A large share of all online display advertisements (ads) are never seen by a human. For instance, an ad could appear below the page fold, where a user never scrolls. Yet, an ad is essentially ineffective if it is not at least somewhat viewable. Ad viewability - which refers to the pixel percentage-in-view and the exposure duration of an online display ad - has recently garnered great interest among digital advertisers and publishers. However, we know very little about the impact of ad viewability on advertising effectiveness. We work to close this gap by analyzing a large-scale observational data set with more than 350,000 ad impressions similar to the data sets that are typically available to digital advertisers and publishers. This analysis reveals that longer exposure durations (>10 seconds) and 100% visible pixels do not appear to be optimal in generating view-throughs. The highest view-through rates seem to be generated with relatively lower pixel/second-combinations of 50%/1, 50%/5, 75%/1, and 75%/5. However, this analysis does not account for user behavior that may be correlated with or even drive ad viewability and may therefore result in endogeneity issues. Consequently, we manipulated ad viewability in a randomized online experiment for a major European news website, finding the highest ad recognition rates among relatively higher pixel/second-combinations of 75%/10, 100%/5 and 100%/10. Everything below 75% or 5 seconds performs worse. Yet, we find that it may be sufficient to have either a long exposure duration or high pixel percentage-in-view to reach high advertising effectiveness. Our results provide guidance to advertisers enabling them to establish target viewability rates more appropriately and to publishers who wish to differentiate their viewability products.
Between 40% and 50% of online ads served by publishers are actually never seen by Internet users, resulting in ineffective branding campaigns and a considerable waste of money for advertisers. In reaction, more and more advertisers use technologies to measure the viewability of advertising campaigns on publisher websites. This paper provides the first comprehensive economic analysis of the impact of the adoption of such technologies on the economics of online advertising. We construct a two-sided market model for advertising where publishers manage their website to attract Internet users and advertisers. We show that the adoption of ad viewability technology affects the number of viewable ads displayed by publishers, the price of ads and publisher profits, and user experience. We finally analyze the total welfare impact of ad viewability and examine how ad-blockers constrain publishers from both sides of the market.
Viewability is a valuable factor for advertising campaigns. Brands are vying for better visibility in digital media to enhance their awareness and acceptance. In these times of calculated investments, it is still observed that many brands lose on millions of dollars every year on a dubious adventure. This adventure is none other than advertising wherein 56% of the ads that advertisers post aren't even seen by people. Here it is where the advertisers should consider the effectiveness of ad viewability, a measure to know the reach of their ads, which in return can scale the success of ad campaign. The paper encapsulates the research findings based on the factors like banner blindness, brand image, perceived usefulness, etc to find the best position for ad placement. Further, the paper provides the critical insights using the above data which can optimize the amount of investment that a company needs to do in advertisements for better profits from viewable ads and also help the company boost conversion ra.
Digital marketing changes the dynamics of traditional routes to market, augments conversations and facilitates the measurement of activities by organisations and consumers alike. This Handbook strives to advance the study and understanding of this domain and provides a digital marketing journey that flows from methods and methodologies. It moves from the fundamentals to the different aspects of digital marketing strategy, tactics, metrics and management, and ethics. This Handbook brings together the critical factors in digital marketing as the essential reference set for researchers in this area of continued growth. It is essential reading for postgraduate students, researchers, and practitioners in a range of disciplines exploring digital marketing. Part 1: Foundations of Digital Marketing Part 2: Methodologies and Theories in Digital Marketing Part 3: Channels and Platforms in Digital Marketing Part 4: Tools, Tactics and Techniques in Digital Marketing Part 5: Management and Metrics in Digital Marketing Part 6: Ethical Issues in Digital Marketing
This research is the first to address this important problem of ad viewability prediction. Inspired by the standard definition of viewability, this study proposes to solve the problem from two angles: 1) scrolling behavior and 2) dwell time. In the first phase, ad viewability is predicted by estimating the probability that a user will scroll to the page depth where an ad is located in a specific page view. Two novel probabilistic latent class models (PLC) are proposed. The first PLC model computes constant use and page memberships offline, while the second PLC model computes dynamic memberships in real-time. In the second phase, ad viewability is predicted by estimating the probability that the page depth will be in-view for certain seconds. Machine learning models based on Factorization Machines (FM) and Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) are proposed to predict the viewability of any given page depth in a specific page view. The experiments show that the proposed algorithms significantly outperform the comparison systems.
Robert East presents evidence on successful advertising campaigns where the brand benefits from more sales and higher prices, and he describes how good advertising can sometimes reduce the cost of doing business. The question of repeated exposure is examined: do sales initially gather pace with additional ad exposures, or do the gains get less and less after the first exposure? New evidence on this issue is assessed. The focus then moves to a model of ad response that covers the evidence on repeated ad exposure and explains how advertising may work over both short-term and long-term periods. The processes that could produce the long-term effect are discussed and new evidence is presented on the function of word of mouth. There is a chapter on the psychological processes that are used to explain ad effect and brief sections on the point of purchase and online advertising.
