Internet advertising

Online advertising [100], as a multi-billion dollar business, provides a common marketing experience when people access online services using electronic devices, such as desktop computers, tablets, smartphones, and so on. Using the Internet as a means of advertising, different stakeholders take actions in the background to provide and deliver advertisements to users through numerous platforms, such as search engines, news sites, and social networks, where dedicated spots of areas are used to display advertisements (ads) along with search results, posts, or page content.
Online advertising is mainly based on dynamically selecting ads through a real-time bidding (or auction) mechanism. Predicting user responses like clicking ads in e-commerce platforms and internet-based advertising systems, as the first measurable user response, is an essential step for many digital advertising and recommendation systems to capture the user’s propensity to follow up actions, such as purchasing a product or subscribing to a service. To maximize revenue and user satisfaction, online advertising platforms must predict the expected user behavior of each displayed advertisement and maximize the user’s expectations of clicking [28]. Based on this observed feedback, these systems are tailored to user preferences to decide the order in that ads or any promoted content should be served to them. This objective provides an incentive to develop new research by using ideas derived from different domains like machine learning and data mining combined with models for information retrieval and mathematical optimization. They introduce different machine learning and data mining methods that employ deep learning-based predictive models to learn the representation of input features with the aim of user response prediction. Feature representation learning is known as a fundamental task on how to input information is going to be represented in machine learning models. A good feature representation learning method that seeks to learn low-dimensional embedding vectors is a key factor for the success of many downstream analytics tasks, such as click-through prediction and conversion prediction in recommendation systems and online advertising platforms.

Online display advertising intends to find the best match between advertise-
ment (ad) campaigns and online users, conditioned by user specific contexts such as
geographic locations, and hobbies etc. During this matching process, user behavior
plays a crucial role in determining whether and when the user, who has been served
the ad, will result in a conversion event. Advertisers seek to understand how users
behave if they are continuously served impressions from the same campaign, as well
as any noticeable patterns between campaign categorization and user behavior. This
thesis carries out data analytics to investigate correlation between user behavior and
campaign conversion rates (CVR), including click-through conversion rates and view-
through conversion rates. We investigate campaign categorization based on both
IAB categories, and campaign dfficulty level defined by effective CPA (eCPA). We
carry out large scale analytics over billions of impressions from over 1000 campaigns,
observing consistent patterns and significant findings.

This research examines how incorporating interactivity into online video
advertisements effects the following key marketing dependent variables: a) Involvement
with the Advertisement, b) Ad Recall, c) Attitude towards the website, d) Attitude
towards the Advertisement, e) Attitude towards the Brand, and f) Purchase Intention.
Deriving from past Interactivity research, three important facets of interactivity are
identified; User Control, Two-way Communication and Synchronicity. In order to test an
Internet based 2 (User Control: high or low) X 2 (two-way communication: high or low)
X 2 (synchronicity: high or low) between subjects experimental design, 8 different online
video platforms were created. The online video experiment was administered to
approximately 400 students in a large South-Eastern school.
Overall the findings regarding interactivity in online video advertising found no
significant effect of synchronicity on the dependent variables. There was however a significant interaction effect of user control and two-way communication on the
dependent variables. These interaction effects were examined further with a cell means
multiple comparison analysis. User control and two-way communication were found to
have a significant interaction effect on ad recall, purchase intention and attitude towards
the brand. User control had a significant effect on involvement and two-way
communication had a significant effect on attitude towards the website. There was no
effect of UC or TWC on attitude towards the ad.

Tommy Hilfiger is a popular fashion designer who incorporates a unique mix of style, patriotism and cultural diversity into the advertising and marketing of his products. But does his approach communicate true equality or divide Americans through race, class, and/or consumerism? Does his conceptualization of cultural diversity support ideals of multiculturalism or promote principles of assimilation? Tommy Hilfiger's official website, www.tommy.com, is analyzed from a cultural studies perspective to examine these questions. Political economic, cultural economic and semiotic theories are applied at the levels of production, text and consumption to reveal whether hegemonic or counter-hegemonic themes prevail. As a result, it is determined that hegemonic themes of white, Anglo-Americanism and consumerism do prevail, while counter-hegemonic messages of multiculturalism are less dominant. Findings also indicate that there is still much progress to be made in terms of achieving cultural equality in the United States.

