Advertiser Learning in Direct Advertising Markets

TL;DR

This paper investigates how advertisers learn about publisher site efficacy in direct advertising markets, revealing they overestimate site performance and benefit significantly from information pooling by ad networks.

Contribution

It models advertiser learning in direct markets and demonstrates how pooling information improves advertiser efficiency and publisher revenue.

Findings

Advertisers overestimate site CTR by a factor of four.

Pooling information leads to an 18.3% increase in advertiser welfare.

Pooling results in a 77.7% increase in publisher revenue.

Abstract

Direct buy advertisers procure advertising inventory at fixed rates from publishers and ad networks. Such advertisers face the complex task of choosing ads amongst myriad new publisher sites. We offer evidence that advertisers do not excel at making these choices. Instead, they try many sites before settling on a favored set, consistent with advertiser learning. We subsequently model advertiser demand for publisher inventory wherein advertisers learn about advertising efficacy across publishers' sites. Results suggest that advertisers spend considerable resources advertising on sites they eventually abandon -- in part because their prior beliefs about advertising efficacy on those sites are too optimistic. The median advertiser's expected CTR at a new site is 0.177\%, four times higher than the true median CTR of 0.045\%. We consider how an ad network's pooling of advertiser information remediates this problem. As ads with similar visual elements garner similar CTRs, the network's pooling of information enables advertisers to better predict ad performance at new sites. Counterfactual analyses indicate that gains from pooling advertiser information are substantial: over six months, we estimate a median advertiser welfare gain of \$3,621 (an 18.3\% increase) and a median revenue gain of \$13,558 (a 77.7\% increase) among the 20 largest publishers.