Strategic Challenges in the Digital Advertising Ecosystem




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This dissertation addresses three issues faced by different stakeholders in the digital advertising ecosystem. In the first chapter, we analyze a problem faced by Mobile-Promotion platforms, which enable advertisers (individual users or businesses) to directly launch their personalized mobile advertising campaigns. These platforms contract with advertisers to provide a certain number of impressions on mobile apps in their desired sets of geographic locations (usually cities or zip codes) within their desired time durations (for example, a month); the execution of each such contract is referred to as a campaign. To fulfill the demands of the campaigns, the platform bids in real-time at an ad exchange to win mobile impressions arising over the desired sets of locations of the campaigns and then allocates the acquired impressions among the ongoing campaigns. The core features that characterize this procurement problem – supply is uncertain, supply cannot be inventoried, and there are demand-side commitments to be met – are applicable to a variety of other business settings as well. Our analysis in this paper offers near-optimal policies for a static model representing a subscription-based setting, where customers provide information of their campaigns in advance to the platform. In the second chapter, we generalize our analysis of the first chapter and consider a dynamic model of campaign arrivals. The dynamic model represents a setting where campaigns arrive randomly and the platform reacts to these arrivals in real time; for this model, our rolling-horizon policy periodically re-computes the platform’s procurement (or bidding) and allocation decisions. We establish performance bounds on our policies for both models and demonstrate the attractiveness of these bounds on real data. By isolating important structural features of a given set of campaigns, we discuss practical implementation issues and offer procurement-policy recommendations for a variety of settings based on these features. In the third chapter, we consider an ongoing issue of ad-blocking and analyze its impact on websites and consumers. We also propose strategies and insights that websites can use to react to ad-block users. We show that the website can increase its revenue by discriminating between regular and ad-block users via the ad-intensities shown to them. More interestingly, we find that the discriminatory power bestowed on the website by ad-blockers can also benefit its users when their outside option is not very attractive. Thus, the advent of ad-blockers can lead to a win-win for both the website and its users. Finally, we propose a superior selective-gating strategy in which only a fraction of ad-block users are gated. We establish the robustness of our conclusions under several enhancements to our base setting: (a) heterogenous profitabilities from regular users and ad-block users, (b) endogenous adoption of ad-blockers, (c) the presence of a subscription option, and (d) negative externality due to increased traffic. Our analysis ends with recommendations for three stakeholders in this problem, namely, publishers, web-browser developers, and policy makers.


Winner of the 2019 Best Dissertation prize in the Naveen Jindal School of Management


Internet advertising, Rational expectations (Economic theory), Revenue management, Inventory control, Advertising campaigns, Advertising—Research