Estimating the incremental effects of interactions for marketing attribution

Abstract

Assigning credit to different marketing activities has long been an important but challenging goal for a marketer. With the advent of digital marketing, the marketer can now potentially record each interaction with a prospective customer. With this development it is possible to measure and assign credit for each marketing interaction. We propose an econometric model to estimate the true incremental number of purchases that can be attributed to a given marketing channel. We extend our model to attribute credit for revenue realization. We also propose an approach to automatically identify audience segments where attribution models differ. We build and test our model on a real world data set belonging to a travel and experience industry organization's web data. The results show that our approach improves upon industry standard rule-based approaches, by correcting for the biases inherent to these model.