2021-suarez.pdf: “Does ad blocking have an effect on online shopping?”, (2021-04-01; ):
- E-commerce and online advertisement are growing trends.
- The overall impact of ad blockers is unclear.
- Using survey data, the effect of ad blocker use on online purchases is quantified.
- The analysis reveals a positive effect of ad blocker use on e-commerce.
- In the light of the results stakeholders should consider if the present online ads formats are the most suitable.
The use of ad blocking software has risen sharply with online advertising and is recognized as challenging the survival of the ad supported web. However, the effects of ad blocking on consumer behavior have been studied scarcely.
This paper uses propensity score matching techniques on a longitudinal survey of 4411 Internet users in Spain to show that ad blocking has a causal positive effect on their number of online purchases. This could be attributed to the positive effects of ad blocking, such as a safer and enhanced navigation.
This striking result reinforces the controversial debate of whether current online ads are too bothersome for consumers.
[Keywords: Ad blockers, advertising avoidance, e-commerce, propensity score matching]
…This study employs a rich dataset coming from a longitudinal survey. The source of the data is a survey conducted by the Spanish Markets and Competition Authority on the same sample of interviewees in the 4th quarter of 2017 and in the second quarter of 2018 ([dataset]CNMCData, 2019). The sample was designed to be representative of the population living in private households in Spain. The information was provided by 4411 Internet users ≥16 years old. At the baseline time point (fourth quarter of 2017) these individuals were asked if they regularly used ad blocking tools when navigating the web. Additionally, the survey collected information on their socio-demographic characteristics (age, gender, education level and employment status) and on how they used Internet (frequency of use of online services like: GPS navigation services, instant messaging, mobile gaming, social networks, e-mail and watching videos on the phone). 6 months later (second quarter of 2018), the same individuals were asked how many online purchases they had made during the previous 6 months (these included goods and services purchases, irrespective of the form of payment). Thus, the outcome variable (number of online purchases) occurred later than the collection of the ad blocking information and the rest of variables (our X covariates).
Analysis N Treated Controls Difference (ATT) 95% LCI 95% UCI p-value Unmatched 4411 5.084 2.735 2.348 — — — PSM—NN 1648 5.084 3.325 1.759 0.994 2.523 <0.001 PSM—KM 4411 5.084 3.733 1.351 0.658 2.044 <0.001 Stratification on PS quintiles 4411 5.084 3.686 1.398 0.724 2.072 <0.001 Stratification on PS deciles 4411 5.084 3.774 1.310 0.626 1.994 <0.001 PSM—NN after CEM pruning (1) 1160 4.979 3.773 1.206 0.165 2.246 0.023 PSM—NN after CEM pruning (2) 1622 5.082 3.476 1.605 0.830 2.380 <0.001
Table 2: Estimated average treatment effects of ad blockers on online shopping (number of purchases in 6 months). [ATT: average treatment effect on the treated. PSM: propensity score matching. NN: nearest neighbor. KM: kernel matching. PS: propensity scores. CEM: coarsened exact matching. LCI: lower confidence interval. UCI: upper confidence interval. (1) CEM pruning by using use of Internet apps covariates. (2) CEM pruning by using socio-demographic covariates.]
20200705-20210103-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (2020-07-05–2021-01-03)”, Gwern Branwen ( )
2020-aral.pdf: “Digital Paywall Design: Implications for Content Demand and Subscriptions”, (2020-08-14; ):
Most online content publishers have moved to subscription-based business models regulated by digital paywalls. But the managerial implications of such freemium content offerings are not well understood. We, therefore, utilized microlevel user activity data from the New York Times to conduct a large-scale study of the implications of digital paywall design for publishers. Specifically, we use a quasi-experiment that varied the (1) quantity (the number of free articles) and (2) exclusivity (the number of available sections) of free content available through the paywall to investigate the effects of paywall design on content demand, subscriptions, and total revenue.
The paywall policy changes we studied suppressed total content demand by about 9.9%, reducing total advertising revenue. However, this decrease was more than offset by increased subscription revenue as the policy change led to a 31% increase in total subscriptions during our seven-month study, yielding net positive revenues of over $287,104$230,0002013. The results confirm an economically-significant impact of the newspaper’s paywall design on content demand, subscriptions, and net revenue. Our findings can help structure the scientific discussion about digital paywall design and help managers optimize digital paywalls to maximize readership, revenue, and profit.
