Identifying Causal Marketing-Mix Effects Using a Regression Discontinuity Design

We discuss how regression discontinuity designs arise naturally in settings where firms target marketing activity at consumers, and discuss how this aspect may be exploited for econometric inference of causal effects of marketing effort. Our main insight is to use commonly observed discreteness and kinks in the heuristics by which firms target such marketing activity to consumers for nonparametric identification. Such kinks, along with continuity restrictions that are typically satisfied in marketing and industrial organization applications, are sufficient for identification of local treatment effects. We review the theory of regression discontinuity estimation in the context of targeting, and explore its applicability to several marketing settings. We discuss identifiability of causal marketing effects using the design, and illustrate theoretically the conditions under which the RD estimator may be valid. Specifically, we argue that consideration of an underlying model of strategic consumer behavior reveals how identification hinges on model features such as the specification and value of structural parameters as well as belief structures. We present two empirical applications: the first, to measuring the effect of casino e-mail promotions targeted to customers based on ranges of their expected profitability; and the second, to measuring the effect of direct mail targeted by a B2C company to zip-codes based on thresholds of expected response. In both cases, we illustrate that exploiting the regression discontinuity design reveals negative effects of the marketing campaigns that would not have been uncovered using other approaches. Our results are nonparametric, easy to compute, and fully control for the endogeneity induced by the targeting rule.