Anya Shchetkina

Major advertising platforms have recently increased privacy protections by limiting advertisers’ access to individual-level data. Instead of providing access to the granular raw data, the platforms only allow a limited number of aggregate queries to a dataset, which is further protected by adding differentially private noise. This paper studies whether and how advertisers can design effective targeting policies within these restrictive privacy preserving data environments. To achieve this, I develop a method based on Bayesian optimization that includes two innovations over the classic setup: (i) integral updating of posterior which allows to select best regions to query rather than points and (ii) targeting-aware acquisition function that dynamically selects regions most informative for the targeting task. I identify the conditions of the dataset and privacy environment that necessitate the use of such a “smart” querying strategy. I also show when a simple strategy, such as uniform binning, is sufficient. Finally, I apply the strategy to the Criteo AI Labs dataset for uplift modeling. I show that a simple benchmark strategy fails under differential privacy requirement in some settings. However, the strategic querying method delivers a robust performance that achieves the same level as a non-privacy-protected state-of-the-art machine learning method.

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.