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Few studies illustrate how contextual effects (e.g. assimilation and contrast) in pay-per-click ad design may impact consumers' attitudes and purchase intention. To fill this research gap, the authors provide theoretical predictions and empirical evidence on how ad design may prompt an assimilation and/or a contrast effect that may influence consumers' attitudes toward the ad and the brand and purchase intention. They also investigate whether the impact of contextual effects on consumers' decisions depends on the level of vividness in the ad.

A 2 (vividness: dynamic motion vs. static page) × 2 (information design: assimilation vs. contrast) × 2 (aesthetic design: assimilation vs. contrast) between-subjects experimental design is used to examine the effects of vividness, information design and aesthetic design. Conditional process analysis is used to assess the mediating role of attitudes toward the ad and the brand in the relationship between contextual effects and purchase intention.

For dynamic ads (i.e. high vividness) but not for static ads (i.e. low vividness), combined information contrast and aesthetic contrast designs generate a more favorable attitude toward the brand and a higher purchase intention than do combined information assimilation and aesthetic assimilation designs. Notably, combined information contrast and aesthetic contrast designs have the strongest effects than any other combination of assimilation and contrast designs of information and aesthetics. Attitudes toward the ad and the brand are significant mediators between contextual factors and intention to purchase.

The study examines the effectiveness of online ads from a new theoretical angle based on the attributes of pay-per-click ads.

The results suggest that when advertisers decide to use dynamic ads, they should adopt a contrast design for both the ad information and its aesthetics.

This study fills a research gap in the contextual effects literature, including providing evidence of an underlying process in the relationship between certain contextual effects and purchase intent. It also extends previous findings of assimilation/contrast in information design to aesthetics design and advances the literature on vividness by examining a moderation effect of vividness.

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.

Rob Griffin, senior vice president and U.S. director of search for Media Contacts, a communications consulting firm, is faced with the task of optimizing search engine marketing (SEM) for Air France. At the time of the case, SEM had become an advertising phenomenon, with North American advertisers spending $9.4 billion in the SEM channel, up 62% from 2005. Moving forward, Griffin wants to ensure that the team keeps its leading edge and delivers the results Air France requires for optimal Internet sales growth. The case centers upon Air France's and Media Contacts' efforts to find the ideal SEM campaign to provide an optimal amount of ticket sales in response to advertising dollars spent. This optimal search marketing campaign is based on choosing effective allocation of ad dollars across the various search engines, as well as selecting appropriate keywords and bid strategies for placement on the search result page for Internet users.

In determining the optimal strategy, the case presents background information on the airline industry as well as the Internet search options available at the time, including Google, Microsoft MSN, Yahoo!, and Kayak. Additionally, background information is provided on SEM and its associated costs and means of measuring the successfulness of each marketing effort. The case illustrates how one must first determine the key performance indicators for the project to guide analysis and enable comparison of various SEM campaigns. Cost per click and probability to produce a sale differ among publishers. Therefore, using a portfolio application model's quadrant positions can be used to determine optimal publisher strategies. Additionally, pivot tables help illustrate campaigns and strategies that have historically been most successful in meeting Air France's target Internet sales. Multiple recommendations on how Media Contacts can assist Air France in improving its SEM strategy can be derived from the data provided.

Students learn how to optimally leverage the Internet in generating customer sales in a cost-effective manner. Students will analyze and manipulate a variety of data using pivot tables to determine optimal strategies for obtaining maximum total online bookings through the various online channels available. Using a portfolio application model, students can determine an optimal publisher strategy and complete copy improvement analysis.

Smaller companies must continually review the pay‐per‐click (PPC) option or an organic listing on search engines. The purpose of this paper is to present a case study of a small manufacturing firm that is beginning to evaluate which search engine, Yahoo or Google, is more cost effective. Ultimately, management would like to identify if PPC advertising is worth the cost to a small company.

A one month's section of data from Yahoo and Google was examined. Patterns or indications as to which key word landed a better bid position was determined. Seven consecutive campaigns for click‐through rates (CTRs), average cost per click (CPC) and average position of keywords between the search engines Yahoo and Google were observed.

The average CPC was higher with Google. Kennedy Incorporated set a budget with Google and Yahoo to stay below a certain dollar limit, thus the total costs were the same. That would make the difference in the average CPC rather significant. Management also noticed a higher CTR on Yahoo than on Google. Thus it appears that Yahoo outperformed Google most of the time in the monthly samples.

Longer historical data need to be studied, to see if these patterns continue. Other statistics that would be interesting to examine are data that would reveal how CTR in Yahoo and Google are affecting conversions to sales.

