Beyond the Last Touch: Attribution in Online Advertising

Abstract

Online advertisers often utilize multiple publishers to deliver ads to multi-homing consumers. These ads often generate externalities and their exposure is uncertain, which impacts advertising effectiveness across publishers. We analytically analyze the inefficiencies created by externalities and uncertainty when information is symmetric between advertisers and publishers, in contrast to most previous research that assumes information asymmetry. Although these inefficiencies cannot be resolved through publisher side actions, attribution methods that measure the campaign uncertainty can serve as an alternative solution to help advertisers adjust their strategies. Attribution creates a virtual competition between publishers, resulting in a team compensation problem. The equilibrium may potentially increase the aggressiveness of advertiser bidding leading to increased advertiser profits. The popular last-touch method is shown to over-incentivize ad exposures, often resulting in lowering advertiser profits. The Shapley value achieves an increase in profits compared to last-touch. Popular publishers and those that appear early in the conversion funnel benefit the most from advertisers using last-touch attribution. The increase in advertiser profits come at the expense of total publisher profits and often results in decreased ad allocation efficiency. We also find that the prices paid in the market will decrease when more sophisticated attribution methods are adopted.

Inefficiencies in Digital Advertising Markets

“… 6 See Abhishek, Despotakis, and Ravi (2017) and Berman (2018) for theoretical analyses of multitouch attribution. …”

Attribution modelling in an omni-channel environment – new requirements and specifications from a practical perspective

“…Archak et al (2012) focus on positive spill-over effects. Furthermore, the performance of different advertisers is analysed as well (Berman, 2015). The interactive effects of online and offline activities and their interaction (Naik and Peters, 2009;Wiesel et al, 2011) are well observed and studied.…”

Delusion in Attribution: Caveats in Using Attribution for Multimedia Budget Allocation

“…For probability models that are typically nonlinear, this lift will be different than when the exposure order is reversed. If we only knew that a consumer was exposed to media 1 and 2 and did not know the order, we would have to cycle through all possible sequences (2 in this case) and assign attribution based on the Shapley (1953) value, as explained in Berman (2017) and Li and Kannan (2014).…”

“… 3 Note that computing the weights as ratios of increments is similar in spirit to the Shapley Value used in attribution literature (Berman 2017; Li and Kannan 2014). The difference is that we do not have to use permutations over different orders of touchpoints because we observe the exact order of touchpoints. …”