Measuring Display Advertising Response Using Observational Data : The Impact of Selection Biases

This paper studies the potential to better understand display advertising response using observational data from real-time bidding (RTB) exchanges. While non-random exposure to display ads creates endogeneity concerns, unique aspects of the RTB ecosystem allow for common sources of selection, targeting and browsing intensity, to be directly observed and measured. Including these measures in a standard advertising response model mitigates the associated biases. In a two-month panel detailing interactions between 29 campaigns and nearly one million users, we estimate that display advertising increased conversion probability by 127% and was positive and significant in 90% of campaigns. When selection controls are omitted, advertising response was overstated by more than five-fold. An advantage of the proposed approach is the ability to readily explore factors moderating response, and the paper concludes by examining two such features: conversion event and device type. By recovering accurate response estimates from available data, the approach allows advertisers to continuously monitor and improve display advertising effectiveness. Further, it provides an incentive aligning structure to mitigate pervasive agency problems between advertisers and ad-serving platforms