2020-shon.pdf: “Free contents vs. inconvenience costs_ Two faces of online video advertising”, Minjung Shon, Jungwoo Shin, Junseok Hwang, Daeho Lee
2020-hsieh.pdf: “Do not allow pop-up ads to appear too early internet users’ browsing behaviour to pop-up ads”, (2020-06-23):
This study examines the timing of pop-up advertising appearance and its effect on perceived intrusiveness, advertising irritation and advertising avoidance. Experiment was designed to build a virtual Internet environment (including the main content on the webpage and a pop-up ad) and to manipulate the timing of the pop-up advertising appearance. Participants were invited to participate in two experiments, and then assigned to a specific target browsing task; their advertising browsing activities during the task were measured. In order to measure their cognitive advertising avoidance, an eye-tracking device was utilised to gain objective and accurate psychological information. Results showed that earlier pop-up advertising appearances are associated with a lower consumer fixation count and fixation length; in contrast, pop-up advertising that appears later is associated with a higher fixation count and fixation length. This study attempts to gain more objective and accurate psychological data by using an eye-tracking device to collect information about eye movements associated with the appearance of pop-up advertising to better analyse consumer behaviours towards them. These results offer insights to Internet advertisers and Internet platform companies on how to provide more efficient Internet advertising.
[Keywords: Pop-up advertising, pop-up timing, advertising intrusiveness, advertising avoidance, eye tracking]
2020-nettelhorst.pdf: “Online viewers’ choices over advertisement number and duration”, (2020-05-22):
Purpose: The purpose of this study is to investigate online viewers’ preferences concerning the number and duration of video advertisements to watch during commercial breaks. The goal of the investigations was to assess whether online viewers preferred watching a fewer number of advertisements with longer durations or a greater number of advertisements with shorter durations.
methodology/: Two studies used experimental research designs to assess viewers’ preferences regarding advertisements. These designs used two independent variables and one dependent variable. The first independent variable manipulated the type of choice options given to online viewers (e.g. one 60 s or two 30 s advertisements). The second independent variable manipulated when the choice was given to online viewers (i.e. at the beginning of the viewing experience or in the middle of the experience). The dependent variable measured viewers’ choices concerning their preferred advertisement option. approach
Findings: The results across both studies found that participants made choices that minimized total advertisement exposure time when possible. When minimizing total exposure time was not possible, participants made choices that minimized the number of exposures instead.
These investigations extend the literature on advertisement choice by examining online viewers’ preferences about the format of their advertising experience rather than the content of the persuasive messages themselves. In addition, these investigations provide value by investigating viewers’ responses to stimuli within realistic online simulations rather than abstract hypotheticals.
2020-michelon.pdf: “A New Benchmark for Mechanical Avoidance of Radio Advertising”, (2020-03-23; ):
Radio remains popular, delivering an audience reach of over 90 percent, but radio ratings may overestimate real advertising exposure. Little is known about audience and media factors affecting radio-advertising avoidance. Many advertisers have believed as much as one-third of the audience switch stations during radio-advertising breaks. In the current study, the authors combined Canadian portable people-meter data ratings to measure loss of audience during advertising. They discovered a new benchmark of 3% (across conditions) for mechanical (or actual physical) avoidance of radio advertising, such as switching stations or turning off the radio. This rate is about one-tenth of current estimates, but was higher for music versus talk stations, out-of-home versus in-home listening, and early versus late dayparts.
2020-bulkan.pdf: “Modelling Quality of Experience for Online Video Advertisement Insertion”, (2020-01-24):
The impact of online video advertisement has an evolving and undeniable influence on the success of online video streaming. A successful online video advertisement campaign deployment necessitates: “targeting appropriate marketing audience, determining optimum intervals to insert advertisement, associating the production quality of the content while considering advertisement conceptual features, matching the relevance of advertisement context to the content theme, calculating the applicable number of ads for stitching into the content, and correlating the ratio of advertisement length to total active watch duration”. This paper proposes a novel model for inserting advertisement into online video that considers content and commercial specific properties while optimizing Quality of Experience (QoE) by estimating suitable duration for advertisement, number of splits and content relation. The proposed model has been evaluated in a controlled on-line video test environment so that the success rate of this platform has been compared with the advertisement insertion strategies of technology frontrunners YouTube and Vimeo. In terms of medium and long length online videos, advertisements located within the content provides a better QoE compared to the ones that are located at the beginning of the video. For short length online videos, the general expectation of the audience tends to see the content immediately and any advertisement insertion related delay results in a corresponding customer behavior where 25% tend to quit after 3 seconds and another 25% after 5 seconds.
