Search

In 2020, the Department of Justice (DOJ) filed an antitrust suit against Google alleging that Google has unlawfully monopolized the markets for general search services, search advertising, and general search text advertising. The complaint raises questions involving market definition, monopoly power, and monopolizing conduct. In this article, we examine these issues through the lens of microeconomic principles. Our analysis finds that there is a sound economic rationale for the DOJ's complaint. [ABSTRACT FROM AUTHOR]

Platform companies such as Alibaba.com increasingly rely on search advertising as a revenue source. This study examines (1) the direct effect of new and existing buyers and sellers on platform advertising revenue, (2) their indirect effect through two intermediary performance variables (buyer's click rate and seller's click price), and (3) how the effects differ between launch and mature stages of the search advertising service. Unique data collected from a leading transactional business-to-business electronic platform suggest that new buyers click on more search advertisements than existing buyers, especially after the firm's buyers and sellers have learned and adapted to the service (mature stage). New sellers tend to outbid existing sellers in the mature stage, but the opposite is true when the service is newly introduced (launch stage). Because existing sellers can more effectively send quality signals in the launch stage, attracting existing, rather than new, sellers has a greater effect on click rate in the launch stage; however, the opposite is true in the mature stage. Attracting new buyers also has a greater effect on click rate and price, especially in the mature stage. Finally, using cost data from the platform, this article examines the economic returns of attracting new and existing buyers and sellers with respect to advertising revenue. [ABSTRACT FROM AUTHOR]

The current marketing environment is characterized by a surge in multichannel shopping and increasing choice of advertising channels. This situation requires firms to understand how advertising in one channel (e.g., online) influences sales in another channel (e.g., offline). This article studies the presence, magnitude, and carryover of these cross-channel effects for online advertising (display and search) and traditional media. The analysis considers how these advertising expenditures translate directly into sales, as well as indirectly through intermediate search advertising metrics-namely, impressions and click-through rate. For a high-end clothing and apparel retailer, the authors find that cross effects exist and are important and that cross-effect elasticities are almost as high as own-effect elasticities. Online display and, in particular, search advertising is more effective than traditional advertising. This result is primarily due to strong cross effects on the offline channel. Return-on-investment calculations suggest that by ignoring these cross effects, firms substantially miscalculate the effectiveness of online advertising. Notably, the authors find that traditional advertising decreases paid search click-through rates, thus reducing the net cross effect of traditional advertising. [ABSTRACT FROM AUTHOR]

Product sales via sponsored keyword advertising on search engines rely on an effective selection of keywords that describe the offerings. In this study, we consider both the direct sales of the advertised products and indirect sales (i.e., cross-selling) of other products, and examine how specific keywords and general keywords influence these two types of sales differently. We also examine how the cross-selling effects may vary across different types of products (main products and accessories). Our results suggest that the use of specific keywords leans toward improving the direct sales of advertised products, while the use of general keywords leans toward improving the indirect sales of other products. The contribution of keywords to indirect sales is influenced by product type. For main products, the use of specific keywords generates a higher marginal contribution to indirect sales than that of general keywords. For accessory products, the use of general keywords generates a higher marginal contribution to indirect sales than that of specific keywords. The key implication of this study is that sellers focusing on different types of sales (direct or indirect sales) or products (main or accessory products) should consider using different types of keywords in search engine advertising to drive sales. [ABSTRACT FROM AUTHOR]

The authors present a modeling approach to assess the purchase conversion performance of individual keywords in paid search advertising. The model facilitates estimation of daily keyword conversion and click-through rates in a sparse data environment while accounting for the endogenous position of the text advertisement served in response to a search. Position endogeneity in paid search data can arise from both omitted variables and measurement error. The authors propose a latent instrumental variable approach to address this problem. They estimate their model on keyword-level paid search data containing daily information on impressions, clicks, and reservations for a major lodging chain. They find that higher positions increase both the click-through and conversion rates. When advertisements are served in higher positions, approximately one-third of new conversions is due to increased click-through while approximately two-thirds are due to increased conversion rates. The authors show that the keyword list generated on the basis of their estimated conversion rates outperforms the status quo list as well as lists generated by observed conversion and click-through rates. [ABSTRACT FROM AUTHOR]

