The digital display advertising industry is phasing out third party cookies due to growing concerns about data privacy, fundamentally altering how advertisers target and reach consumers. This paper investigates the potential impact of this shift on advertisers and platforms, focusing on the retargeting strategy, where advertisers reach consumers who have previously visited their website. Utilizing a dataset of 1.4 billion auctions paired with an experiment varying bids of live campaigns, I estimate a structural model of endogenous advertiser entry in the online advertising industry. The model disentangles two opposing forces influencing winning bids when third-party retargeting cookies are removed: the matching effect, where the inability to precisely identify and reach advertisers' most valued audiences decreases their bids, and the competition effect, where advertisers face tougher competition in auctions after adjusting their targeting strategies toward broader audiences, increasing the winning bids. Counterfactual simulations reveal that removing third-party cookies would decrease total ad spend by 60%, with an 84% reduction being due to lower match values, partially offset by a 24% increase arising from intensified competition.

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 0.23%, five times higher than the true median CTR of 0.045%.

We consider how pooling advertiser information remediates this problem. Specifically, we show that ads with similar visual elements garner similar CTRS, enabling 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 $2,756 (a 15.5% increase) and a median publisher revenue gain of $9,618 (a 63.9% increase).