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This purpose of this article is to solve the problem of bidding on keywords in newly set-up search engine advertising campaigns. Advertisers setting up search engine advertising campaigns for the first time need to place bids on keywords, but typically lack experience and data to determine ranks that maximize a keyword’s profit (generally referred to as a cold-start problem).

The authors suggest that advertisers collect data from the Google Keyword Planner to obtain precise estimates of the percentage increases in prices per click and click-through rates, which are needed to calculate optimal bids (exact approach). Together with the profit contribution per conversion and the conversion rate, the advertiser might then set bids that maximize profit. In case advertisers cannot afford to collect the required data, the authors suggest two proxy approaches and evaluate their performance using the exact approach as a benchmark.

The empirical study shows that both proxy approaches perform reasonably well, the easier approach to implement (Proxy 2) sometimes performs even better than the more sophisticated one (Proxy 1). As a consequence, advertisers might just use this very simple proxy when bidding on keywords in newly set-up search engine advertising campaigns.

This research extends the stream of literature on how to determine optimal bids, which so far focuses on campaigns that are already running and where the required data to calculate bids are already available. This research offers a novel approach of determining bids when advertisers lack the aforementioned information.

Explores one of the many exciting advertising research possibilities spawned by the Web, namely the efficacy of banner advertisements designed to lure the browser to an external Web page. Traditional advertising research usually relies on self‐report or memory. With Web advertisement banners, on the other hand, we can track actual behavior. In our pilot study, we demonstrate conclusively that click‐through rate, the percentage of visitors to a Web page clicking on an advertisement banner, can vary according to the advertisement copy. We also find that the imperative call for behavior, “Click here”, has a positive effect. These findings, using a new research method with a new medium, open the door to further advertising and communication research on Web advertisement banners.

To identify key success factors related to internet advertising, in the specific case of a portal directed at a teenage audience in Hong Kong; to derive a generally applicable formula for measuring the effectiveness of internet advertising.

Proposes a framework for advertising effectiveness that includes traditional objective measures (click‐through rates), subjective measures applied by advertisers, and seasonality corrections. Derives a mathematical formula for measurement of effectiveness. Extracts components from ten banner advertising campaigns at one web site between January 2001 and May 2002, and analyses them by means of proprietary data‐mining rule‐induction software. Selects two rules form the several generated, on the basis of confidence levels.

Finds that, in the case example studied: small interactive games and free gifts deliver the highest brand impression after normalization of the banner click‐through rate; revenue is significantly affected by local cultural and seasonal factors; “media‐rich” design is an important factor in attracting the target audience to click on a banner. Notes the importance of countering the natural variability of click‐through rates, and proposes a variety of design add‐ons to stabilise them.

The study relates to a very specific case example of internet advertising in one special administrative area of China. Caution is therefore indicated in applying the proposed model and formula or the related communication strategies more generally. Future research should investigate the conversion of muse‐clicks to sales.

Since, the cost to internet users of switching from one site to another is almost zero, competition for audiences is particularly severe, and planned marketing communication strategies essential to effective performance. In particular, continuing uncertainty about the cause‐effect link to sales threatens the revenue stream for firms that rely heavily on internet advertising. It is vital that such advertisers keep abreast of relevant research, such as the case study reported here.

The proposed formula for measurement of effectiveness is original in combining objective and subjective measures of effectiveness. The general principles underpinning strategic conclusions drawn in this particular situation could be selective applied by planners of internet advertising campaigns for other products and services to other target audiences.

Internet referral services are a common form of online marketing operating activities. To incentivize infomediaries and improve referral performance, brand retailers typically apply the cost-per-click (CPC) or the cost-per-sale (CPS) payments. The purpose of this paper is to investigate the effect of referral services on the optimal contract with CPC or CPS payments.

This paper studies a mechanism design problem for internet referral services. To maximize the expected utility of the brand retailer, an uncertain contract model is established in which the brand retailer's assessment of the infomediary's referral service capability is characterized as an uncertain variable. Then equivalent models under CPC and CPS payments are presented to obtain the optimal solutions.

The results demonstrate that under CPC payments, as the referral service capability increases, the optimal sales volume is increasing, and the optimal transfer payment first shows a declining and then a rising trend. The brand retailer is less likely to raise the optimal transfer payment for the infomediary given a higher CPC revenue-sharing fee percentage, which is counterintuitive. Under CPS payments, the optimal sales volume and transfer payment are also increasing in the referral service capability. In addition, an increase in the click-through rate leads to the infomediary's incremental marginal utility.

The value of this research is its application of incentive contracts to the internet referral services considering CPC or CPS payments. The results of this research can serve as a guide for retailers and infomediaries in their decision-making around online retailing.