E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well known that there are two types of preferences: long-term ones and short-term ones. The former refers to users' inherent purchasing bias and evolves slowly. By contrast, the latter reflects users' purchasing inclination in a relatively short period. They both affect users' current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end,we propose a novel Attentive Long Short-Term Preferencemodel, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long- and shortterm user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users' current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics. [ABSTRACT FROM AUTHOR]

The authors build a game-theoretic model to investigate how a manufacturer and its retailers should coordinate in search advertising and prescribe the manufacturer's optimal cooperative search advertising strategy. Channel coordination in search advertising is an important but complicated managerial decision for both manufacturers and retailers. Because of the highly concentrated market of search advertising, a manufacturer's and its retailers' ads can compete instead of complementing each other. We consider a manufacturer, who coordinates with its retailers by sharing a fixed percentage of each retailer's advertising cost and, at the same time, competes with its retailers and outside advertisers in search ad position auctions. Our model prescribes the optimal cooperative advertising strategies from the manufacturer's perspective. We find that different from cooperative advertising in traditional media, it can be optimal for a manufacturer to cooperate with only a subset of its retailers even if they are ex ante the same. This reflects the manufacturer's trade-off between higher demand and higher bidding cost caused by more intense competition. We also find that with two asymmetric retailers, the manufacturer should support the retailer with higher channel profit per click to get a higher position than the other retailer, which demonstrates the effectiveness of the participation-rate mechanism. The manufacturer should take a higher position than a retailer when its profit per click via direct sales exceeds the channel profit per click of the retailer. The main results still hold when we endogenize retail price competition or wholesale contracts. The online appendix is available at https://doi.org/10.1287/mksc.2018.1111. [ABSTRACT FROM AUTHOR]

In sponsored search advertising, keywords serve as an essential bridge linking advertisers, search users, and search engines. Advertisers have to deal with a series of keyword decisions throughout the entire lifecycle of search advertising campaigns. This paper proposes a multilevel and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions. Based on this framework, we develop corresponding optimization strategies for keyword targeting, keyword assignment, and keyword grouping at different levels (e.g., market, campaign, and adgroup). With two real-world datasets obtained from past search advertising campaigns, we conduct computational experiments to evaluate our keyword optimization framework and instantiated strategies. Experimental results show that our method can approach the optimal solution in a steady way, and it outperforms two baseline keyword strategies commonly used in practice. The proposed MKOF framework also provides a valid experimental environment to implement and assess various keyword strategies in sponsored search advertising. [ABSTRACT FROM AUTHOR]

Although keyword auctions are often studied in the context of a single keyword in the literature, firms generally have to participate in multiple keyword auctions at the same time. Advertisers purchase a variety of keywords that can be categorized as generic-relevant, focal-brand, and competing-brand keywords. At the same time, firms also have to choose how the keywords can be matched to search queries: exact, phrase, or broad. This study empirically examines how keyword categories and match types influence the performance of advertising campaigns. We build a hierarchical Bayesian model to address the endogeneity problem contained in the simultaneous equations of the click-through rate, the conversion rate, cost per click, and rank, and we use the Markov Chain Monte Carlo method to identify the parameters. Our results suggest that it is important to differentiate among the various bidding strategies for various keyword categories and match types. We also report results related to financial performance such as number of sales, profit, and return on investment for different keywords. These findings shed light on the practice of sponsored search advertising by offering insights into how to manage ad campaigns when advertisers have to bid on multiple keywords. The online appendix is available at https://doi.org/10.1287/isre.2017.0724. [ABSTRACT FROM AUTHOR]

Search advertising is the ordered list of advertisements that appears when a user searches for something in an online search engine. By construction, these ads differ in prominence: ads higher up the list are more prominent than ads lower down the list. However, search ads also differ in prominence in another way: prominence of advertiser. This paper examines how these two types of prominence interact in determining the click-through rate (CTR) of these ads. Using individual-level click-stream data from Microsoft's Live Search platform and measures of advertiser prominence fromAlexa.com, we find that ad position and advertiser prominence are substitutes. Specifically, in searches for camera brands, a retailer not in the top 100 of Alexa rankings has a 30%-50% higher CTR in position 1 than in position 2, whereas a retailer in the top 100 of Alexa rankings has only a 0%-13% higher CTR for the same position improvement. Qualitatively similar results are obtained for several other search strings. These findings demonstrate, first, that advertiser brand matters even for search ads, and, second, the way it matters is the opposite of what is usually assumed in the theoretical literature on search advertising. [ABSTRACT FROM AUTHOR]

