Showing papers by "Kartik Hosanagar published in 2011"

Location, Location, Location: An Analysis of Profitability of Position in Online Advertising Markets

Abstract: The authors evaluate the impact of ad placement on revenues and profits generated from sponsored search. Their approach uses data generated through a field experiment for several keywords from an online retailer's ad campaign. Using a hierarchical Bayesian model, the authors measure the impact of ad placement on both click-through and conversion rates. They find that while click-through rate decreases with position, conversion rate increases with position and is even higher for more specific keywords. The net effect is that, contrary to the conventional wisdom in the industry, the topmost position is not necessarily the revenue- or profit-maximizing position. The authors' results inform the advertising strategies of firms participating in sponsored search auctions and provide insight into consumer behavior in these environments. Specifically, they help correct a significant misunderstanding among advertisers regarding the value of the top position. Furthermore, they reveal potential inefficiencie...

Functionality-rich versus minimalist platforms: a two-sided market analysis

Abstract: Should a new "platform" target a functionality-rich but complex and expensive design or instead opt for a bare-bone but cheaper one? This is a fundamental question with profound implications for the eventual success of any platform. A general answer is, however, elusive as it involves a complex trade-off between benefits and costs. The intent of this paper is to introduce an approach based on standard tools from the field of economics, which can offer some insight into this difficult question. We demonstrate its applicability by developing and solving a generic model that incorporates key interactions between platform stakeholders. The solution confirms that the "optimal" number of features a platform should offer strongly depends on variations in cost factors. More interestingly, it reveals a high sensitivity to small relative changes in those costs. The paper's contribution and motivation are in establishing the potential of such a cross-disciplinary approach for providing qualitative and quantitative insights into the complex question of platform design.

Sponsored Search: Do Organic Results help or hurt the Performance and under what conditions?

Abstract: We study the relative impact of competing links in organic and sponsored search results on the performance of sponsored search ads. We use data generated through a field experiment for several keywords from the ad campaign of an online retailer. Using a hierarchical Bayesian model, we measure the impact of competition on both clickthrough rate and conversion rate of sponsored search ads for these keywords. We find that the competitor links in organic results have a higher impact on the performance as compared to the competitor links in sponsored results. We also find that competition has a greater influence on the conversion performance as compared to the click through performance. Our results inform advertisers on the impact of organic results on their performance. Our results reveal inefficiency in the current auction mechanism as the click performance may not reveal the true quality of advertisers.

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

Abstract: There has been significant recent interest in studying consumer behavior in sponsored search advertising (SSA). Researchers have typically used daily data from search engines containing measures such as average bid, average ad position, total impressions, clicks and cost for each keyword in the advertiser's campaign. A variety of random utility models have been estimated using such data and the results have helped researchers explore the factors that drive consumer click and conversion propensities. However, virtually every analysis of this kind has ignored the intra-day variation in ad position. We show that estimating random utility models on aggregated (daily) data without accounting for this variation will lead to systematically biased estimates -- specifically, the impact of ad position on click-through rate (CTR) is attenuated and the predicted CTR is higher than the actual CTR. We demonstrate the existence of the bias analytically and show the effect of the bias on the equilibrium of the SSA auction. Using a large dataset from a major search engine, we measure the magnitude of bias and quantify the losses suffered by the search engine and an advertiser using aggregate data. The search engine revenue loss can be as high as 11% due to aggregation bias. We also present a few data summarization techniques that can be used by search engines to reduce or eliminate the bias.

Recommender Systems and their Effects on Consumers

Abstract: Recommender systems are becoming integral to how consumers discover media The value that recommenders offer is personalization In environments with many product choices, recommenders personalize the experience to each user’s taste Popular applications include product recommendations at online retail sites and online newspapers’ automatically selecting articles to display based on the current reader’s interests This ability to focus more closely on one's taste and filter all else out has spawned criticism that recommenders will fragment users Critics say recommenders cause consumers to have less in common with one another and that the media should do more to increase exposure to a variety of content Others, however, contend that recommenders do the opposite: they may homogenize users because they share information among those who would otherwise not communicate These are opposing views, discussed in the literature for over fourteen years, for which there is not yet empirical evidence We present an empirical study of recommender systems in the music industry In contrast to concerns that users are becoming more fragmented, we find that in our setting users become more similar to one another in their purchases This increase in similarity occurs for two reasons, which we term volume and taste effects The volume effect is that consumers simply purchase more after recommendations, increasing the chance of having more purchases in common The taste effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations When we view consumers as a similarity network, the network becomes denser and smaller (characterized by shorter inter-user distances) after recommendations are introduced These findings suggest that for this setting, recommender systems are associated with an increase in commonality among users and that concerns of fragmentation may be misplaced

Usercentric Operational Decision Making in Distributed Information Retrieval

Abstract: Information specialists in enterprises regularly use distributed information retrieval (DIR) systems that query a large number of information retrieval (IR) systems, merge the retrieved results, and display them to users. There can be considerable heterogeneity in the quality of results returned by different IR servers. Further, because different servers handle collections of different sizes and have different processing and bandwidth capacities, there can be considerable heterogeneity in their response times. The broker in the DIR system has to decide which servers to query, how long to wait for responses, and which retrieved results to display based on the benefits and costs imposed on users. The benefit of querying more servers and waiting longer is the ability to retrieve more documents. The costs may be in the form of access fees charged by IR servers or user's cost associated with waiting for the servers to respond. We formulate the broker's decision problem as a stochastic mixed-integer program and present analytical solutions for the problem. Using data gathered from FedStats---a system that queries IR engines of several U.S. federal agencies---we demonstrate that the technique can significantly increase the utility from DIR systems. Finally, simulations suggest that the technique can be applied to solve the broker's decision problem under more complex decision environments.

Functionality-rich Versus Minimalist Platforms: A Two-sided Market Analysis This article is an editorial note submitted to CCR. It has NOT been peer reviewed. The authors take full responsibility for this article's technical content. Comments can be posted through CCR Online.

Abstract: Should a new “platform” target a functionality-rich but complex and expensive design or instead opt for a bare-bone but cheaper one? This is a fundamental question with profound implications for the eventual success of any platform. A general answer is, however, elusive as it involves a complex trade-o between benefits and costs. The intent of this paper is to introduce an approach based on standard tools from the field of economics, which can oer some insight into this dicult question. We demonstrate its applicability by developing and solving a generic model that incorporates key interactions between platform stakeholders. The solution confirms that the “optimal” number of features a platform should oer strongly depends on variations in cost factors. More interestingly, it reveals a high sensitivity to small relative changes in those costs. The paper’s contribution and motivation are in establishing the potential of such a cross-disciplinary approach for providing qualitative and quantitative insights into the complex question of platform design.