A Dynamic Model of Sponsored Search Advertising

Abstract

Sponsored search advertising is ascendant--Forrester Research reports expenditures rose 28% in 2007 to $8.1 billion and will continue to rise at a 26% compound annual growth rate [VanBoskirk, S. 2007. U.S. interactive marketing forecast, 2007 to 2012. Forrester Research (October 10)], approaching half the level of television advertising and making sponsored search one of the major advertising trends to affect the marketing landscape. Yet little empirical research exists to explore how the interaction of various agents (searchers, advertisers, and the search engine) in keyword markets affects consumer welfare and firm profits. The dynamic structural model we propose serves as a foundation to explore these outcomes. We fit this model to a proprietary data set provided by an anonymous search engine. These data include consumer search and clicking behavior, advertiser bidding behavior, and search engine information such as keyword pricing and website design. With respect to advertisers, we find evidence of dynamic bidding behavior. Advertiser value for clicks on their links averages about 26 cents. Given the typical $22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.2%, well within common estimates of 1%-2% [Narcisse, E. 2007. Magid: Casual free to pay conversion rate too low. GameDaily.com (September 20)]. With respect to consumers, we find that frequent clickers place a greater emphasis on the position of the sponsored advertising link. We further find that about 10% of consumers do 90% of the clicks. We then conduct several policy simulations to illustrate the effects of changes in search engine policy. First, we find the search engine obtains revenue gains of 1% by sharing individual-level information with advertisers and enabling them to vary their bids by consumer segment. This also improves advertiser revenue by 6% and consumer welfare by 1.6%. Second, we find that a switch from a first- to second-price auction results in truth telling (advertiser bids rise to advertiser valuations). However, the second-price auction has little impact on search engine profits. Third, consumer search tools lead to a platform revenue increase of 2.9% and an increase of consumer welfare by 3.8%. However, these tools, by reducing advertising exposures, lower advertiser profits by 2.1%.sponsored search advertising, two-sided market, dynamic game, structural models, empirical IO, customization, auctions

Aggregation Bias in Sponsored Search Data: The Curse and the Cure

“…A myriad of researchers have turned to SSA data to uncover new insights about consumer search (Ghose and Yang, 2009;Rutz and Bucklin, 2011), choice and related purchasing behaviors (Jeziorski and Segal, 2009;Yang and Ghose, 2010;Agarwal et al, 2011) and advertiser/search engine strategies (Animesh et al, 2009;Yao and Mela, 2011;Rutz et al, 2012). Many of these papers have used random utility models to study the effect of ad position, keyword length, presence or absence of brand name, etc.…”

Measurement and analysis of OSN ad auctions

“…Researchers have studied existing Web-search-based advertising networks (e.g., Google's ad market) [15,32,39], prediction markets run by Google, Ford and others [5], and have used "estimated prices" [17] from Google's Traffic Estimator Tool [23] (similar to our suggested bids) as a mechanism for understanding the network. Noti et al [29] demonstrated that bidders with some explicit knowledge during an initial learning phase can bid with better valuations than those without such knowledge; this implies that suggested bids can provide useful guidance for bidders.…”

“…Using a sample from a week's worth of data across all keywords on Bing, other work [4] showed 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. There has also been much work proposing new models for conducting online auctions [19,20,28,39].…”

“…Many of these advertising services are implemented as auctions, with individual advertisers bidding on specific keywords, pages, or search terms. These auctions are extremely popular with advertisers (Google alone earned over $50 billion in advertising revenue in 2013 [21]) and are well-studied in the research literature [15,33,37,39].…”

Online Advertising

“…One exception is Yao and Mela (2010), who make the problem tractable by looking at a hybrid auction mechanism developed for an individual website and suggest that dynamics are important in understanding firm bidding behavior. Another question addressed by academics is how the position of a search ad affects its performance.…”