Search Engine Revenue Research Articles

Search Engine Revenue from Navigational and Brand Advertising

Search Engine Revenue from Navigational and Brand Advertising

Bid Coordination in Sponsored Search Auctions: Detection Methodology and Empirical Analysis*

Bid delegation to specialized intermediaries is common in internet ad auctions. When the same intermediary bids for competing advertisers, its incentive to coordinate client bids might alter the functioning of the auctions. This study develops a methodology to detect bid coordination and presents a strategy to estimate a bound on the search engine revenue losses imposed by bid coordination. When the method is applied to data from auctions held on a major search engine, coordination is detected in 55% of the cases of delegated bidding and the search engine's revenue loss ranges between 5.3% and 10.4%.

Modeling Consumer Footprints on Search Engines: An Interplay with Social Media

It is now well understood that social media plays an increasingly important role in consumers’ decision making. However, an overload of social media content in product search engines can hinder consumers from efficiently seeking information. We propose a structural econometric model to understand consumers’ preferences and costs on search engines to improve user experience under unstructured social media. Our model combines an optimal stopping framework with an individual-level random utility choice model and analyzes click behavior in conjunction with purchase choices. Our model accounts for three major constraints in a consumer’s decision-making process: (1) interdependency in decision making for different alternatives, (2) sequential arrival of information revealed by click-throughs, and (3) nonnegligible search cost. Our approach allows us to jointly estimate consumers’ heterogeneous preferences and search costs under the interplay of social media and search engines, and to predict search and purchase behavior for each consumer. We validate the model using an individual session-level data set of approximately seven million observations resulting in room bookings in 2,117 U.S. hotels. Interestingly, our analysis allows us to quantify the trade-off between consumers’ benefits and cognitive costs from using large-scale unstructured social media information during decision making. Our policy experiments show that providing a carefully curated digest of social media content during the earlier stages of consumer search (i.e., on the search results summary page) can lead to a 12.01% increase in the overall search engine revenue. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2991 . This paper was accepted by Anandhi Bharadwaj, information systems.

Impact of response latency on sponsored search

Recent research in the human computer interaction and information retrieval areas has revealed that search response latency exhibits a clear impact on the user behavior in web search. Such impact is reflected both in users’ subjective perception of the usability of a search engine and in their interaction with the search engine in terms of the number of search results they engage with. However, a similar impact analysis has been missing so far in the context of sponsored search. Since the predominant business model for commercial search engines is advertising via sponsored search results (i.e., search advertisements), understanding how response latency influences the user interaction with the advertisements displayed on the search engine result pages is crucial to increase the revenue of a commercial search engine. To this end, we conduct a large-scale analysis using query logs obtained from a commercial web search. We analyze the short-term and long-term impact of search response latency on the querying and clicking behaviors of users using desktop and mobile devices to access the search engine, as well as the corresponding impact on the revenue of the search engine. This analysis demonstrates the importance of serving sponsored search results with low latency and provides insight into the ad serving policy of commercial search engines to ensure long-term user engagement and search revenue.

Learning algorithms for second-price auctions with reserve

Second-price auctions with reserve play a critical role in the revenue of modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real-world data demonstrating their effectiveness.

Global Optimization for Advertisement Selection in Sponsored Search

Advertisement (ad) selection plays an important role in sponsored search, since it is an upstream component and will heavily influence the effectiveness of the subsequent auction mechanism. However, most existing ad selection methods regard ad selection as a relatively independent module, and only consider the literal or semantic matching between queries and keywords during the ad selection process. In this paper, we argue that this approach is not globally optimal. Our proposal is to formulate ad selection as such an optimization problem that the selected ads can work together with downstream components (e.g., the auction mechanism) to achieve the maximization of user clicks, advertiser social welfare, and search engine revenue (we call the combination of these objective functions as the marketplace objective for ease of reference). To this end, we 1) extract a bunch of features to represent each pair of query and keyword, and 2) train a machine learning model that maps the features to a binary variable indicating whether the keyword is selected or not, by maximizing the aforementioned marketplace objective. This formalization seems quite natural; however, it is technically difficult because the marketplace objective is non-convex, discontinuous, and indifferentiable regarding the model parameter due to the ranking and second-price rules in the auction mechanism. To tackle the challenge, we propose a probabilistic approximation of the marketplace objective, which is smooth and can be effectively optimized by conventional optimization techniques. We test the ad selection model learned with our proposed method using the sponsored search log from a commercial search engine. The experimental results show that our method can significantly outperform several ad selection algorithms on all the metrics under investigation.

Optimal Revenue Maximizing Mechanisms in Common-Value Position Auctions

We study the optimal mechanism in position auctions in a common-value setting where only ordinal information about the advertisers is posted and auctioneer revenue depends on consumer belief about ad qualities. We show that when bidder valuations are correlated, existence of a simple non-ironed optimal mechanism requires a stronger condition that the usual increasing virtual valuation assumption. With fixed number of positions, the corresponding optimal decision rule (among all mixed and pure strategies) is to allocate the ad positions to advertisers in decreasing order of quality. More importantly, when the search engine also chooses the number of ads to post, ignoring the consumer belief endogeneity will lead to posting too many ads compared to what maximizes the search engine revenue. We also show that when only one ad position is available, the optimal allocation rule uses a reserve price which is higher than what is implied by Myerson 1981 seminal work. Finally, we characterize the optimal mechanism when cardinal information about advertiser quality is posted, and we provide results on how search engine revenue compares under the two regimes.

