Estimating Marketing Component Effects : Double Machine Learning from Targeted Digital Promotions

We estimate the causal effects of different targeted email promotions on the opening and purchase decisions of the consumers who receive them. To do so, we synthesize and extend recent advances in causal machine learning techniques to capture heterogeneity in the content of the email subject line itself, as well as heterogeneous consumer responses to the promotional offers and semantic choices contained therein. We find that content and framing are important for driving performance. We identify precise causal estimates of the effects of individual deal components, personalized content, and various semantic choices on consumer outcomes all the way down the conversion funnel. The decompositional nature of our methodology allows us to show how different combinations of keywords and promotional inducements produce significantly different outcomes, both within a given stage and across all stages of the funnel. Notably, discounts framed as clearance events sharply outperform those tied to particular products. We also find that components that drive engagement at the top of the funnel don't always lead to conversion at the bottom: their efficacy, across the funnel, is significantly moderated by the engagement levels of the consumers who receive them. Finally, leveraging both aspects of heterogeneity, we use off-policy evaluation to demonstrate the potential for significant gains from improved targeting