Abundant data has led to new opportunities for empirical auctions research in recent years, with much of the newest work on auctions of multiple objects, including: (1) auctions of ranked objects (such as sponsored search ads), (2) auctions of identical objects (such as Treasury bonds), and (3) auctions of dissimilar objects (such as FCC spectrum licenses). This paper surveys recent developments in the empirical analysis of such auctions. (JEL D44, H82) [ABSTRACT FROM AUTHOR]

Reported literature in sponsored search advertising markets asserts that at equilibrium an advertiser has no incentive to swap her position with another advertiser and her bid on a keyword would be bound with the click value acting as an upper bound. We investigate a closer-to-practice case where advertisers do not have an ex-ante known value per click and her bid on a keyword is an outcome of simple cost-cap heuristics on a portfolio of keywords. Using simulations and an experimental setup containing advertisers that have the same upper-cap on cost, we show that the distribution of advertisers' cost per click and bids are emergent in nature. Keywords exhibit ex post heterogeneity in observed valuation even when all advertisers bid under the same cost-cap constraint. We explore the dynamics of the market, such as temporal stability of advertiser's bids, and advertiser's rank based on the click-share along with the distribution of ex post valuation of keywords associated with this closer to practice setup. The results call for a richer understanding of these markets that can incorporate temporal interdependence between auctions of a keyword as well as boundedly rational behavior of advertisers working under imperfect information. [ABSTRACT FROM AUTHOR]

Although keyword auctions are often studied in the context of a single keyword in the literature, firms generally have to participate in multiple keyword auctions at the same time. Advertisers purchase a variety of keywords that can be categorized as generic-relevant, focal-brand, and competing-brand keywords. At the same time, firms also have to choose how the keywords can be matched to search queries: exact, phrase, or broad. This study empirically examines how keyword categories and match types influence the performance of advertising campaigns. We build a hierarchical Bayesian model to address the endogeneity problem contained in the simultaneous equations of the click-through rate, the conversion rate, cost per click, and rank, and we use the Markov Chain Monte Carlo method to identify the parameters. Our results suggest that it is important to differentiate among the various bidding strategies for various keyword categories and match types. We also report results related to financial performance such as number of sales, profit, and return on investment for different keywords. These findings shed light on the practice of sponsored search advertising by offering insights into how to manage ad campaigns when advertisers have to bid on multiple keywords. The online appendix is available at . [ABSTRACT FROM AUTHOR]

Copyright of Socialiniai Tyrimai is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Paid search advertising has been a widely used marketing tool in both Chinese and English countries. Matching strategy greatly influences the effectiveness of paid search advertising. Extant studies have examined the matching strategy between keywords and ad content in paid search advertising using the English language. However, the rapidly growing Chinese paid search advertising market has been largely ignored. Different from the English market, the Chinese paid search advertising market has a comparatively greater use of synonyms. Considering the high semantic dependence of words and characters in Chinese, we develop a method to classify Chinese keywords according to the information complexity of the keywords. Based on the keyword classification, we use synonym-based matching, defined as the semantic similarity of ad content and the keyword, to study the bidding behavior of Chinese paid search advertisers. Our results indicate that synonym-based matching increases click-through rate, especially for complex keywords that have multiple search attributes. Both the empirical analysis using secondary data from the Chinese paid search market and a subsequent controlled experiment show the robustness of the results. Our results point to the need for understanding the local characteristics (especially language) when studying online paid search advertising in the Chinese market. [ABSTRACT FROM AUTHOR]

Firms use different attribution strategies such as last-click or first-click attribution to assign conversion credits to search keywords that appear in their consumers' paths to purchase. These attributed credits impact a firm's future bidding and budget allocations among keywords and, in turn, determine the overall return-on-investment of search campaigns. In this paper, we model the relationship among the advertiser's bidding decision for keywords, the search engine's ranking decision for these keywords, and the consumer's click-through rate and conversion rate on each keyword, and analyze the impact of the attribution strategy on the overall return-on-investment of paid search advertising. We estimate our simultaneous equations model using a six-month panel data of several hundred keywords from an online jewelry retailer. The data comprises a quasi-experiment as the firm changed attribution strategy from last-click to first-click attribution halfway through the data window. Our results show that returns for keyword investments vary significantly under the different attribution strategies. For the focal firm, first-click attribution leads to lower revenue returns and a more pronounced decrease for more specific keywords. Our policy simulation exercise shows how the firm can increase its overall returns by better attributing the real contribution of keywords. We discuss how an appropriate attribution strategy can help firms to better target customers and lower acquisition costs in the context of paid search advertising. Data, as supplemental material, are available at . [ABSTRACT FROM AUTHOR]

