Working Paper: NBER ID: w19520
Authors: Randall Lewis; Justin M. Rao; David H. Reiley
Abstract: Online advertising offers unprecedented opportunities for measurement. A host of new metrics, clicks being the leading example, have become widespread in advertising science. New data and experimentation platforms open the door for firms and researchers to measure true causal effects of advertising on a variety of consumer behaviors, such as purchases. We dissect the new metrics and methods currently used by industry researchers, attacking the question, "How hard is it to reliably measure advertising effectiveness?" We outline the questions that we think can be answered by current data and methods, those that we believe will be in play within five years, and those that we believe could not be answered with arbitrarily large and detailed data. We pay close attention to the advances in computational advertising that are not only increasing the impact of advertising, but also usefully shifting the focus from "who to hit" to "what do I get."
Keywords: Advertising; Measurement; Causal Effects
JEL Codes: D47; L22; M37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| browsing behavior and purchase intent (M31) | estimates of advertising effectiveness (M37) |
| online ads (M37) | offline sales (L81) |
| display ads (M37) | keyword searches for the advertised brand (M37) |