Much of the Internet economy relies on online advertising for monetizing digital content: Users are expected to accept the presence of online advertisements in exchange for content being free. However, online advertisements have become a serious problem for many Internet users: while some are merely annoyed by the incessant display of distracting ads cluttering Web pages, others are highly concerned about the privacy implications – as ad providers typically track users’ behavior for ad targeting purposes. Similarly, security problems related to technologies and practices employed for online advertisement have frustrated many users. Consequently, a number of software solutions have emerged that block online ads from being downloaded and displayed on users’ screens as they browse the Web. We focus on these advertisement avoidance technologies for online content and their economic ramifications for the monetization of websites. More specifically, our work addresses the interplay between users’ attempts to avoid commercial messages and content providers’ design of countermeasures. Our investigation is substantiated by the development of a game-theoretic model that serves as a framework usable by content providers to ponder their options to mitigate the consequences of ad avoidance techniques. We complement our analytical approach with simulation results, addressing different assumptions about user heterogeneity. Our findings show that publishers who treat each user individually, and strategically deploy fee-financed or ad-financed monetization strategy, obtain higher revenues, compared to deploying one monetization strategy across all users. In addition, our analysis shows that understanding the distribution of users’ aversion to ads and valuation of the content is essential for publishers to make a well-informed decision.
All Science Journal Classification (ASJC) codes
- Computer Science(all)