In sponsored search advertising, advertisers bid to be displayed in response to a keyword search. The operational activities associated with participating in an auction, i.e., submitting the bid and the ad copy, customizing bids and ad copies based on various factors (such as the geographical region from which the query originated, the time of day and the season, the characteristics of the searcher), and continuously measuring outcomes, involve considerable effort. We call the costs that arise from such activities keyword management costs. To reduce these costs and increase advertisers' participation in keyword auctions, search engines offer an opt-in tool called broad match with automatic and flexible bidding, wherein the search engine automatically places bids on behalf of the advertisers and takes over the above activities as well. The bids are based on the search engine's estimates of the advertisers' valuations and, therefore, may be less accurate than the bids the advertisers would have turned in themselves. Using a game-theoretic model, we examine the strategic role of keyword management costs, and of broad match, in sponsored search advertising. We show that because these costs inhibit participation by advertisers in keyword auctions, the search engine has to reduce the reserve price, which reduces the search engine's profits. This motivates the search engine to offer broad match as a tool to reduce keyword management costs. If the accuracy of broad match bids is sufficiently high, advertisers adopt broad match and benefit from the cost reduction, whereas if the accuracy is very low, advertisers do not use it. Interestingly, at moderate levels of bid accuracy, advertisers individually find it attractive to reduce costs by using broad match, but competing advertisers also adopt broad match and the increased competition hurts all advertisers' profits, thus creating a "prisoner's dilemma." When advertisers adopt broad match, search engine profits increase. It therefore seems natural to expect that the search engine will be motivated to improve broad match accuracy. Our analysis shows that the search engine will increase broad match accuracy up to the point where advertisers choose broad match, but that increasing the accuracy any further reduces the search engine's profits. [ABSTRACT FROM AUTHOR]

This article examines the disruption that the global advertising industry is undergoing as it struggles to adapt to the extraordinary opportunities for marketing over the Internet and still maintain its former hold over "legacy" media. The "fresh" global media players, particularly Google, have risen quickly to claim unprecedented influence over the flows of advertising revenue that are the lifeblood of the media, obliging advertising agencies to compete in the new digital space that Internet advertising has opened. However, U.S.-based new media companies are having to deal with strong local competitors in major developing markets, at the same time as they face social criticism and regulatory intervention for their practices in exploiting users for marketing purposes in their more established markets. [ABSTRACT FROM AUTHOR]

In keyword search advertising, many advertisers operate on a limited budget. Yet how limited budgets affect keyword search advertising has not been extensively studied. This paper offers an analysis of the generalized second-price auction with budget constraints. We find that the budget constraint may induce advertisers to raise their bids to the highest possible amount for two different reasons, i.e., to accelerate the elimination of the budget-constrained competitor, and to reduce their own advertising cost. Thus, in contrast to the current literature, our analysis shows that both budget-constrained and unconstrained advertisers could bid more than their own valuation. We further extend the model to consider dynamic bidding and budget-setting decisions. [ABSTRACT FROM AUTHOR]

Consumer search activities can be endogenously determined by the ad positions in sponsored search advertising. We model how advertisers compete for ad positions in sponsored listings and, conditional on the list of sponsored ads, how online consumers search for information and make purchase decisions. On the consumer side, assuming that users browse information from top to bottom and adopt a sequential search strategy, we develop a two-stage model of consumer search (whether to click and whether to stop the search) that extends the standard sequential search framework in economics literature. On the advertiser side, it is very difficult to fully specify the optimal strategies of advertisers because the equilibrium outcome depends on variables that researchers do not observe. As we have an incomplete model of advertiser competition, we propose using the necessary condition that, at equilibrium, no advertiser will find another available ad position more valuable than the one it has chosen. Using a data set obtained from a search engine, we find that consumers can be classified into two segments that exhibit distinct search behaviors. For advertisers, the value of search advertising comes primarily from terminal clicks, which represent the last link (including organic results) clicked by an online user. We also demonstrate that the value of ad positions depends not only on the identities and positions of the advertisers in sponsored listings but also the composition of online consumers who exhibit distinct search behaviors. [ABSTRACT FROM AUTHOR]

