Sharing Audience Data: Strategic Participation in Behavioral Advertising Networks

Steven Schmeiser

doi:10.1007/s11151-017-9605-9

Review of Industrial Organization

May 2018, Volume 52, Issue 3, pp 429–450 | Cite as

Sharing Audience Data: Strategic Participation in Behavioral Advertising Networks

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Steven Schmeiser

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First Online: 24 November 2017

Abstract

I consider the incentives of special interest websites to participate in behavioral advertising intermediaries. Participation in the intermediary reveals valuable audience data and allows the intermediary to use those data to target the site’s audience on general interest websites—thus expanding the supply of impressions and decreasing average revenue per impression. I explore monopoly and duopoly settings and highlight the trade-off between sharing audience data and displaying higher-value ads, as well as the strategic interaction between sites serving the same advertising market. The model generates empirical predictions about the choice of intermediary technologies within advertising markets. I also find that higher concentration among special interest websites benefits consumer privacy.

Keywords

Online advertising Online privacy Behavioral advertising

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Notes

Acknowledgements

This paper benefited greatly from discussions with Lou Silversin and comments by the editor and anonymous referees.

Appendix 1: Endogenous P

Here, I examine the behavioral intermediary’s choice of P. As discussed in the main text, P accounts for two things: the premium that is generated by the behavioral intermediary’s additional information about a consumer that a contextual intermediary cannot obtain, and any difference in revenue shares between behavioral and contextual intermediaries. The latter effect is a direct choice of the behavioral intermediary, while the former is driven by how additional consumer information drives advertising demand. To isolate the behavioral intermediary’s choice of revenue sharing, I let $P=\delta p$ where $\delta $ is the fraction of revenue that the intermediary passes on to the website, and p is the premium that is generated by extra behavioral data.

Consider an advertising market with a monopoly special interest site that chooses the behavioral intermediary: The total revenue that is generated in the market is given by $pV(N,\alpha +\gamma ;\lambda )$. $\delta $ of this total revenue goes to the publisher, and the ad intermediary retains $(1-\delta )pV(N,\alpha +\gamma ;\lambda )$.

The behavioral intermediary chooses $\delta $. Increasing $\delta $ decreases the amount of revenue that the intermediary keeps. However, increasing $\delta $ also attracts marginal advertisers that switch from contextual to behavioral intermediaries as P increases. Recall that the cutoff premium for a monopoly special interest site is given by $P_M$ in Eq. 6 and $P_M$ is strictly decreasing in $\lambda $. Therefore we can invert the relationship and for any P obtain $\lambda (P)$: the minimum $\lambda $ that will induce transactions with the behavioral intermediary given P. Suppose that advertising markets are indexed by their $\lambda $ and that $\lambda $ is distributed according to $F(\cdot )$ with density function $f(\cdot )$. For expositional clarity, I assume that $N,\alpha $, and $\gamma $ are the same across advertising markets. The optimal $\delta $ is then given by the solution to the behavioral intermediary’s problem

$$\begin{aligned} \max _{\delta \in [0,1]} \int _{\lambda (\delta p)}^1 p(1-\delta )V(N,\alpha +\gamma ;\lambda )f(\lambda )d\lambda . \end{aligned}$$

(12)

The marginal benefit of increasing $\delta $ is given by $-\lambda _\delta (\delta p)(1-\delta )p^2V(N,\alpha +\gamma ;\lambda )f(\lambda (\delta p))$. $\lambda _\delta (\delta p)$ is negative, which makes the marginal benefit positive. The marginal cost of increasing $\delta $ is given by $-\int _{\lambda (\delta p)}^1 pV(N,\alpha +\gamma ;\lambda )f(\lambda )d\lambda $.

