How Do Digital Advertising Auctions Impact Product Prices?

TL;DR

The paper develops a monopolist digital platform model where data-enabled matching and cross-channel competition interact. It compares data-augmented auctions to managed campaigns, showing the platform-optimal mechanism is a managed campaign that conditions on-platform prices on off-platform prices, yielding efficient on-platform allocations but higher off-platform prices. It derives that posted off-platform prices rise with the share of on-platform shoppers and that pass-through of online-ad costs to off-platform prices can exceed the number of advertisers, depending on the regime. It discusses policy interventions—competition management and cohort privacy—that shift prices and welfare in predictable ways, especially as the number of advertisers grows.

Abstract

We present a model of digital advertising with three key features: (i) advertisers can reach consumers on and off a platform, (ii) additional data enhances the value of advertiser-consumer matches, and (iii) bidding follows auction-like mechanisms. We contrast data-augmented auctions, which leverage the platform's data advantage to improve match quality, and managed campaign mechanisms that automate match formation and price-setting. The platform-optimal mechanism is a managed campaign that conditions on-platform prices for sponsored products on the off-platform prices set by all advertisers. This mechanism yields the efficient on-platform allocation but inefficient off-platform allocations due to high product prices; it attains the vertical integration profit for the platform and advertisers; and it increases off-platform product prices and decreases consumer surplus, relative to data-augmented auctions.

Figures (8)

• Figure 1: Model Depiction
• Figure 2: Posted prices as a function of $\lambda$. Results are plotted for $J= 3 ,5, 7$.
• Figure 3: Consumer surplus, firm profit, platform revenue, and welfare with data-augmented bidding as a function of the share of consumers on the platform, $\lambda$. Results are plotted for $J = 3, 5, 7$.
• Figure 4: Managed campaign consumer surplus and competition. Distribution of consumer values $F$ is uniform on $[0,1]$, plotted for varying $\lambda$ with $J = 1, 3, 5$.
• Figure 5: Platform revenue with consumer values distributed as $F(v) = v^{3/4}$. With $J=2$, $F^{J-1}$ is concave, and with $J=3$, $F^{J-1}$ is convex. In the first figure for $J=2$, the platform revenue from the independent managed campaign lies in between the sophisticated managed campaign revenue and bidding with participation fees. In the second figure, the relative ordering of bidding and independent campaigns are switched.

Theorems & Definitions (29)

• Proposition 1: Efficient Bidding Outcome
• Theorem 1: Symmetric Equilibrium
• Proposition 2: Posted Prices and Welfare Effects
• Definition 1: Best-Value Pricing
• Definition 2: Efficient Steering
• Theorem 2: Best-Value Pricing Managed Campaign Equilibrium
• Theorem 3: Optimal Managed Campaign
• Theorem 4: Welfare and Posted Price Comparison
• Proposition 3: Advertising Mechanism Pass Through
• Theorem 5: Independent Managed Campaigns