This paper summarizes the display advertising literature, organizing the content by the agents in the display advertising ecosystem, and proposes new research directions. In doing so, we take an interdisciplinary view, drawing connections among diverse streams of theoretical and empirical research in information systems, marketing, economics, operations, and computer science. By providing an integrated view of the display advertising ecosystem, we hope to bring attention to the outstanding research opportunities in this economically consequential and rapidly growing market.

This paper considers the monetization of online marketplaces. These platforms trade-off fees from advertising with commissions from product sales. While featuring advertised products can make search less efficient (lowering transaction commissions), it incentivizes sellers to compete for better placements via advertising (increasing advertising fees). We consider this trade-off by modeling both sides of the platform. On the demand side, we develop a joint model of browsing (impressions), clicking, and purchase. On the supply side, we consider sellers’ valuations and advertising competition under various fee structures (CPM, CPC, CPA) and ranking algorithms.
Using buyer, seller, and platform data from an online marketplace where advertising dollars affect the order of seller items listed, we explore various product ranking and ad pricing mechanisms. We find that sorting items below the fifth position by expected sales revenue while conducting a CPC auction in the top 5 positions yields the greatest improvement in profits (181%) because this approach balances the highest valuations from advertising in the top positions with the transaction revenues in the lower positions.

This paper considers how a publisher (e.g., Wall Street Journal) should set reserve prices for real-time bidding (RTB) auctions when selling display advertising impressions through ad exchanges, a $40 billion market and growing. Through a series of field experiments, we find that setting the reserve price increases publisher’s revenues by 32%, thereby affirming the importance of reserve price in maximizing publisher’s revenues from auctions. Further, we find that advertisers increase their bids in response to an experimental increase in reserve price, and show this behavior is consistent with the use of a minimum impression constraint to ensure advertising reach.
Based on this insight, we construct an advertiser bidding model and use it to infer the overall demand curve for advertising as a function of reserve prices. Using this demand model, we solve the publisher pricing problem. Incorporating the minimum impression constraint into the reserve price setting process yields a 50% increase over a solution that does not incorporate the constraint, and an additional increase in profits of nine percentage points.

A publisher in a display ad market sells its advertising inventory both via ad exchange auctions and via direct sales. Ad exchanges (e.g., DoubleClick Ad Exchange) are centralized marketplaces where publishers supply and advertisers demand display advertising impressions in real-time bidding (RTB) auctions. The direct sale involves the advance sale of a bundle of ad impressions directly to the advertiser at a fixed price. The important research questions are then i) how ad inventories should be priced (fixed price in the direct sale and the reserve price in the exchange channel), ii) how ad inventories should be allocated across distribution channels (direct or exchanges first), and iii) how much information should be revealed to advertisers about specific ad impressions.
Based on a two-stage game theory model, we show that the equilibrium channel choice depends on the coefficient of variation (CV) of the valuation distribution. Advertisers with higher CV prefer the exchange over the direct channel because the value of information revealed in the ad exchange (e.g., cookie) is high and they have a higher option value of waiting. We are extending this theoretical framework to an empirical context with the novel dataset collected both for the direct and the exchange channels from a large, premium publisher, ranked within U.S. top 10.

E-commerce platforms, including online marketplaces, often send out SMS messages for advertising and promotions. Using a large-scale dataset involving natural experiments, I find that SMS messages increase the average purchase amount significantly (i.e., short-term direct responses); but at the same time, they also increase consumers’ opting-out of marketing communications. I also find that less than 1% of consumers opt back in after making these opt-out decisions. Therefore, I examine the optimal SMS targeting strategy to maximize the long-term gain by considering both the rate of opt-out decisions and the effects on short-term direct responses.