Appendix 2: Proofs

Proof of Proposition 2

$$\begin{aligned} \frac{\partial P_M}{\partial \lambda }&= \frac{\alpha + \gamma }{\alpha } \times \frac{\alpha (e^{-(\alpha +\gamma )(1-\lambda )}-e^{-\alpha (1-\lambda )})+\gamma (e^{-(2\alpha +\gamma )(1-\lambda )}-e^{-(\alpha +\gamma )(1-\lambda )})}{\alpha (e^{-(\alpha +\gamma )(1-\gamma )})^2}\,<\, 0 \\ \frac{\partial P_M}{\partial \alpha }&= \frac{e^{-\alpha (2-\lambda )-\gamma } \left( -\gamma e^{\lambda (\alpha +\gamma )}-(e^{\alpha +\gamma \lambda } - e^{\alpha +\gamma })(\alpha (1-\lambda ) (\alpha +\gamma )+\gamma ) \right) -\gamma }{a^2 \left( e^{-(1-\lambda ) (\alpha +\gamma )}-1\right) ^2} \,<\, 0 \\ \frac{\partial P_M}{\partial \gamma }&= \frac{\left( 1-e^{a (\lambda -1)}\right) \left( e^{-(1-\lambda ) (\alpha +\gamma )} (-(\alpha +\gamma ) (1-\lambda )-1)+1\right) }{\alpha \left( e^{-(1-\lambda ) (\alpha +\gamma )}-1\right) ^2} \,>\, 0 \end{aligned}$$

Proof of Proposition 3

The following comparative statics show how V varies with the parameters of interest holding intermediary choice constant. If a change in the parameters results in a change in equilibrium intermediary, the results hold as revenue is the upper envelope of R(C) and R(B), which have the (weakly) the same sign slope in each of the parameters. As $\lambda $ increases, V also increases. As $\alpha $ increases, both $V(N,\alpha )$ and $\frac{\alpha }{\alpha +\gamma }V(N,\alpha +\gamma )$ increase. Last, as $\gamma $ increases, there is no effect on revenue if the site uses the contextual intermediary, and revenue is decreasing if it uses the behavioral intermediary, as $\frac{\alpha }{\alpha +\gamma }V(N,\alpha +\gamma )$ is decreasing in $\gamma $. Next, V is increasing in N. Results for P follow immediately from the definition of R(C) and R(B).

$$\begin{aligned} \frac{\partial }{\partial \lambda }V(N,\alpha ;\lambda )&= \frac{e^{1/N} \left( e^{-\alpha (1-\lambda )} \left( (\alpha N-1) e^{\lambda /N}-\alpha e^{1/N} N\right) +e^{\lambda /N}\right) }{N \left( e^{1/N}-e^{\lambda /N}\right) ^2}\,>\, 0\\ \frac{\partial }{\partial \alpha }V(N,\alpha )&= (1-\lambda )\frac{e^{-\alpha (1-\lambda )+1/N}}{e^{1/N}-e^{\lambda /N}}\,>\,0 \\ \frac{\partial }{\partial \alpha }\left( \frac{\alpha }{\alpha +\gamma }V(N,\alpha +\gamma )\right)&= \frac{e^{1/N}\left( \gamma -e^{-(\alpha +\gamma )(1-\lambda )}\left( \gamma -\alpha (\alpha +\gamma )(1-\gamma )\right) \right) }{(e^{1/N}-e^{\lambda /N})(\alpha +\gamma )^2}\,>\,0 \\ \frac{\partial }{\partial \gamma }\left( \frac{\alpha }{\alpha +\gamma }V(N,\alpha +\gamma )\right)&= -\frac{\alpha e^{1/N} \left( e^{-(1-\lambda ) (\alpha +\gamma )} (1-\lambda (\alpha +\gamma )+\alpha +\gamma +1)\right) }{(\alpha +\gamma )^2 \left( e^{1/N}-e^{\lambda /N}\right) }\,<\,0 \\ \frac{\partial }{\partial N}V(N,\alpha )&= \frac{(1-\lambda ) \left( 1-e^{-\alpha (1-\lambda )}\right) e^{(1+\lambda )/N}}{N^2 \left( e^{1/N}-e^{\lambda /N}\right) ^2} \,>\, 0 \end{aligned}$$

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Steven Schmeiser
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1.Economics DepartmentMount Holyoke CollegeSouth HadleyUSA

Sharing Audience Data: Strategic Participation in Behavioral Advertising Networks

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