<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> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/1073 |
| Date | January 2009 |
| Creators | Yao, Song |
| Contributors | Mela, Carl |
| Source Sets | Duke University |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1538884 bytes, application/pdf |
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