explicitly assume that marketing effectiveness varies over time. In this paper, we show that nonlinear and time-varying effects are often not identifiable from standard marketing mix data: while certain data patterns may be suggestive of nonlinear effects, such patterns may also emerge under simpler models that incorporate dynamics in marketing effectiveness. This lack of identification is problematic because nonlinearities and dynamics suggest fundamentally different optimal marketing allocations. We examine this identification issue through theory and simulations, wherein we explore the exact conditions under which conflation between the two types of models is likely to occur. In doing so, we introduce a flexible Bayesian nonparametric model that allows us to both flexibly simulate and estimate different data-generating processes. We show that conflating the two types of effects is especially likely in the presence of autocorrelated marketing variables, which are common in practice, especially given the widespread use of stock variables to capture long-run effects of advertising. We illustrate these ideas through numerous empirical applications to real-world marketing mix data, showing the prevalence of the conflation issue in practice. Finally, we show how marketers can avoid this conflation, by designing experiments that strategically manipulate spending in ways that pin down model form.