Multi-channel Attribution Modeling on User Journeys

Abstract

Consumers are often confronted with multiple types of online advertising before they click on advertisements or make a purchase. The respective attribution of the success of the companies’ marketing activities leads to a sophisticated allocation process. We developed a new approach to (1) address consumers’ buying decision processes, (2) to account for the effects of multiple online advertising channels, and (3) consequently attribute the success of marketing activities more realistically than current management heuristics do. For example, compared to standardized metrics, we found paid search advertising to be overestimated and retargeting display advertising to be underestimated. We further found that the use of a Bayesian mixture of normals approach is useful for considering heterogeneity in the individual propensity of consumers to purchase; for the majority of consumers (more than 90 %), repeated clicks on advertisements decrease their probability of purchasing. In contrast with this segment, we found a smaller segment of consumers (nearly 10 %) whose clicks on advertisements increase conversion probabilities. Our approaches will help managers to better understand consumer online search and buying behavior over time and to allocate financial spending more efficiently across multiple types of online advertising.