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dc.contributor.advisor Mela, Carl
dc.contributor.author Yao, Song
dc.date.accessioned 2009-05-01T18:19:24Z
dc.date.available 2009-05-01T18:19:24Z
dc.date.issued 2009
dc.identifier.uri http://hdl.handle.net/10161/1073
dc.description Dissertation
dc.description.abstract <p>Central to the explosive growth of the Internet has been the desire</p><p>of dispersed buyers and sellers to interact readily and in a manner</p><p>hitherto impossible. Underpinning these interactions, auction</p><p>pricing mechanisms have enabled Internet transactions in novel ways.</p><p>Despite this massive growth and new medium, empirical work in</p><p>marketing and economics on auction use in Internet contexts remains</p><p>relatively nascent. Accordingly, this dissertation investigates the</p><p>role of online auctions; it is composed of three essays.</p><p>The first essay, ``Online Auction Demand,'' investigates seller and</p><p>buyer interactions via online auction websites, such as eBay. Such</p><p>auction sites are among the earliest prominent transaction sites on</p><p>the Internet (eBay started in 1995, the same year Internet Explorer</p><p>was released) and helped pave the way for e-commerce. Hence, online</p><p>auction demand is the first topic considered in my dissertation. The</p><p>second essay, ``A Dynamic Model of Sponsored Search Advertising,''</p><p>investigates sponsored search advertising auctions, a novel approach</p><p>that allocates premium advertising space to advertisers at popular</p><p>websites, such as search engines. Because sponsored search</p><p>advertising targets buyers in active purchase states, such</p><p>advertising venues have grown very rapidly in recent years and have</p><p>become a highly topical research domain. These two essays form the</p><p>foundation of the empirical research in this dissertation. The third</p><p>essay, ``Sponsored Search Auctions: Research Opportunities in</p><p>Marketing,'' outlines areas of future inquiry that I intend to</p><p>pursue in my research.</p><p>Of note, the problems underpinning the two empirical essays exhibits</p><p>a common form, that of a two-sided network wherein two parties</p><p>interact on a common platform (Rochet and Tirole, 2006). Although</p><p>theoretical research on two-sided markets is abundant, this</p><p>dissertation focuses on their use in e-commerce and adopts an</p><p>empirical orientation. I assume an empirical orientation because I</p><p>seek to guide firm behavior with concrete policy recommendations and</p><p>offer new insights into the actual behavior of the agents who</p><p>interact in these contexts. Although the two empirical essays share</p><p>this common feature, they also exhibit notable differences,</p><p>including the nature of the auction mechanism itself, the</p><p>interactions between the agents, and the dynamic frame of the</p><p>problem, thus making the problems distinct. The following abstracts</p><p>for these two essays as well as the chapter that describes my future</p><p>research serve to summarize these contributions, commonalities and</p><p>differences.</p><p>Online Auction Demand</p><p>With $40B in annual gross merchandise volume, electronic auctions</p><p>comprise a substantial and growing sector of the retail economy. For</p><p>example, eBay alone generated a gross merchandise volume of $14.4B</p><p>during the fourth quarter of 2006. Concurrent with this growth has</p><p>been an attendant increase in empirical research on Internet</p><p>auctions. However, this literature focuses primarily on the bidder;</p><p>I extend this research to consider both seller and bidder behavior</p><p>in an integrated system within a two-sided network of the two</p><p>parties. This extension of the existing literature enables an</p><p>exploration of the implications of the auction house's marketing on</p><p>its revenues as well as the nature of bidder and seller interactions</p><p>on this platform. In the first essay, I use a unique data set of</p><p>Celtic coins online auctions. These data were obtained from an</p><p>anonymous firm and include complete bidding and listing histories.</p><p>In contrast, most existing research relies only on the observed</p><p>website bids. The complete bidding and listing histories provided by</p><p>the data afford additional information that illuminates the insights</p><p>into bidder and seller behavior such as bidder valuations and seller</p><p>costs.</p><p>Using these data from the ancient coins category, I estimate a</p><p>structural model that integrates both bidder and seller behavior.