When shopping for hotels online, consumers usually follow a sequential process of search, click-through and book. How to maximize consumer conversion on the path to purchase and prevent potential customers from giving up the online search remains an important topic to hotel marketers and online travel agents (OTAs). The purpose of this study is to understand how informational cues displayed in an online hotel search process, including quality indicators, brand affiliation, incentives (discounted price and promotion) and position in the search results, influence consumer conversion from one stage to another.

The authors collected clickstream data of hotel search from Expedia. The data include information on individual consumers’ click-through and booking, as well as events leading up to the conversions (or failure to convert) from search, click-through to book. It contains 940,164 hotels searched and displayed in 39,574 online search queries made by users in a regional US market between November 1, 2012 and June 20, 2013. The modeling strategy comprised the Heckman model and random effects model, which integrated sequential consumer behavior in different problem-solving stages while accounting for heterogeneity across different hotels online.

The authors find that consumers rely on informational cues displayed online to make decisions about hotel booking. Specifically, consumers tend to click through hotels with higher consumer-generated ratings and industry-endorsed ratings. However, they tend to rely on consumer-generated ratings rather than industry-endorsed ratings when committing to a booking. Moreover, consumers are strongly responsive to incentives (discounted price and promotion) when clicking-through and booking a hotel. Finally, the likelihood of consumer conversions from search to click-through and booking is higher for hotels with brand affiliation and higher positions in the search results.

This research provides critical managerial implications of online search for hotel marketers and OTAs. The results inform hotel marketers and OTAs on how consumers respond to informational cues displayed in their search process and how these informational cues influence consumer conversion from one stage to another. The sequential problem-solving process of search, click-through and booking disclosed in this study also helps hotel marketers to identify customer conversion opportunities using effective informational cues.

当在线酒店预定时, 消费者往往遵循一系列流程, 搜索, 点击查询, 到最后预定。对于酒店营销商和线上旅游社（OTAs）来说, 如何最大化提高消费转化, 使得消费者不会半途中断, 最后预定酒店, 是一个重要话题。本论文的研究目的就是理解酒店在线搜索过程中, 信息线索如何影响每个阶段的消费转化, 其中涉及的因素有：信息质量、品牌、激励（折扣和促销）、以及搜索结果排名等。

研究样本数据采集于Expedia酒店搜索点击流。其中包括个人消费者点击和预定信息、以及由搜索、点击查询到预定过程中的消费转化（或者中途转化失败）的各种事件。样本容量包括940,164家酒店, 其涉及到由美国局部市场消费者在2012年11月1日到2013年6月20日之间做出的39,574条搜索结果。 我们采用Heckman模型和随机效应模型来整合不同线性时间上的消费者行为, 同时考虑不同酒店的多样性。

研究发现消费者使用在线信息线索来做酒店预订决策。具体来说, 消费者倾向于对于消费者评价高和行业认证高的酒店进行点击查询。然而, 相比行业认证, 消费者更倾向于借鉴消费者评价, 来做出最后预定决策。此外, 在点击查询和预定时, 消费者对于激励（折扣和促销）反应强烈。最后, 品牌和搜索排名靠前的酒店往往获得从搜索、点击查询到最后预定中更高的消费转化率。

本论文对酒店营销商和OTAs有重要的在线搜索启示。研究结果向酒店营销商和OTAs证明消费者在搜索过程中对信息线索如何反应, 以及这些信息线索如何影响每个阶段之间的消费转化。本论文展示的从搜索、点击查询、到预定的线性决策过程对于酒店营销商们有着重大帮助, 帮助其使用信息线索找出各种消费转化机遇。

This study aims to analyze whether average video watch time or click-through rates (CTR) on YouTube videos are more closely associated with high numbers of views per subscriber using linear regressions.

In 2018, YouTube began releasing CTR data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. To the best of the author’s knowledge, this is the first academic study looking at that CTR data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube?

The author analyzed new video releases on YouTube. This paper finds almost no or limited evidence that higher percent audience retention or total average watch time per view, respectively, are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views.

The author knows no other study that tests the relative importance of CTR or watch time per view in predicting views for new videos on YouTube.

This study aims to examine the effect of cybersecurity threat and efficacy upon click-through, response to a phishing attack: persuasion and protection motivation in an organizational context.

In a simulated field trial conducted in a financial institute, via PhishMe, employees were randomly sent one of five possible emails using a set persuasion strategy. Participants were then invited to complete an online survey to identify possible protective factors associated with clicking and reporting behavior (N = 2,918). The items of interest included perceived threat severity, threat susceptibility, response efficacy and personal efficacy.

The results indicate that response behaviors vary significantly across different persuasion strategies. Perceptions of threat susceptibility increased the likelihood of reporting behavior beyond clicking behavior. Threat susceptibility and organizational response efficacy were also associated with increased odds of not responding to the simulated phishing email attack.

This study again highlights human susceptibility to phishing attacks in the presence of social engineering strategies. The results suggest heightened awareness of phishing threats and responsibility to personal cybersecurity are key to ensuring secure business environments.

