  In a multi-channel marketing world, the purchase decision journey encounters
many interactions (e.g., email, mobile notifications, display advertising,
social media, and so on). These impressions have direct (main effects), as well
as interactive influence on the final decision of the customer. To maximize
conversions, a marketer needs to understand how each of these marketing efforts
individually and collectively affect the customer's final decision. This
insight will help her optimize the advertising budget over interacting
marketing channels. This problem of interpreting the influence of various
marketing channels to the customer's decision process is called marketing
attribution. We propose a Bayesian model of marketing attribution that captures
established modes of action of advertisements, including the direct effect of
the ad, decay of the ad effect, interaction between ads, and customer
heterogeneity. Our model allows us to incorporate information from customer's
features and provides usable error bounds for parameters of interest, like the
ad effect or the half-life of an ad. We apply our model on a real-world dataset
and evaluate its performance against alternatives in simulations.