One critical operational decision facing online advertisers when they engage in sponsored search advertising is concerned with the allocation of a limited advertising budget. In particular, dealing with multi-keyword search markets over multiple decision periods poses significant decision-making challenges. In this paper, we develop a novel budget allocation optimization model with multiple search advertising markets and a finite time horizon. One key element of our modeling work is developing a customized advertising response function when considering distinctive features of sponsored search, including the quality score and the dynamic advertising effort. We derive a feasible solution to our budget model and study its properties. Computational experiments are conducted on real-world data to evaluate our budget model and perform parameter sensitivity analysis. Experimental results indicate that our budget allocation strategy significantly outperforms several baseline strategies. In addition, the identified properties derived from the solution process illuminate critical managerial insights for advertisers in sponsored search. [ABSTRACT FROM AUTHOR]

Quality score (QS) plays a critical role in sponsored search advertising (SSA) auctions, and in practice is closely correlated to the historical click-through rate (CTR) of an advertisement. The CTR-QS correlation may impose great influence on advertisers’ positioning strategies of selecting the targeting slots in the sponsored list. In the literature, however, QS is implicitly assumed to be an independent variable and exogenously assigned by Web search engines, so that little theoretical or managerial insights can be offered to help understand the positioning dynamics in SSA auctions with CTR-QS correlation. We strive to bridge this research gap in this paper. Based on a discrete time-dependent optimal control model, which explicitly captures the relationship between the historical CTR and QS, we determine the optimal strategy for revenue-maximizing advertisers’ QS-based positioning decisions through a policy-iteration-based numerical approximation method. We also investigate two practically-used heuristic strategies, namely the greedy and farsighted positioning strategies, aiming to examine and help understand advertisers’ real-world positioning dynamics. Our analysis indicates that both the optimal and greedy positioning strategies lead advertisers to monotonically increase or decrease their targeting slots over time, which may cause a polarization trend emerging in SSA markets. Meanwhile, the farsighted positioning strategy can accelerate the polarization. Our simulations show that both the greedy and farsighted strategies have good revenue performance. Our findings indicate that advertisers should monotonically adjust their targeting positions to maximize their revenue in CTR-QS correlated SSA auctions. Our findings also highlight the need for Web search engine companies to set a lowered weight for historical CTRs or use position-normalized CTRs in their QS measurements, so as to suppress the polarization trend. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to test the utility of regulatory focus theory principles in a real-world setting; specifically, Internet hosted text advertisements. Effect of compatibility of the ad text with the regulatory focus of the consumer was examined. Design/methodology/approach -- Advertisements were created using Google AdWords. Data were collected for the number of views and clicks each ad received. Effect of regulatory fit was measured using logistic regression. Findings -- Logistic regression analyses demonstrated that there was a strong main effect for keyword, such that users were almost six times as likely to click on a promotion advertisement as a prevention advertisement, as well as a main effect for compatibility, such that users were twice as likely to click on an advertisement with content that was consistent with their keyword. Finally, there was a strong interaction of these two variables, such that the effect of consistent advertisements was stronger for promotion searches than for prevention searches. Research limitations/implications -- The effect of ad compatibility had medium to large effect sizes, suggesting that individuals' state may have more influence on advertising response than do individuals' traits (e.g. personality traits). Measurement of regulatory fit was limited by the constraints of Google AdWords. Practical implications -- The results of this study provide a possible framework for ad creation for Internet advertisers. Originality/value -- This paper is the first study to demonstrate the utility of regulatory focus theory in online advertising. [ABSTRACT FROM AUTHOR]

One of the big surprises of the 21st century has been the ability of all sorts of interesting Web applications to support themselves through advertising, rather than subscription. While radio and television have managed to use advertising as their primary revenue source, most media - newspapers and magazines, for example - have had to use a hybrid approach, combining revenue from advertising and subscriptions. A venue for on-line advertising has been search, and much of the effectiveness of search advertising came from the "adwords" model of matching search queries to advertisements. This paper presents the algorithms for optimizing the way of matching search queries to advertisements is done. The algorithms discussed are of unusual type; they are greedy and they are on-line which are used to tackle the adwords problem. [ABSTRACT FROM AUTHOR]