The authors extend existing phishing literature by investigating not only click-through behavior, but also no-response and reporting behaviors. Furthermore, the authors observed the relative effectiveness of persuasion strategies used in phishing emails as they compete to manipulate unsafe email behavior.

This paper aims to develop and test a method of automating, for online retailers, the practice of adaptive selling, which is typically used by salespeople in face-to-face interactions. This method customizes persuasive messages for individual customers as they navigate a retailer’s website.

This paper demonstrates a method for the online implementation of automated adaptive selling using sales influence tactics. Automated adaptive selling is compared to nonadaptive selling in three e-commerce field studies.

The results reveal that adaptive selling is more effective than nonadaptive selling. The click-through rates increased significantly when adaptive selling was used.

This paper highlights the effectiveness of existing theories concerning adaptive human-to-human selling and their utility to online selling. The authors demonstrate the added value of adaptive selling in e-commerce, thereby opening up a novel area of research into adaptive selling online. While the paper focuses on the adjustment of sales influence tactics, other factors could be investigated for adjustment in future research (e.g. prices).

The methods, described in detail, are readily available for implementation by online retailers. The implementations are timely and increasingly valuable as e-commerce expands into interpersonal channels (e.g. instant messengers and social media).

To the authors’ knowledge, this paper is the first to formally implement automated adaptive selling as described in the ISTEA model in an e-commerce setting.

The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.

Consumer sequential online click data, collected from JD.com, is used to analyze the dynamic competitive relationship between brands. It is found that the competition intensity across categories of products can differ considerably. Consumers exhibit big differences in purchasing time of durable-like goods, that is, the purchasing probability of such products changes considerably over time. The local polynomial regression model (LPRM) is used to analyze the relationship between brand competition of durable-like goods and the purchasing probability of a particular brand.

The statistical results of collective behaviors show that there is a 90/10 rule for the category durable-like goods, implying that ten percent of the brands account for 90 percent market share in terms of both clicking and purchasing behavior. The dynamic brand cognitive process of impulsive consumers displays an inverted V shape, while cautious consumers display a double V shaped cognitive process. The dynamic consumers’ cognition illustrates that when the brands capture a half of the click volume, the brands’ competitiveness reaches to its peak and makes no significant different from brands accounting for 100 percent of the click volume in terms of the purchasing probability.

There are some limitations to the research, including the limitations imposed by the data set. One of the most serious problems in the data set is that the collected click-stream is desensitized severely, restricting the richness of the conclusions of this study. Second, the data set consists of many other consumer behavioral data, but only the consumer’s clicking behavior is analyzed in this study. Therefore, in future research, the parameters brand browsing by consumers and the time of browsing in each brand should be added as indicators of brand competitive intensity.

The authors study brand competitiveness by analyzing the relationship between the click rate and the purchase likelihood of individual brands for durable-like products. When the brand competitiveness is less than 50 percent, consumers tend to seek a variety of new brands, and their purchase likelihood is positively correlated with the brand competitiveness. Once consumers learn about a particular brand excessively among all other brands at a period of time, the purchase likelihood of its products decreases due to the thinner consumer’s short-term loyalty the brand. Till the brand competitiveness runs up to 100 percent, consumers are most likely to purchase a brand and its product. That indicates brand competitiveness maintain 50 percent of the whole market is most efficient to be profitable, and the performance of costing more to improve the brand competitiveness might make no difference.

There are many studies on brand competition, but most of these research works analyze the brand’s marketing strategy from the perspective of the company. The limitation of this research is that the data are historical and failure to reflect time-variant competition. Some researchers have studied brand competition through consumer behavior, but the shortcoming of these studies is that it does not consider sequentiality of consumer behavior as this study does. Therefore, this study contributes to the literature by using consumers’ sequential clicking behavior and expands the perspective of brand competition research from the angle of consumers. Simultaneously, this paper uses the LPRM to analyze the relationship between consumer clicking behavior and brand competition for the first time, and expands the methodology accordingly.

The purpose of this paper is to focus specifically on the examination of factors influencing the effectiveness of product placement in social media.

Two field experiments were used to test research models and questions. In each experiment, random sampling was used to assign volunteers into groups, controlled by different experimental settings. Questionnaires were distributed to the volunteers in order to collect their attitude toward advertisement, brand impression, and intention to click the advertisement. Their browsing behavior was measured by click through rate, browse depth, and browsing time.

The paper found that the effects of product placement conducts (product prominence and presentation) in social media are similar to the effects of product placement in other media. Also, a match between the vehicle and product would create deeper browsing depth and longer browsing time on the product web site. Product placement on a higher awareness vehicle would be more effective for marketing.

This study makes its contribution in understanding the effectiveness of product placement in social media on delivering a marketing message. The results further highlight the importance of “integrating” the product placement content with its context and its vehicle.