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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.

Endorsement marketing has been widely used to generate consumer attention, interest and purchase decisions among targeted audiences. Internet celebrities who become famous on the Internet are dependent on strategic intimacy to appeal to their followers. Our study aims to examine how multiple exposures to Internet celebrity endorsements influence consumers’ click and purchase decisions in the context of influencer marketing.

Based on a unique and representative dataset, the authors first model consumers’ choices for clicks and purchases with two panel fixed-effect logit models linking clicks and purchases with the frequency of exposure to Internet celebrity endorsement. To further control the endogeneity produced by the intercorrelation between the click and purchase models, the authors also adopt the two-stage Heckman probit structure to jointly estimate the two models using Maximum Likelihood Estimation. Robustness checks confirm the effectiveness of the models.

The results suggest that Internet celebrity endorsement plays a significant role in bringing referral traffic to e-commerce sites but is less helpful in affecting conversion to sales. The impact of repetitive Internet celebrity endorsements on consumers’ click decisions is U-shaped, but the role of Internet celebrities as online retailers will “shape-flip” this relationship to a negative linear relation.

Our study is the first to investigate the repetitive exposure effect of Internet celebrity endorsement. The results show a contradictory pattern with a wear-out effect of repetition in the advertising literature. This is the first study to show how the endorsing self, which is a common business model in influencer marketing, moderates the effectiveness of influencer marketing.

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.

It seems that the Internet boom, which started at the end of the 1990s and finished with the spectacular collapse of the so-called dotcoms, is probably over. We are currently enjoying a period of fast and stable growth. This is manifested by the growing number of both Internet users and companies which – to an ever-increasing extent – use the Internet as a form of communication (both internal and external), promotion, sales etc. Expenditures on Internet advertising are growing continuously and now constitute more than 25% of all advertising expenditure. A natural consequence of this development is the need for the standardisation and organisation of the world of the Internet. These activities will result in a greater awareness of the benefits which this medium provides, increasing the possibilities of its use, and – most importantly – the opportunity to evaluate the return on investments made on the Internet. Nowadays, it is clear that many companies are striving to increase the quality of their activities on the Internet or to improve the effectiveness of such activities. As a consequence, the number of companies that look for indices which would enable the making of more precise and effective decisions in the scope of online operations is growing.

This chapter is dedicated to the phenomenon of the increasing role of the Internet in business, including the scale of its use by Polish and international companies. We present the most commonly used measures of marketing activities on the Internet and in social media. This group includes the indices which make it possible to determine whether a company actually needs a website. Other measures allow for the improvement in the effectiveness of the activity on the Internet, whereas others specify the costs of activities on the Internet and often serve as the basis for settlements between a company and advertising agencies or companies specialising in website design. It is worth emphasising that the Paid, Earned, Shared, Owned (PESO) model, worked out by Don Bartholomew,¹ is the basis for creation and description of indices concerning social media. This model has gained certain popularity in the social media industry. It does not, however, specify how individual indices should be named and calculated. It maps already existing indices and adapts them to specific levels of marketing communication measurement. All the measures indicated by the author of the model have been grouped into five major areas: exposure, engagement, brand awareness, action and recommendations. This model– similarly to all models of performance measurement – inspired by the sales funnel concept, adjusts certain standard indices and proposals of measurements for specific levels. Additionally, the measures are divided into four types, depending on who the owner of the content is: Paid (P) – refers to all forms of paid content; Owned (O) – all websites and web properties controlled by a company or brand; Earned (E) – the contents about a given brand created spontaneously by Internet users; and Shared (S) – the contents shared by Internet users.

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。

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

Very low click-through rates (CTR) raise serious questions about the effectiveness of banner advertisements. However, we believe that the effect of a banner ad is not limited by clicks. Banner ad information itself can be processed by the audience.

We propose that the exposure effect of a banner ad exists even when the banner is not clicked. The results of our experiments strongly support this effect. Analyses also revealed that a non-clicked banner ad can create as strong of an exposure effect as clicked banner ads. Also, audiences that are able to recall the existence of the banner ad on a web page develop stronger implicit memory than those who cannot. Researchers are invited to re-test these interesting findings in various cultures with differing levels of Internet penetration and experience.