Bid keyword suggestion in sponsored search based on competitiveness and relevance

In sponsored search, many advertisers have not achieved their expected performances while the search engine also has a large room to improve their revenue. Specifically, due to the improper keyword bidding, many advertisers cannot survive the competitive ad auctions to get their desired ad impressions; meanwhile, a significant portion of search queries have no ads displayed in their search result pages, even if many of them have commercial values. We propose recommending a group of relevant yet less-competitive keywords to an advertiser. Hence, the advertiser can get the chance to win some (originally empty) ad slots and accumulate a number of impressions. At the same time, the revenue of the search engine can also be boosted since many empty ad shots are filled. Mathematically, we model the problem as a mixed integer programming problem, which maximizes the advertiser revenue and the relevance of the recommended keywords, while minimizing the keyword competitiveness, subject to the bid and budget constraints. By solving the problem, we can offer an optimal group of keywords and their optimal bid prices to an advertiser. Simulation results have shown the proposed method is highly effective in increasing ad impressions, expected clicks, advertiser revenue, and search engine revenue.

Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue

In this paper, we study the effects of three different kinds of search engine rankings on consumer behavior and search engine revenues: direct ranking effect, interaction effect between ranking and product ratings, and personalized ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) A consumer-utility-based ranking mechanism can lead to a significant increase in overall search engine revenue. (2) Significant interplay occurs between search engine ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their ranking algorithms. (3) Our randomized experiments also reveal that an “active” personalized ranking system (wherein users can interact with and customize the ranking algorithm) leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a “passive” personalized ranking system (wherein users cannot interact with the ranking algorithm). This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized ranking systems by default. Overall, our study unravels the economic impact of ranking and its interaction with social media on product search engines.This paper was accepted by Lorin Hitt, information systems.

Ad-centric Model Discovery for Prediciting Ads's Click-through Rate

Click here and insert your abstract text. Search engine advertising has become one of the most important revenue models of electronic commerce. It strongly affects the probability that users click on the ads at the side of the search results page if the system shows the right ones. To maximize the outcome of search engine revenue and improve perception on those ads, it is important to understand the factors which affect the click through rate (CTR) on those ads. Tencent founded in 1998, is one of China's largest and most used Internet service portals. It provides a number of online services such as value-added Internet, mobile and telecom services and online advertising. As of September 30, 2011, Tencent had 711.7 million active Instant Messenger users. It forms the largest Internet Community in China. In this research, we use a very large dataset of Tencent click logs (soso.com) with millions records. First we describe how soso.com searching engine advertising works, our system architecture is designed with the click log dataset, and observations inside it aims at those ads with enough historical click logs. Then we show how to use ad-centric features to discover models that can find factors affecting CTR prediction performance. The proposed framework could help both optimizing the search engine system for soso.com and improving the ads designs for the advertisers.

Paid Placement: Advertising and Search on the Internet

Paid placement, where advertisers bid payments to a search engine to have their products displayed prominently among the results of a keyword search, has emerged as a predominant form of advertising on the Internet. This article studies a model of product differentiation in which the auction of advertisement positions is embedded in a market game of consumer search. In equilibrium, more relevant sellers for a given keyword bid more and their paid placement by the search engine reveals information about the relevance of their products. This results in efficient sequential search by consumers and increases total output. We also find that the search engine's revenue may have an inverted U-shape with respect to the match probability of the most relevant seller.

Exclusive Display in Sponsored Search Advertising

As sponsored search becomes increasingly important as an advertising medium for firms, search engines are exploring more advanced bidding and ranking mechanisms to increase their revenue from auctions for sponsored search advertising. For instance, Google, Yahoo! and Bing are experimenting with auction mechanisms in which each advertiser can bid to be displayed exclusively. In such an auction, each advertiser submits two bids: one bid for the standard display format in which multiple advertisers are displayed, and one bid for being shown exclusively. The search engine decides the outcome as non-exclusive or exclusive based on revenue generated — if the exclusive-placement bid by an advertiser is high enough then only that advertiser is displayed, otherwise multiple advertisers are displayed and ranked based on their multiple-placement bids. We find that allowing advertisers to bid for exclusivity has two countervailing effects for search engine revenue. First, competition is heightened because bidders can express their display preferences more flexibly and compete not only for positions in the non-exclusive outcome but also compete for the outcome to be exclusive or non-exclusive; this is good for search engine revenue. Second, competition between non-exclusive and exclusive outcomes gives bidders the incentive to reduce their bids for their non- preferred outcome; this is bad for search engine revenue. Under different parametric conditions, either force can prevail and search engine revenue may increase or decrease accordingly. We also find counterintuitive results on the bidding strategies of advertisers; for instance, we find that, under certain conditions, advertisers have the incentive to bid above their true valuations.

On Aggregation Bias in Sponsored Search Data: Existence and Implications

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.

A dynamic model of bidding patterns in sponsored search auctions

We develop an infinite horizon alternative-move model of the unique second-price sponsored search auction. We use this model to explain two distinguishable bidding patterns observed in our bidding data: bidding war cycle and stable bid. With examples, we show that only a small portion of the value generated may be extracted by search engines, if advertisers are engaged in bidding war cycles. Finally, we show the impact of auction design on advertiser bids and search engine revenue.

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

Recently there has been significant 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 intraday 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 analytically demonstrate the existence of the bias and show the effect of the bias on the equilibrium of the SSA auction. Using a large data set 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.