The increasing availability of individual-level data has raised the standards for measurability and accountability in digital advertising. Using a massive individual-level data set, our paper captures the effectiveness of display advertising across a wide range of consumer behaviors. Two unique features of our data set that distinguish this paper from prior work are: (i) the information on the actual viewability of impressions and (ii) the duration of exposure to the display advertisements, both at the individual-user level. Employing a quasi-experiment enabled by our setting, we use difference-in-differences and corresponding matching methods as well as instrumental variable techniques to control for unobservable and observable confounders. We empirically demonstrate that mere exposure to display advertising increases users' propensity to search for the brand and the corresponding product; consumers engage both in active search exerting effort to gather information, and in passive search using information sources that arrive exogenously. We also find statistically and economically significant effect of display advertising on increasing consumers' propensity to make a purchase. Furthermore, our findings reveal that the longer the duration of exposure to display advertising, the more likely the consumers are to engage in direct search behaviors (e.g., direct visits) rather than indirect ones (e.g., search engine inquiries). We also study the effects of various types of display advertising (e.g., prospecting, retargeting, affiliate targeting, video advertising, etc.) and the different goals they achieve. Our framework for evaluating display advertising effectiveness constitutes a stepping stone towards causally addressing the digital attribution problem.
This thesis investigates the effects of online advertising on human behavior: clicks, new-account sign-ups, and retail sales. Five chapters cover natural and field experiments used to measure these effects for both display and search advertising. The first chapter uses a natural experiment on the Yahoo! Front Page, aided by a flexible semiparametric model, to identify the causal effects of display ad frequency on internet users' responses as measured at the individual level by clicks and new-account sign-ups. Performance is heterogeneous regarding frequency and clickability; some campaigns exhibit significant decreasing returns to scale after one or two impressions while others show constant returns to scale even after fifty impressions. For some campaigns, a simple nonparametric regression which ignores selection bias finds increasing returns to scale, but none is found with the model that uses exogenous variation in views. Conversely, many campaigns that appear to exhibit diminishing returns when failing to account for selection, in fact, show little to no wear-out. The second chapter assesses the ability of online display advertising to attract new customers by analyzing a large-scale field experiment which exposed 3.7 million subjects to ads on Yahoo!. The number of new account sign-ups at an online business was tracked and shows a statistically significant impact of one of the two types of advertising campaigns. The ads served as Yahoo! run-of-network succeeded in generating a statistically significant increase in sign-ups of 8-14% relative to the control group. The ads shown on Yahoo! Mail did not produce a statistically significant increase in sign-ups. Despite being derived using millions of subjects, this estimate is quite noisy, with the upper bound of the 95% confidence interval estimate being a 15% increase in new customers. These estimates call into question click-only attribution models, as the number of users that clicked on an ad and converted is less than 30% of the estimated treatment effect. The third chapter asks, "Does advertising affect sales in a measurable way?" New technologies for tracking both sales and advertising at the individual level are used to investigate the effectiveness of brand advertising for a nationwide retailer. A controlled experiment on 1,577,256 existing customers measures the causal effect of advertising on actual purchases, overcoming the major hurdles regarding attribution typically encountered in advertising effectiveness research by exogenously varying exposure to the ads. Each customer was randomly assigned to treatment and control groups for an online advertising campaign for this retailer. Online brand advertising generated a statistically and economically significant effect on in-store sales for this retailer. The design of the experiment permits a demographic breakdown of the advertising's heterogeneous effects. Somewhat surprisingly, the effects are especially large for the elderly. Customers aged 65 and older, comprising only 5% of the experimental subjects, exhibited a 20% average increase in sales due to the advertising campaign, which represents 40% of the total effect among all age groups. The fourth chapter further investigates the effects of online advertising on sales. A quasi experimental approach is taken to analyze the randomized experiment in Chapter 3. Individual level data on ad exposure and weekly purchases at this retailer, both online and in stores, are combined and used to find statistically and economically significant impacts of the advertising on sales. The treatment effect persists for weeks after the end of an advertising campaign, and the total effect on revenues is estimated to be more than seven times the retailer's expenditure on advertising during the study. Additional results explore differences in the number of advertising impressions delivered to each individual, online and offline sales, and the effects of advertising on those who click the ads versus those who merely view them. The fifth chapter quantifies the externalities exerted among competing north ads in search advertising. "North" ads, or sponsored listings appearing just above the organic search results, generate the majority of clicks and revenues for search engines. The research question asks, "Does increasing the number of rival north ads decrease the number of clicks I receive on my own north ad?" A controlled experiment investigates this question and finds, surprisingly, that additional rival ads in the north tend to increase rather than decrease the click-through rate (CTR) of the top sponsored listing. Several possible explanations are proposed for this phenomenon, and directions for new theoretical models of sponsored search are suggested.