Traditional advertising, such as TV and print advertising, primarily builds awareness of a firm's product among consumers, whereas sponsored search advertising on a search engine can target consumers closer to making a purchase because they reveal their interest by searching for a relevant keyword. Increased consumer targetability in sponsored search advertising induces a firm to "poach" a competing firm's consumers by directly advertising on the competing firm's keywords; in other words, the poaching firm tries to obtain more than its "fair share" of sales through sponsored search advertising by free riding on the market created by the firm being poached. Using a game theory model with firms of different advertising budgets, we study the phenomenon of poaching, its impact on how firms allocate their advertising budgets to traditional and sponsored search advertising, and the search engine's policy on poaching. We find that, as budget asymmetry increases, the smaller-budget firm poaches more on the keywords of the larger-budget firm. This may induce the larger-budget firm to allocate more of its budget to traditional advertising, which, in turn, hurts the search engine's advertising revenues. Therefore, paradoxically, even though poaching increases competition in sponsored search advertising, the search engine can benefit from limiting the extent of poaching. This explains why major search engines use "ad relevance" measures to handicap poaching on trademarked keywords. [ABSTRACT FROM AUTHOR]

In search advertising, brand names are often purchased as keywords by the brand owner or a competitor. We aim to understand the strategic benefits and costs of a firm buying its own brand name or a competitor's brand name as a keyword. We model the effect of search advertising to depend on the presence or absence of a competitor's advertisement on the same results page. We find that the quality difference between the brand owner and the competitor moderates the purchase decision of both firms. Interestingly, in some cases, a firm may buy its own brand name only to defend itself from the competitor's threat. It is also possible that the brand owner, by buying its own branded keyword, precludes the competitor from buying the same keyword. Our result also implies that the practice of bidding on the competitor's brand name creates a prisoner's dilemma, and thus both firms may be worse off, but the search engine captures the lost profits. We also discuss the difference in our results when the search is for a generic keyword instead of a branded keyword. Finally, we find some empirical support for our theory from the observation of actual purchase patterns on Google AdWords. [ABSTRACT FROM AUTHOR]

This paper studies the effects of various types of online advertisements on purchase conversion by capturing the dynamic interactions among advertisement clicks themselves. It is motivated by the observation that certain advertisement clicks may not result in immediate purchases, but they stimulate subsequent clicks on other advertisements, which then lead to purchases. We develop a novel model based on mutually exciting point processes, which consider advertisement clicks and purchases as dependent random events in continuous time. We incorporate individual random effects to account for consumer heterogeneity and cast the model in the Bayesian hierarchical framework. We construct conversion probability to properly evaluate the conversion effects of online advertisements. We develop simulation algorithms for mutually exciting point processes to compute the conversion probability and for out-of-sample prediction. Model comparison results show the proposed model outperforms the benchmark models that ignore exciting effects among advertisement clicks. Using a proprietary data set, we find that display advertisements have relatively low direct effect on purchase conversion, but they are more likely to stimulate subsequent visits through other advertisement formats. We show that the commonly used measure of conversion rate is biased in favor of search advertisements and underestimates the conversion effect of display advertisements the most. Our model also furnishes a useful tool to predict future purchases and advertisement clicks for the purpose of targeted marketing and customer relationship management. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we use data envelopment analysis combined with principal component analysis to evaluate the efficiency of online retailers in search advertising. We examine various efficiency model specifications involving several resource and performance-related variables in search advertising. Our analysis based on 200 retailers suggests different efficiency patterns for multichannel and Web-only retailers. The results of our efficiency pattern analysis indicate that the performance metrics, impressions, online sales, click-through rate, and conversion rate together reveal differences in efficiency mainly for multichannel retailers. On the other hand, ad positions in sponsored and organic links reveal differences in efficiency for Web-only retailers. In terms of overall efficiency, we find that multichannel retailers occupy relatively most of the top positions. These results contribute to organizational level understanding of search advertising practices in online retailing and offer insights into keyword management, resource utilization, and performance metrics in search advertising campaigns. [ABSTRACT FROM AUTHOR]

