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The value of promotional marketing and word-of-mouth (WOM) is well recognized, but few studies have compared the effects of these two types of information in online settings. This research examines the effect of marketing efforts and online WOM on product sales by measuring the effects of online coupons, sponsored keyword search, and online reviews. It aims to understand the relationship between firms' promotional marketing and WOM in the context of a third party review platform. Using a three-year panel data set from one of the biggest restaurant review websites in China, the study finds that both online promotional marketing and reviews have a significant impact on product sales, which suggests promotional marketing on third party review platforms is still an effective marketing tool. This research further explores the interaction effects between WOM and promotional marketing when these two types of information coexist. The results demonstrate a substitute relationship between the WOM volume and coupon offerings, but a complementary relationship between WOM volume and keyword advertising.

The authors examine prominent placement of search engines' own services and effects on users' choices. Evaluating a natural experiment in which different results were shown to users who performed similar searches, they find that Google's prominent placement of its Flight Search service increased the clicks on paid advertising listings by more than half while decreasing the clicks on organic search listings by about the same quantity. This effect appears to result from interactions between the design of search results and users' decisions about where and how to focus their attention: users who decide what to click on the basis of relevance were more likely to select paid listings, whereas users who are influenced by visual presentation and page position were more likely to click on Google's own Flight Search listing. The authors consider implications of these findings for competition policy and for online marketing strategies.

We study the bidding strategies of vertically differentiated firms that bid for sponsored search advertisement positions for a keyword at a search engine. We explicitly model how consumers navigate and click on sponsored links based on their knowledge and beliefs about firm qualities. Our model yields several interesting insights; a main counterintuitive result we focus on is the \"position paradox.\" The paradox is that a superior firm may bid lower than an inferior firm and obtain a position below it, yet it still obtains more clicks than the inferior firm. Under a pay-per-impression mechanism, the inferior firm wants to be at the top where more consumers click on its link, whereas the superior firm is better off by placing its link at a lower position because it pays a smaller advertising fee, but some consumers will still reach it in search of the higher-quality firm. Under a pay-per-click mechanism, the inferior firm has an even stronger incentive to be at the top because now it only has to pay for the consumers who do not know the firms' reputations and, therefore, can bid more aggressively. Interestingly, as the quality premium for the superior firm increases, and/or if more consumers know the identity of the superior firm, the incentive for the inferior firm to be at the top may increase. Contrary to conventional belief, we find that the search engine may have the incentive to overweight the inferior firm's bid and strategically create the position paradox to increase overall clicks by consumers. To validate our model, we analyze a data set from a popular Korean search engine firm and find that (i) a large proportion of auction outcomes in the data show the position paradox, and (ii) sharp predictions from our model are validated in the data.

Sponsored search advertising is ascendant-Forrester Research reports expenditures rose 28% in 2007 to $8.1 billion and will continue to rise at a 26% compound annual growth rate [VanBoskirk, S. 2007. U.S. interactive marketing forecast, 2007 to 2012. Forrester Research (October 10)], approaching half the level of television advertising and making sponsored search one of the major advertising trends to affect the marketing landscape. Yet little empirical research exists to explore how the interaction of various agents (searchers, advertisers, and the search engine) in keyword markets affects consumer welfare and firm profits. The dynamic structural model we propose serves as a foundation to explore these outcomes. We fit this model to a proprietary data set provided by an anonymous search engine. These data include consumer search and clicking behavior, advertiser bidding behavior, and search engine information such as keyword pricing and website design. With respect to advertisers, we find evidence of dynamic bidding behavior. Advertiser value for clicks on their links averages about 26 cents. Given the typical $22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.2%, well within common estimates of 1%-2% [Narcisse, E. 2007. Magid: Casual free to pay conversion rate too low. GameDaily.com (September 20)]. With respect to consumers, we find that frequent clickers place a greater emphasis on the position of the sponsored advertising link. We further find that about 10% of consumers do 90% of the clicks. We then conduct several policy simulations to illustrate the effects of changes in search engine policy. First, we find the search engine obtains revenue gains of 1% by sharing individual-level information with advertisers and enabling them to vary their bids by consumer segment. This also improves advertiser revenue by 6% and consumer welfare by 1.6%. Second, we find that a switch from a first- to second-price auction results in truth telling (advertiser bids rise to advertiser valuations). However, the second-price auction has little impact on search engine profits. Third, consumer search tools lead to a platform revenue increase of 2.9% and an increase of consumer welfare by 3.8%. However, these tools, by reducing advertising exposures, lower advertiser profits by 2.1%.