It appears from this data that Yahoo is outperforming Google for Kennedy Incorporated with a better CTR and a lower variance in average position when listed on the screen by a return from a search. The company has the impression that Yahoo is a better company for PPC advertising when the marketing budget is small.

After studying and analyzing this case, students will be able to: understand and evaluate the integrated marketing communications (IMC) strategy for online businesses; understand and create the various components of an IMC plan; determine the challenges of scaling up a business and how social media marketing can help in that; learn how to assemble the positioning statement of a startup; determine and differentiate the different business models (revenue models); and understand and create the social media marketing strategy.

It was late April 2021, and Mr Srinivas Rao, the co-founder and director of Mentorrd EduTech, India, was contemplating the journey of more than five years. Much had changed since the startup Mentorrd Education Technologies Pvt. Ltd. (Mentorrd EduTech) was launched in 2015. From being an online artificial intelligence (AI)-based tool for analyzing and building resumes, Mentorrd expanded to specialized premium services for career development catering to MBA aspirants from India to international markets. However, during the same time, many problems had also started showing up. The competition had increased, and conversions became difficult. Mentorrd generated 95% of its leads through Google Ads pay per click (PPC) advertising. However, now, the increased competition made PPC costlier, where Mentorrd started at INR 10 per click but was now paying INR 50 per click. The customers also were only limited to searching for keywords; hence, actual lead conversion was minimal. Mentorrd was present on different social media platforms and had developed an audience: LinkedIn (close to 10,000 followers), Facebook (1,600+ followers) and Twitter (numbers), but conversions from there were only contributing to 5% of the total revenue.Mr Rao wondered that with a change in demand from general review services to specialist resume and interview preparation services such as MBA, he would have to develop a sound social media strategy integrated with the current Google Ads usage to reach the audience and generate conversions. He had a limited marketing budget which he wanted to use most effectively but was not sure how to spend across platforms.

This case can be used as an IMC strategy case for MBA courses in marketing. In a marketing course, the focus should be on components of IMC strategy, customer perception, company positioning and marketing channels. The case can also be applied as a strategy case for courses in entrepreneurship or digital marketing strategy. In an entrepreneurship course, the focus should be on the business model, growth and scaling decisions a startup needs to make to grow the business. In a digital marketing strategy course, the focus should be on social media marketing and search engine marketing decisions and the implications for revenues and profits. The case can also be used in executive-level courses to illustrate IMC, growth and digital marketing strategies for a startup.

Teaching notes are available for educators only.

CSS 8: Marketing.

The purpose of this paper is to illustrate the usefulness and results of applying web mining as extensions of data mining.

Web mining is performed using three selected software to databases related to customer survey, marketing campaign data, and web site usage. The three selected software are PolyAnalyst^® of Megaputer Intelligence, Inc., SPSS Clementine^®, and ClickTracks by Web Analytics.

This paper discusses and compares the web mining technologies used by the selected software as applied to text, web, and click stream data.

The limitations include the availability of databases and software to perform the web mining. The implications include that this methodology can be extended to other databases.

The methodology used in this paper could be representative of that used for managers to manage their relationships with customers, their marketing campaigns, and their web site activities.

PolyAnalyst is applied to analyze text data of actual written hotel comments. SPSS Clementine is applied to customer web data collected in response to several different marketing campaigns, including age, gender, and income. ClickTracks is applied to click‐stream data for Bob's Fruit web site to generate click fraud report, search report with revenues, pay‐per‐click, and search keywords for all visitors.

Analysis of the publisher's behavior plays a vital role in identifying fraudulent publishers in the pay-per-click model of online advertising. However, the vast amount of raw user click data with missing values pose a challenge in analyzing the conduct of publishers. The presence of high cardinality in categorical attributes with multiple possible values has further aggrieved the issue.

In this paper, gradient tree boosting (GTB) learning is used to address the challenges encountered in learning the publishers' behavior from raw user click data and effectively classifying fraudulent publishers.

The results demonstrate that the GTB effectively classified fraudulent publishers and exhibited significantly improved performance as compared to other learning methods in terms of average precision (60.5 %), recall (57.8 %) and f-measure (59.1%).

The experiments were conducted using publicly available multiclass raw user click dataset and eight other imbalanced datasets to test the GTB's generalizing behavior, while training and testing were done using 10-fold cross-validation. The performance of GTB was evaluated using average precision, recall and f-measure. The performance of GTB learning was also compared with eleven other state-of-the-art individual and ensemble classification models.