| My dissertation examines novel interactions between consumers and advertisers enabled by Internet platforms with new targeting technologies in online advertising markets. As paid-search and display become two most prevalent forms of online advertising, this dissertation empirically investigates the consumer and advertiser interactions in these two online advertising markets.;In my first essay, I examine the determinant of competition and its impact on click-volume and cost-per-clicks in paid-search advertising. I regard each keyword as a market and measure the competition by the number of ads on the paid-search listings. I build an integrative model of the number entrant advertisers, the realized click-volume and cost-per-clicks of each entrant. The proposed model is applied to data of keywords associated with digital camera/video and accessories. Results indicate that the number of competing ads has a significant impact on baseline click-volume, decay factor, and value-per-click. These findings help search advertisers assess the impact of competition on their entry decisions and advertising profitability. The proposed framework can also provide profit implications to the search host regarding two polices: raising the decay factor by encouraging consumers to engage in more in-depth search/click-through, and providing coupons to advertisers.;As Internet advertising infomediaries now provide rich competition-related information, search advertisers are becoming more strategic in their keyword decisions. In the second essay, I explore whether positive or negative spillover effects occur in advertisers' keyword entry decisions, which lead to assimilation or differentiation in their keyword choices. I develop a model of advertisers' keyword decisions based on the incomplete-information and simultaneous-move game with two novel extensions: (i) I allow the strategic interactions to vary with advertisement positions to reflect consumers' top-down search pattern; and (ii) I infer potential entrants of a keyword by modeling the advertisers' keyword consideration process to capture their limited capacity in analyzing all existing keywords. Using a panel dataset of laptop-related keywords mainly used by 28 manufacturers, retailers, and comparison websites that advertise on Google, I find both assimilation and differentiation tendencies, which vary across firm types and the expected ranking of competing firms. A counterfactual simulation suggests that the more accurate competition information provided by infomediaries leads to a market-expansion effect.;Behavioral targeting, displaying personalized advertisements based on consumers' past online behaviors, has become a popular practice in the online advertising industry. Yet, empirical research on behavioral targeting remains relatively nascent. The final essay studies the impact of targeting level on three key players (users, advertisers, and the advertising host) in behaviorally targeted display advertising. The targeting level is defined as an inverse scale of the number of a consumer's recently activated interests used in the user-ad match. Intuitively, a high targeting level can increase the relevance of the served ads and consequently the users' click-through rate, but it might also evoke users' negative reactions by triggering privacy concerns and/or information satiation, lowering the click-through rate. Besides the mixed reaction from users, advertisers may also respond to a high targeting level by either raising bids in an anticipation of a higher value-per-click, or lowering bids due to the reduced competition as the result of a high targeting level. To understand the impact of the targeting level, I develop a model to simultaneously capture users' reactions to behaviorally targeted ads, the advertising host's decision on ad serving, and advertisers' bid decisions. I apply the model to a novel dataset obtained from a leading Internet advertising platform. Results suggest that although the advertisers' profits increase with the targeting level, both the consumers' click-through rate and the advertising host's revenue has an inverted-U relationship with the targeting level. |