</p><p>Bidders choose coins and sellers list them to maximize their</p><p>respective profits. I then develop a Markov Chain Monte Carlo (MCMC)</p><p>estimation approach that enables me, via data augmentation, to infer</p><p>unobserved bidder and seller characteristics and to account for</p><p>heterogeneity in these characteristics. My findings indicate that:</p><p>i) bidder valuations are affected by item characteristics (e.g., the</p><p>attributes of the coin), seller (e.g. reputation), and auction</p><p>characteristics (e.g., the characteristics of the listing); ii)</p><p>bidder costs are affected by bidding behavior, such as the recency</p><p>of the last purchase and the number of concurrent auctions; and iii)</p><p>seller costs are affected by item characteristics and the number of</p><p>concurrent listings from the seller (because acquisition costs</p><p>evidence increasing marginal values).</p><p>Of special interest, the model enables me to compute fee</p><p>elasticities, even though no variation in historical fees exists in</p><p>these data. I compute fee elasticities by inferring the role of</p><p>seller costs in their historical listing decision and then imputing</p><p>how an increase in these costs (which arises from more fees) would</p><p>affect the seller's subsequent listing behavior. I find that these</p><p>implied commission elasticities exceed per-item fee elasticities</p><p>because commissions target high value sellers, and hence, commission</p><p>reductions enhance their listing likelihood. By targeting commission</p><p>reductions to high value sellers, auction house revenues can be</p><p>increased by 3.9%. Computing customer value, I find that attrition</p><p>of the largest seller would decrease fees paid to the auction house</p><p>by $97. Given that the seller paid $127 in fees, competition</p><p>offsets only 24% of the fees paid by the seller. In contrast,</p><p>competition largely in the form of other bidders offsets 81% of the</p><p>$26 loss from buyer attrition. In both events, the auction house</p><p>would overvalue its customers by neglecting the effects of</p><p>competition.</p><p>A Dynamic Model of Sponsored Search Advertising</p><p>Sponsored search advertising is ascendant. Jupiter Research reports</p><p>that expenditures rose 28% in 2007 to $8.9B and will continue to</p><p>rise at a 26% Compound Annual Growth Rate (CAGR), approaching half</p><p>the level of television advertising and making sponsored search</p><p>advertising one of the major advertising trends affecting the</p><p>marketing landscape. Although empirical studies of sponsored search</p><p>advertising are ascending, little research exists that explores how</p><p>the interactions of various agents (searchers,</p><p>advertisers, and the search engine) in keyword</p><p>markets affect searcher and advertiser behavior, welfare and search</p><p>engine profits. As in the first essay, sponsored search constitutes</p><p>a two-sided network. In this case, bidders (advertisers) and</p><p>searchers interact on a common platform, the search engine. The</p><p>bidder seeks to maximize profits, and the searcher seeks to maximize</p><p>utility.</p><p>The structural model I propose serves as a foundation to explore</p><p>these outcomes and, to my knowledge, is the first structural model</p><p>for keyword search. Not only does the model integrate the behavior</p><p>of advertisers and searchers, it also accounts for advertisers</p><p>competition in a dynamic setting. Prior theoretical research has</p><p>assumed a static orientation to the problem whereas prior empirical</p><p>research, although dynamic, has focused solely on estimating the</p><p>dynamic sales response to a single firm's keyword advertising</p><p>expenditures.</p><p>To estimate the proposed model, I have developed a two-step Bayesian</p><p>estimator for dynamic games. This approach does not rely on</p><p>asymptotics and also facilitates a more flexible model</p><p>specification.</p><p>I fit this model to a proprietary data set provided by an anonymous</p><p>search engine. These data include a complete history of consumer</p><p>search behavior from the site's web log files and a complete history</p><p>of advertiser bidding behavior across all advertisers. In addition,</p><p>the data include search engine information, such as keyword pricing</p><p>and website design.</p><p>With respect to advertisers, I find evidence of dynamic</p><p>bidding behavior. Advertiser valuation for clicks on their sponsored</p><p>links averages about $0.27. Given the typical $22 retail price of</p><p>the software products advertised on the considered search engine,</p><p>this figure implies a conversion rate (sales per click) of about</p><p>1.2%, well within common estimates of 1-2% (gamedaily.com). With</p><p>respect to consumers, I find that frequent clickers place a</p><p>greater emphasis on the position of the sponsored advertising link.</p><p>I further find that 10% of consumers perform 90% of the clicks.