This paper analyzes how the presence of organic listing as a competing information source affects advertisers' sponsored bidding and the equilibrium outcomes in search advertising. We consider a game-theoretic model in which two firms bid for sponsored advertising slots provided by a monopolistic search engine and then compete for consumers in price in the product market. Firms are asymmetrically differentiated in market preference and are given different exposure in organic listing aligned with their market appeal. We identify two aspects of a firm's sponsored bidding incentive, namely, the promotive and the preventive incentives. The presence of organic listing alters firms' sponsored bidding incentives such that the stronger firm has primarily preventive incentive, whereas the weaker has mainly promotive incentive. We show that the preventive incentive decreases and the promotive incentive increases as the difference in firms' market appeal decreases, and as a result, even the weaker firm may outbid the stronger competitor under such a co-listing setting. We further examine how the presence of organic listing affects the equilibrium outcomes by comparing it with a benchmark case in which there is only a sponsored list. We show that the differentiated exposure in the organic list gives the weaker advertiser chances to win a better sponsored position, which improves the overall information structure the search engine provides. As a result, the equilibrium social welfare, sales diversity, and consumer surplus increase. Although the presence of the free exposure from the organic list may reduce advertisers' sponsored bidding incentive per se, the overall effect benefits the search engine's growth in the long run. [ABSTRACT FROM AUTHOR]

Budget optimization is one of the primary decision-making issues faced by advertisers in search auctions. A quality budget optimization strategy can significantly improve the effectiveness of search advertising campaigns, thus helping advertisers to succeed in the fierce competition of online marketing. This paper investigates budget optimization problems in search advertisements and proposes a novel hierarchical budget optimization framework (BOF), with consideration of the entire life cycle of advertising campaigns. Then, we formulated our BOF framework, made some mathematical analysis on some desirable properties, and presented an effective solution algorithm. Moreover, we established a simple but illustrative instantiation of our BOF framework which can help advertisers to allocate and adjust the budget of search advertising campaigns. Our BOF framework provides an open testbed environment for various strategies of budget allocation and adjustment across search advertising markets. With field reports and logs from real-world search advertising campaigns, we designed some experiments to evaluate the effectiveness of our BOF framework and instantiated strategies. Experimental results are quite promising, where our BOF framework and instantiated strategies perform better than two baseline budget strategies commonly used in practical advertising campaigns. [ABSTRACT FROM PUBLISHER]

In search advertising, a search engine uses a generalized second-price auction to sell advertising slots adjacent to search results on its webpage. In this paper, we study an interesting question related to the design of the generalized second-price auction: how should a search engine strategically decide on the number of advertising slots? To answer this question, we analyze the implication of varying the number of slots in a base model in which the click-through rates are assumed to be independent of the number of slots. When deciding the number of slots, we find that a search engine's profit is based on two counteracting factors: the incremental clicks from an extra slot and the influence of the extra slot on advertisers' payments per click. Our analysis characterizes the conditions for optimality of the number of slots and the implications of different distributions for advertiser valuations. We also extend the base model to allow for attraction and cannibalization of clicks from existing slots by new ad slots and show how such effects affect the optimal number of slots. Our overall results show that search engines need to optimize the number of ad slots offered for auction in order to maximize profit. [ABSTRACT FROM AUTHOR]

Firms that sell via a direct channel and via indirect channels have to decide whether to allow third-party sellers to use trademarked brand names of products in their advertising. This question has been particularly controversial for advertising on search engines. In June 2009, Google started allowing any third-party reseller of a product to use a trademark such as "DoubleTree" in the text of its ad, even if the reseller did not have the trademark holder's permission. We study the effects of this change empirically within the hotel industry. We find some evidence that allowing third-party sellers to use a trademark in their online search advertising weakly reduced the likelihood of a consumer clicking on a trademark holder's paid search ads. However, the decrease in paid clicks was outweighed by a large increase in consumers clicking on the unpaid links to the hotelier's website within the main search results. Our evidence shows that when a third-party seller focuses on a trademarked brand in its ads, the ads become less distinct, and customers are more likely to ignore the advertised offers and buy from the direct channel. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the role of frustration in online search endeavors. The research applies precepts from frustration theory to investigate the nature of search goals for hedonic vs. utilitarian product and the moderating influence of encountered frustration during online searches on attitude toward keyword search advertising (A Ad), attitude toward searched brand (A brand), purchase intention (PI), and attitude toward search engine effectiveness. An experiment with a pretest was conducted. The results of the pretest suggest that hedonic product searches associate with the goal of finding relevant search results, while utilitarian product searches relate to time saving. The findings of the experiment indicate that the influence of hedonic vs. utilitarian search on A Ad and search engine effectiveness is moderated by the level of frustration. The present research contributes to the substantive and conceptual domains of frustration theory, Internet search advertisements, search engine effectiveness, and electronic commerce. [ABSTRACT FROM AUTHOR]