Abstract

Advancements in targeting technology have allowed firms to engage in more precise targeting based on several aspects of consumers' preferences. Exposed to more targeted ads, consumers are becoming increasingly aware of being targeted and respond accordingly. This paper provides a theoretical analysis of multidimensional targeting under which consumers can draw inferences about multiple components of their utility from the advertised product. We show that the firm can be worse off under multidimensional targeting than under single-dimensional targeting, in which the firm targets consumers based only on a single component of their utility. This is because, with multidimensional targeting, targeted consumers may face greater uncertainty about on which specific dimension(s) they can expect to enjoy the advertised product. Therefore, they may be less willing to exert a costly effort of clicking the ad and making a purchase decision. When this result holds, the firm may want to adopt a single-dimensional targeting strategy. However, we show that the firm cannot credibly commit to such a strategy once given access to multiple dimensions of customer data. Interestingly, a higher unit cost of advertising can mitigate the firm's commitment problem for utilizing customer data and, thus, increase the firm's profit. Moreover, the firm can sometimes lower the price to recover some of, but not entirely offset, the drawbacks of multidimensional targeting. We discuss the implications of our results regarding the current practice of targeted advertising and data privacy protection policies.

Abstract

We explain the rapid and dramatic move from second-price to first-price auction format in the display advertising market to be a simple consequence of the move from the waterfalling mechanism employed by publishers for soliciting bids in a pre-ordered cascade over exchanges, to an alternate header bidding strategy that broadcasts the request for bid to all exchanges. First, we argue that the move by the publishers from waterfalling to header bidding was a revenue improving move for publishers in the old regime when exchanges employed second-price auctions. Given the publisher move to header bidding, we show that exchanges move from second-price to first-price auctions to increase their expected clearing prices. Interestingly, when all exchanges move to first-price auctions, each exchange faces stronger competition from other exchanges and some exchanges may end up with lower revenue than when all exchanges use second-price auctions; yet, all exchanges move to first-price auctions in the unique equilibrium of the game. We show that the new regime commoditizes the exchanges' offerings and drives their buyer-side fees to zero in equilibrium. Furthermore, it allows the publishers to achieve the revenue of the optimal mechanism despite not having direct access to the advertisers.

Abstract

Although online advertising is the lifeline of many internet content platforms, the usage of ad blockers has surged in recent years, presenting a challenge to platforms dependent on ad revenue. Using a simple analytical model with two competing platforms, we show that the presence of ad blockers can actually benefit platforms. In particular, there are conditions under which the optimal equilibrium strategy for the platforms is to allow the use of ad blockers (rather than using an ad-block wall or charging a fee for viewing ad-free content). The key insight is that allowing ad blockers serves to differentiate platform users based on their disutility to viewing ads. This allows platforms to increase their ad intensity on those that do not use the ad blockers and achieve higher returns than in a world without ad blockers. We show robustness of these results when we allow a larger combination of platform strategies, as well as by explaining how ad white-listing schemes offered by modern ad blockers can add value. Our study provides general guidelines for what strategy a platform should follow based on the heterogeneity in the ad sensitivity of their user base.

Abstract

We examine the effect of the presence of expert buyers on other buyers, the platform, and the sellers in online markets. We model buyer expertise as the ability to accurately predict the quality, or condition, of an item, modeled as its common value. We show that nonexperts may bid more aggressively, even above their expected valuation, to compensate for their lack of information. As a consequence, we obtain two interesting implications. First, auctions with a “hard close” may generate higher revenue than those with a “soft close”. Second, contrary to the linkage principle, an auction platform may obtain a higher revenue by hiding the item's common-value information from the buyers. We also consider markets where both auctions and posted prices are available and show that the presence of experts allows the sellers of high quality items to signal their quality by choosing to sell via auctions.

Abstract

In this paper, we study the problem of attributing credit for customer acquisition to different components of a digital marketing campaign using an analytical model. We investigate attribution contracts through which an advertiser tries to incentivize two publishers that affect customer acquisition. We situate such contracts in a two-stage marketing funnel, where the publishers should coordinate their efforts to drive conversions.

First, we analyze the popular class of multi-touch contracts where the principal splits the attribution among publishers using fixed weights depending on their position. Our first result shows the following counterintuitive property of optimal multi-touch contracts: higher credit is given to the portion of the funnel where the existing baseline conversion rate is higher. Next, we show that social welfare maximizing contracts can sometimes have even higher conversion rate than optimal multi-touch contracts, highlighting a prisoners' dilemma effect in the equilibrium for the multi-touch contract. While multi-touch attribution is not globally optimal, there are linear contracts that “coordinate the funnel” to achieve optimal revenue. However, such optimal-revenue contracts require knowledge of the baseline conversion rates by the principal. When this information is not available, we propose a new class of ‘reinforcement’ contracts and show that for a large range of model parameters these contracts yield better revenue than multi-touch.

Abstract

Behavioral advertising targets consumers based on their personal characteristics, while contextual advertising is based on the content consumers are currently viewing. Nowadays, many websites employ a combination of the two, known as microtargeting, to display more tailored ads to consumers. Although it is debated whether using behavioral data benefits or harms publishers' revenues, the prevailing belief has been that more personalized ads always yield higher conversion rates. However, recent empirical evidence indicates that less targeted ads can sometimes achieve equal or even greater conversion rates.

We introduce a game-theoretic model to investigate this phenomenon and explain why disabling microtargeted behavioral advertising can sometimes result in increased conversion rates and publisher revenue simultaneously. We link this outcome to the information asymmetry present in the online advertising market. Allowing third-party cookies on a website enables some advertisers to access behavioral data from large ad exchanges, a privilege not universally available. Moreover, not all advertisers have the advanced tools necessary to leverage this additional information effectively. We show that such information asymmetry can reduce market efficiency. Surprisingly, disabling microtargeting can create a more efficient market by providing equal opportunities to all advertisers, despite them being less informed.