Advances in information technologies have given firms unprecedented opportunities to collect, store, and process consumer data. The rise of data analytics has profoundly reshaped competitive landscapes, in which firms can adjust their competitive strategies based on the insights generated from the data. By bringing together data from across the business, firms can leverage data analytics to obtain real-time insights into sales, marketing, product development, and other processes. For instance, enabled by the deluge of online data on consumer characteristics, digital targeted advertising, and data-drive pricing have become more prevalent. Empowered by consumer data analytics, firms can deliver their ads to target relevant consumer segments that are interested in their products and set different prices for different consumers (i.e., personalized pricing). Although data analytics has long been regarded as a critical asset to gain competitive advantages, how data analytics affect the strategic interactions and competitive strategies of different stakeholders in the process are unclear. Given the growing importance of data analytics, this thesis aims to advance our understanding of the business value of data analytics and its impact on firms’ competition in the new digital ecosystems. The dissertation consists of three studies.

The first study, “Compete, Cooperate, or Coopete? The Strategic Role of Data Analytics in Targeted Advertising,” focuses on the impact of data analytics on the demand-side advertising market, i.e., the interactions among ad tech firms and between ad tech firms and advertisers. The rise of data analytics has changed the competitive landscape of the digital advertising industry. Many ad tech intermediaries have flooded the market to implement targeted advertising for advertisers. These ad tech intermediaries are known as data management platform (DMP), which performs data analytics to build target audience profiles, and demand-side platform (DSP), which provides ad delivery service ad for advertisers. To increase targeting capability, DSP firms have started to integrate data management platforms to become hybrid firms. This study builds an analytical model to examine the strategic interactions among the hybrid firm, the standalone DSP firm, and advertisers. We shed light on under what conditions the hybrid firm can leverage its data analytics capabilities to compete, cooperate, or coopete with the standalone DSP firm. We extend the main model to study the following issues: whether and when the hybrid firm should adopt discriminatory offering strategies on data analytics levels; the impact of a new DMP entrant in the market; and the potential change in competitive strategies if advertisers face a niche or mass market. We scrutinize the impact of data analytics on ad prices and advertiser surplus and discuss the theoretical and managerial implications.

The second study, “The Competitive Implications of Data Brokers on Publisher Competition,” examines the impact of a data broker and its data analytics service on downstream publisher competition. The proliferation of consumer data has spawned a new data broker industry that plays a pivotal role in the targeted advertising industry. Many publishers rely on data brokers to gain (i) individual insights drawn from their own data or (ii) collective insights drawn from their own data and those of their competitors to improve their targeting capabilities. The control of extensive data enables data brokers to dictate how to sell data insights to downstream publishers. Prior studies provide little insight into this strategic role of data brokers and its subsequent impact on downstream competition. To address this research gap, we build an analytical model to analyze the interactions between the data broker, two competing publishers, and advertisers. Our analysis yields several novel results. First, we show that the data broker may strategically sell collective insights exclusively to one publisher (exclusive selling) instead of selling to both publishers (non-exclusive selling). This result implies that adopting an exclusive strategy and making data insights more scarce for some firms may be better for the data broker. Second, we illustrate that the data broker can facilitate or inhibit publisher competition, depending on its interests in setting the data insights selling strategies. Finally, the data broker may induce both competition-mitigation and value-enhancing effects that affect the outcome of social welfare. Interestingly, while the data broker’s decision (i.e., exclusive selling) may lead to less competition in the market, social welfare can still improve because the collective insights generated by data brokers allow publishers to increase their targeting value to advertisers more efficiently.

The third study, “A Blessing or a Curse? The Implications of Data Analytics and Consumers’ Aversion on Personalized Pricing,” examines the interactive effects of consumer data analytics and consumers' negative reactions to personalized pricing. The availability of consumer data and advanced data analytics techniques (e.g., machine learning) has empowered firms to implement personalized pricing by learning individual consumer preferences. However, the use of data analytics for personalized pricing may create some negative feelings in consumers. Behavioral research and anecdotal evidence suggest that consumers exhibit aversion to such a pricing practice for various reasons, such as fear of price fraud, unfairness, privacy concerns, etc. However, little research has examined how consumers’ aversion, which can be seen as the byproduct of using data analytics, influences the impact of personalized pricing on firms and consumers in a competitive setting. Using a game-theoretic model, we investigate the effect of consumers’ aversion when competing firms use personalized pricing in a market where firms have differing extents of information access to different consumer segments (i.e., different consumer addressability). Interestingly, our result shows that personalized pricing does not necessarily hurt consumers even though we consider consumers are averse to it. Contrary to conventional wisdom, firms’ profits from conducting personalized pricing may increase with consumers’ aversion level, whereas consumer surplus decreases. The driving force is that a high aversion level enables firms to poach exclusive target consumers of the rival firm at a high uniform price. Finally, when consumers’ aversion is not very high, greater exclusive access to consumer data can work against the firms.

This dissertation makes several important contributions to both research and practice by examining the role of data analytics in the new digital era. First, the first study sheds light on the impact of data analytics on coopetition strategies among ad-tech firms in the demand-side advertising market. We show that the hybrid firm may leverage its data analytics capability to compete, cooperate or coopete with a standalone DSP firm. Our findings provide implications for ad firms to optimize their service offerings. Second, by examining the interactions among a data broker and two downstream competing publishers, our second study contributes to the literature stream that examines the effect of data analytics on competitive outcomes in the supply-side advertising market. Our study reveals an unintended consequence that a high data analytics capability may soften the market competition between publishers, thereby hurting the advertiser surplus. Finally, the third study contributes to the literature on personalized pricing by examining the impact of data analytics and consumers’ aversion on firms’ profitability and consumer surplus. We show that in the presence of consumers’ aversion, having more exclusive data access to consumer data may not benefit firms’ profits. Our findings provide implications for managers on using consumer data for personalized pricing and add to policy debates on how personalized pricing could affect consumer interests.