Our main objective in this paper is to measure the value of customers acquired from Google search advertising accounting for two factors that have been overlooked in the conventional method widely adopted in the industry: (1) the spillover effect of search advertising on customer acquisition and sales in off-line channels and (2) the lifetime value of acquired customers. By merging Web traffic and sales data from a small-sized U.S. firm, we create an individual customer-level panel that tracks all repeated purchases, both online and off-line, and tracks whether or not these purchases were referred from Google search advertising. To estimate the customer lifetime value, we apply the methodology in the customer relationship management literature by developing an integrated model of customer lifetime, transaction rate, and gross profit margin, allowing for individual heterogeneity and a full correlation of the three processes. Results show that customers acquired through Google search advertising in our data have a higher transaction rate than customers acquired from other channels. After accounting for future purchases and spillover to off-line channels, the calculated value of new customers using our approach is much higher than the value obtained using conventional method. The approach used in our study provides a practical framework for firms to evaluate the long-term profit impact of their search advertising investment in a multichannel setting.

Budget-related decisions in sponsored search auctions are recognized as a structured decision problem rather than a simple constraint. Budget planning over several coupled campaigns (e.g., substitution and complementarity) remains a challenging but important task for advertisers. In this paper, we propose a dynamic multicampaign budget planning approach using optimal control techniques, with consideration of the substitution relationship between advertising campaigns. A three-dimensional measure of substitution relationships between campaigns is presented, namely, the overlapping degree in terms of campaign contents, promotional periods, and target regions. We also study some desirable properties and possible solutions to our budget model. Computational simulations and experiments are conducted to evaluate our model using real-world data from practical campaigns in sponsored search auctions. Experimental results show that (1) our approach outperforms the baseline strategy that is commonly used in practice; (2) coupled campaigns with a higher overlapping degree in between reduce the optimal total budget level, then reduce the optimal payoff, and reach the budgeting cap earlier than those with a less overlapping degree; and (3) the advertising effort can be seriously weakened by ignoring the degree of overlapping between campaigns.

Over the past 15 years, digital media platforms have revolutionized marketing, offering new ways to reach, inform, engage, sell to, learn about, and provide service to customers. As a means of taking stock of academic work's ability to contribute to this revolution, this article tracks the changes in scholarly researchers' perspectives on three major digital, social media, and mobile (DSMM) marketing themes from 2000 to 2015. The authors first use keyword counts from the premier general marketing journals to gain a macro-level view of the shifting importance of various DSMM topics since 2000. They then identify key themes emerging in five-year time frames during this period: (1) DSMM as a facilitator of individual expression, (2) DSMM as decision support tool, and (3) DSMM as a market intelligence source. In both academic research to date and corresponding practitioner discussion, there is much to appreciate. However, there are also several shortcomings of extant research that have limited its relevance and created points of disconnect between academia and practice. Finally, in light of this, an agenda for future research based on emerging research topics is advanced.

This article examines a model in which advertisers bid for \"sponsored-link\" positions on a search engine. The value advertisers derive from each position is endogenized as coming from sales to a population of consumers who make rational inferences about firm qualities and search optimally. Consumer search strategies, equilibrium bidding, and the welfare benefits of position auctions are analyzed. Implications for reserve prices and a number of other auction design questions are discussed.

In this paper, we explore how keyword ambiguity can affect search advertising performance. Consumers arrive at search engines with diverse interests, which are often unobserved and nontrivial to predict. The search interests of different consumers may vary even when they are searching using the same keyword. In our study, we propose an automatic way of examining keyword ambiguity based on probabilistic topic models from machine learning and computational linguistics. We examine the effect of keyword ambiguity on keyword performance using a hierarchical Bayesian approach that allows for topic-specific effects and nonlinear position effects, and jointly models click-through rate (CTR) and ad position (rank). We validate our study using a novel data set from a major search engine that contains information on consumer click activities for 2,625 distinct keywords across multiple product categories from 10,000 impressions. We find that consumer click behavior varies significantly across keywords, and such variation can be partially explained by keyword ambiguity. Specifically, higher keyword ambiguity is associated with higher CTR on top-positioned ads, but also a faster decay in CTR with screen position. Therefore, the overall effect of keyword ambiguity on CTR varies across positions. Our study provides implications for advertisers to improve the prediction of keyword performance by taking into account keyword ambiguity and other semantic characteristics of keywords. It can also help search engines design keyword planning tools to aid advertisers when choosing potential keywords.

Manipulation of social media affects perceptions of candidates and compromises decision-making. In the United States, social media sites—such as Facebook, Twitter, and YouTube—are currently being used by two out of three people ( 1 ), and search engines are used daily ( 2 ). Monitoring what users share or search for in social media and on the Web has led to greater insights into what people care about or pay attention to at any moment in time. Furthermore, it is also helping segments of the world population to be informed, to organize, and to react rapidly. However, social media and search results can be readily manipulated, which is something that has been underappreciated by the press and the general public.