Targeting Breadth in Behaviorally Targeted Display Advertising

Targeting breadth is defined as the number of consumer interests that an ad network uses to form the set of potential advertisers who compete for an ad impression to a consumer in the context of display advertising. Despite its relevance and importance to display ad platforms, the impact and optimal design of targeting breadth remain largely unexplored. We develop an integrated model of decisions made by consumers, advertisers, and the platform, where advertisers' bids are characterized by the mean field equilibrium. Using a proprietary dataset obtained from a large ad platform that comprises of 1.26 million ad impressions over 56,000 unique ads from 4,400 consumers over six weeks, we estimate model parameters, draw insights, and configure the optimal targeting breadth to improve the platform's ad revenue. Counterfactual experiments indicate that both click-through rate (CTR) and cost-per-click (CPC) first increase and then decrease, when a broader targeting is used in ad serving. The inverted-U shaped effect of targeting breadth on CTR is because a broader targeting tends to serve more ads that are less relevant to consumers (which decreases CTR), but also from different product categories and therefore alleviate the negative spillovers from the highly similar ads delivered simultaneously (which increases CTR). For CPC, the non-monotonic effect is driven by the increased competition (which increases CPC) and the lower match value of targeted audience (which decreases CPC) under a broader targeting. Moreover, the platform's revenue can be improved by 9.2% if optimizing the targeting breadth uniformly and by another increase of 6.8% if setting the targeting breadth individually