Externalities in online advertising (original) (raw)

Auctions for online ad space among advertisers sensitive to both views and clicks

Electronic Commerce Research, 2017

Advertisement in dedicated webpage spaces or in search engines sponsored slots is usually sold using auctions, with a payment rule that is either per impression or per click. But advertisers can be both sensitive to being viewed (brand awareness effect) and being clicked (conversion into sales). In this paper, we generalize the auction mechanism by including both pricing components: the advertisers are charged when their ad is displayed, and pay an additional price if the ad is clicked. Applying the results for Vickrey-Clarke-Groves (VCG) auctions, we show how to compute payments to ensure incentive compatibility from advertisers as well as maximize the total value extracted from the advertisement slot(s). We provide tight upper bounds for the loss of efficiency due to applying only pay-per-click (or pay-per-view) pricing instead of our scheme. Those bounds depend on the joint distribution of advertisement visibility and population likelihood to click on ads, and can help identify situations where our mechanism yields significant improvements. We also describe how the commonly used generalized second price (GSP) auction can be extended to this context.

Characterizing optimal adword auctions

Arxiv preprint cs/0611063, 2006

We present a number of models for the adword auctions used for pricing advertising slots on search engines such as Google, Yahoo! etc. We begin with a general problem formulation which allows the privately known valuation per click to be a function of both the identity of the ...

Ad Auctions with Data

Lecture Notes in Computer Science, 2012

The holy grail of online advertising is to target users with ads matched to their needs with such precision that the users respond to the ads, thereby increasing both advertisers' and users' value. The current approach to this challenge utilizes information about the users: their gender, their location, the websites they have visited before, and so on. Incorporating this data in ad auctions poses an economic challenge: can this be done in a way that the auctioneer's revenue does not decrease (at least on average)? This is the problem we study in this paper. Our main result is that in Myerson's optimal mechanism, additional data leads to additional revenue. However in simpler auctions, namely the second price auction with reserve prices, there are instances in which additional data decreases the revenue, albeit by only a small constant factor.

Selling Banner Ads: Online Algorithms with Buyback

2008

We initiate the study of online pricing problems in markets with "buyback," i.e., markets in which prior allocation decisions can be revoked, but at a cost. In our model, a seller receives requests online and chooses which requests to accept, subject to constraints on the subsets of requests which may be accepted simultaneously. A request, once accepted, can be canceled

Auctions and mechanisms in keyword-based advertising

2011

Today most of search engines' profits come from advertising, and in particular from sponsored search. In sponsored search, advertisement slots next to search results are sold. When a query is made, besides processing the query results themselves, the search engine selects ads relevant to that query. Two of the main characteristics of this form of advertising are that advertisers are billed only when a click on their ad is made, and that prices are computed using an auction. In this thesis we consider some generalizations of the sponsored search auction model presented in the literature. In particular we account for the fact that the search engines have a great control over the order in which advertisers are ranked. In fact search engines assign quality scores to each advertiser, and, prior to sorting, scale all bids by such factors. We show how this changes the main properties of the equilibria in these auctions, and that in particular, the efficiency directly depends on how the...

Optimising trade-offs among stakeholders in ad auctions

Proceedings of the fifteenth ACM conference on Economics and computation - EC '14, 2014

We examine trade-offs among stakeholders in ad auctions. Our metrics are the revenue for the utility of the auctioneer, the number of clicks for the utility of the users and the welfare for the utility of the advertisers. We show how to optimize linear combinations of the stakeholder utilities, showing that these can be tackled through a GSP auction with a per-click reserve price. We then examine constrained optimization of stakeholder utilities.

Optimal Auctions Capturing Constraints in Sponsored Search

Lecture Notes in Computer Science, 2009

Most sponsored search auctions use the Generalized Second Price (GSP) rule. Given the GSP rule, they try to give an optimal allocation, an easy task when the only need is to allocate ads to slots. However, when other practical conditions must be fulfilled -such as budget constraints, exploration of the performance of new ads, etc.-optimal allocations are hard to obtain. We provide a method to optimally allocate ads to slots under the practical conditions mentioned above. Our auctions are stochastic, and can be applied in tandem with different pricing rules, among which we highlight two: an intuitive generalization of GSP and VCG payments.

Methodology for Designing Reasonably Expressive Mechanisms with Application to Ad Auctions

2018

Mechanisms (especially on the Internet) have begun allowing people or organizations to express richer preferences in order to provide for greater levels of overall satisfaction. In this paper, we develop an operational methodology for quantifying the expected gains in economic efficiency associated with different forms of expressiveness. We begin by proving that the sponsored search mechanism (GSP) used by Google, Yahoo!, MSN, etc. can be arbitrarily inefficient. We then experimentally compare its efficiency to a slightly more expressive variant (PGSP), which solicits an extra bid for a premium class of positions. We generate random preference distributions based on published industry knowledge. We determine ideal strategies for the agents using a custom tree search technique, and we also benchmark using straightforward heuristic bidding strategies. The GSP's efficiency loss is greatest in the practical case where some advertisers ("brand advertisers") prefer top posit...

On Revenue Maximization in Second-Price Ad Auctions

Lecture Notes in Computer Science, 2009

Most recent papers addressing the algorithmic problem of allocating advertisement space for keywords in sponsored search auctions assume that pricing is done via a first-price auction, which does not realistically model the Generalized Second Price (GSP) auction used in practice. Towards the goal of more realistically modeling these auctions, we introduce the Second-Price Ad Auctions problem, in which bidders' payments are determined by the GSP mechanism. We show that the complexity of the Second-Price Ad Auctions problem is quite different than that of the more studied First-Price Ad Auctions problem. First, unlike the first-price variant, for which small constant-factor approximations are known, it is NP-hard to approximate the Second-Price Ad Auctions problem to any non-trivial factor. Second, this discrepancy extends even to the 0-1 special case that we call the Second-Price Matching problem (2PM). In particular, offline 2PM is APX-hard, and for online 2PM there is no deterministic algorithm achieving a non-trivial competitive ratio and no randomized algorithm achieving a competitive ratio better than 2. This stands in contrast to the results for the analogous special case in the first-price model, the standard bipartite matching problem, which is solvable in polynomial time and which has deterministic and randomized online algorithms achieving better competitive ratios. On the positive side, we provide a 2-approximation for offline 2PM and a 5.083-competitive randomized algorithm for online 2PM. The latter result makes use of a new generalization of a classic result on the performance of the "Ranking" algorithm for online bipartite matching. * azar@tau.ac.il, Microsoft Research, Redmond and Tel-Aviv University. † birnbaum@cs.washington.edu, University of Washington.