</p><p>I then conduct several policy simulations to illustrate the effects</p><p>of change in search engine policy. First, I find that the</p><p>search engine obtains revenue gains of nearly 1.4% by sharing</p><p>individual level information with advertisers and enabling them to</p><p>vary their bids by consumer segment. This strategy also improves</p><p>advertiser profits by 11% and consumer welfare by 2.9%. Second, I</p><p>find that a switch from a first to second price auction results in</p><p>truth telling (advertiser bids rise to advertiser valuations), which</p><p>is consistent with economic theory. However, the second price</p><p>auction has little impact on search engine profits. Third, consumer</p><p>search tools lead to a platform revenue increase of 3.7% and an</p><p>increase of consumer welfare of 5.6%. However, these tools, by</p><p>reducing advertising exposure, lower advertiser profits by 4.1%.</p><p>Sponsored Search Auctions: Research Opportunities in Marketing</p><p>In the final chapter, I systematically review the literature on</p><p>keyword search and propose several promising research directions.</p><p>The chapter is organized according to each agent in the search</p><p>process, i.e., searchers, advertisers and the search engine, and</p><p>reviews the key research issues for each. For each group, I outline</p><p>the decision process involved in keyword search. For searchers, this</p><p>process involves what to search, where to search, which results to</p><p>click, and when to exit the search. For advertisers, this process</p><p>involves where to bid, which word or words to bid on, how much to</p><p>bid, and how searchers and auction mechanisms moderate these</p><p>behaviors. The search engine faces choices on mechanism design,</p><p>website design, and how much information to share with its</p><p>advertisers and searchers. These choices have implications for</p><p>customer lifetime value and the nature of competition among</p><p>advertisers. Overall, I provide a number of potential areas of</p><p>future research that arise from the decision processes of these</p><p>various agents.</p><p>Foremost among these potential areas of future research are i) the</p><p>role of alternative consumer search strategies for information</p><p>acquisition and clicking behavior, ii) the effect of advertiser</p><p>placement alternatives on long-term profits, and iii) the measure of</p><p>customer lifetime value for search engines. Regarding the first</p><p>area, a consumer's search strategy (i.e., sequential search and</p><p>non-sequential search) affects which sponsored links are more likely</p><p>to be clicked. The search pattern of a consumer is likely to be</p><p>affected by the nature of the product (experience product vs. search</p><p>product), the design of the website, the dynamic orientation of the</p><p>consumer (e.g., myopic or forward-looking), and so on. This search</p><p>pattern will, in turn, affect advertisers payments, online traffic,</p><p>sales, as well as the search engine's revenue. With respect to the</p><p>second area, advertisers must ascertain the economic value of</p><p>advertising, conditioned on the slot in which it appears, before</p><p>making decisions such as which keywords to bid on and how much to</p><p>bid. This area of possible research suggests opportunities to</p><p>examine how advertising click-through and the number of impressions</p><p>differentially affect the value of appearing in a particular</p><p>sponsored slot on a webpage, and how this value is moderated by an</p><p>appearance in a non-sponsored slot (i.e., a slot in the organic</p><p>search results section). With respect to the third area of future</p><p>research, customer value is central to the profitability and</p><p>long-term growth of a search engine and affects how the firm should</p><p>allocate resources for customer acquisition and retention.</p><p>Organization</p><p>This dissertation is organized as follows. After this brief</p><p>introduction, the essay, ``Online Auction Demand,'' serves as a</p><p>basis that introduces some concepts of auctions as two-sided</p><p>markets. Next, the second essay, ``A Dynamic Model of Sponsored</p><p>Search Advertising,'' extends the first essay by considering a</p><p>richer context of bidder competition and consumer choice behavior.</p><p>Finally, the concluding chapter, which outlines my future research</p><p>interests, considers potential extensions that pertain especially to</p><p>sponsored search advertising.</p>
dc.format.extent 1538884 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Business Administration, Marketing
dc.subject Economics, General
dc.subject Advertising
dc.subject Auction
dc.subject Industrial Organization
dc.subject Pricing
dc.subject Sponsored Search
dc.subject Structural Models
dc.title Online Auction Markets
dc.type Dissertation
dc.department Business Administration


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