Much of what we currently know about consumers' reaction to persuasive attempts (advertisements) comes from studying mass media communications which are largely one-way and image-centered. Internet-based advertising is neither given the focus on information and the ability to narrowly target such appeals (sites), as in direct marketing advertising. Given the emergence of Internet promotions, this is a substantial gap in our knowledge. This research seeks to both extend the boundaries of current direct marketing research (with its predominant focus on who will reply) and the domain of existing persuasion theories and models, attempting to gain a greater understanding of why consumers respond as they do to Internet advertising. By adapting the Elaboration Likelihood Model (ELM) to the Internet, this research attempts to determine if executional cues play a role in enhancing persuasiveness and the moderating effects of involvement. Additionally, this research draws on the substantial work on attitude towards the ad (A$\sb{\rm ad}$), including attempts to greater understand the antecedents of these attitudes and proposed distinctions between executional and content aspects of Internet ads, to see if this rich framework is useful in predicating outcomes for Internet advertising. This research has significant implications for practitioners as it looks beyond atheoretical testing-based insights of what people respond to, drawing on established theory to provide insights into why consumers respond as they do to Internet based advertising. Testing the adaptability of current persuasion theory to this emerging medium will do much to begin building a solid understanding of how Internet advertising works. Using an interactive computer-based simulation in a 2 (involvement) x 2 (argument strength) x 2 (peripheral cue: present or absent) between subject factorial design with 224 subjects the adaptability of existing persuasion research to the Internet was tested. Findings demonstrate the adaptability of the ELM to Internet advertising, as well as the important antecedent effects of attitudes towards Internet advertising on persuasion. However, the decomposition of A$\sb{\rm ad}$ proved less useful in this new domain. All told, much of what we currently know about persuasion is likely to prove useful in developing promotional campaigns for the Internet.

While academic research has begun to probe Internet users' underlying beliefs, attitudes, and purchase intent toward Internet advertising, there has been no attempt to develop an underlying theory comparing male versus female users' Internet versus print advertising content information processing. A logical place to begin examining Internet advertising is in the sphere of gender which is a critical factor in developing sales and marketing strategy and commonly used as a basis for market segmentation. Through the application of the selectivity model (Meyers-Levy 1989) and interactivity model (Bezjian-Avery, Calder, and lacobucci 1998), an attempt to predict gendered reactions to Internet versus traditional advertising is undertaken. The hypotheses drawn from the literature are tested using a 2 x 2 x 2 x 2 experimental design. The dependent variables examined are cognition, affect, and conation. The independent variables are gender, claim objectivity/subjectivity, perceived product risk category, and media. Results indicated a strong preference for objective versus subjective claims. Claim and product significantly impacted cognition and affect while gender and product significantly impacted conation. Several main and interaction effects were present and several hypotheses supported.

The purpose of this research is to determine whether optimal ad placement and page context can significantly impact advertising effects, by extending hemispheric processing theory. This study contributes to the marketing literature by 1) addressing theoretical conflicts regarding optimal hemispheric ad placement (more favorable effects with leftward photo ads and rightward text ads; Janiszewski 1988) and page context (matching activation from "priming" of opposing brain hemispheres Janiszewski 1990), 2) by evaluating multiple advertising effects in relation to mere exposure rather than focusing primarily on attitudes (Janiszewski 1988, 1990), and 3) by addressing an important knowledge gap regarding optimal Web advertising (Dahlen, Rasch and Rosengren 2003). A growing amount of money is being spent on Internet advertising, with revenues totaling $12.5 billion in 2005, up more than 30 percent over 2004 (IAB 2006). However, banner ad click-through rates are low (between .1 and .2 percent for standard ads; DoubleClick 2007) and only 10% of business executives believe that banner advertising is highly effective in generating new business (Forrester 2006). Advertisers continue to use banner ads, perhaps because the "branding" benefits are not limited to clickthroughs (Briggs and Hollis 1997). While numerous ad-related factors have been previously studied (e.g., ad context creative factors, recall/recognition effects, repetition), to the author's knowledge no research has examined the effect of banner ad placement on advertising outcomes such as attention, recognition, brand attitude and purchase intention.