20190703-20200102-gwern.net-analytics-pages.pdf: “Gwern.net Google Analytics, pageviews by page: 3 July 2019 - 2 January 2020”, Gwern Branwen ( )
20180101-20191231-annualcomparison.pdf: “2019 gwern.net Site Traffic (Comparison with 2018)”, Gwern Branwen ( )
2019-aribarg.pdf: “Native Advertising in Online News: Trade-Offs Among Clicks, Brand Recognition, and Website Trustworthiness”, (2019-11-10):
Native advertising is a type of online advertising that matches the form and function of the platform on which it appears. In practice, the choice between display and in-feed native advertising presents brand advertisers and online news publishers with conflicting objectives. Advertisers face a trade-off between ad clicks and brand recognition, whereas publishers need to strike a balance between ad clicks and the platform’s trustworthiness. For policy makers, concerns that native advertising confuses customers prompted the U.S. Federal Trade Commission to issue guidelines for disclosing native ads. This research aims to understand how consumers respond to native ads versus display ads and to different styles of native ad disclosures, using randomized online and field experiments combining behavioral clickstream, eye movement, and survey response data. The results show that when the position of an ad on a news page is controlled for, a native ad generates a higher click-through rate because it better resembles the surrounding editorial content. However, a display ad leads to more visual attention, brand recognition, and trustworthiness for the website than a native ad.
[Keywords: native advertising, public policy, eye-tracking, field experiments, advertising disclosure]
20190218-20190720-twdne-analytics.pdf: “ThisWaifuDoesNotExist.net: Google Analytics: All Traffic 20190218-20190720”, Gwern Branwen ( )
2019-shapiro.pdf: “Generalizable and Robust TV Advertising Effects”, (2019-06-11; ):
We provide generalizable and robust results on the causal sales effect of TV advertising based on the distribution of advertising elasticities for a large number of products (brands) in many categories. Such generalizable results provide a prior distribution that can improve the advertising decisions made by firms and the analysis and recommendations of anti-trust and public policy makers. A single case study cannot provide generalizable results, and hence the marketing literature provides several meta-analyses based on published case studies of advertising effects. However, publication bias results if the research or review process systematically rejects estimates of small, statistically insignificant, or “unexpected” advertising elasticities. Consequently, if there is publication bias, the results of a meta-analysis will not reflect the true population distribution of advertising effects. To provide generalizable results, we base our analysis on a large number of products and clearly lay out the research protocol used to select the products. We characterize the distribution of all estimates, irrespective of sign, size, or statistical-significance. To ensure generalizability we document the robustness of the estimates. First, we examine the sensitivity of the results to the approach and assumptions made when constructing the data used in estimation from the raw sources. Second, as we aim to provide causal estimates, we document if the estimated effects are sensitive to the identification strategies that we use to claim causality based on observational data. Our results reveal substantially smaller effects of own-advertising compared to the results documented in the extant literature, as well as a sizable percentage of statistically insignificant or negative estimates. If we only select products with statistically-significant and positive estimates, the mean or median of the advertising effect distribution increases by a factor of about five. The results are robust to various identifying assumptions, and are consistent with both publication bias and bias due to non-robust identification strategies to obtain causal estimates in the literature.
[Keywords: advertising, publication bias, generalizability]
20180704-20190103-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20180704-20190103)”, Gwern Branwen ( )
20170101-20181231-annualcomparison.pdf: “2018 gwern.net Site Traffic (Comparison with 2017)”, Gwern Branwen ( )
20180104-20180703-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20180104-20180703)”, Gwern Branwen ( )
2018-04-10-tavan-isadblockingtenpercenthigherthancommonlymeasured.html: “Is Ad Blocking 10% Higher Than Commonly Measured?”, (2018-04-10; ):
A recent study by contentpass indicates that more than 25% of all ad blockers on desktop devices use the EasyPrivacy blocklist and are therefore invisible to common website analytics software…The by far most popular filter list to block ads is the so-called “Easylist”. It is activated by default in popular ad blockers like Adblock Plus, Adblock or uBlock Origin and focuses on blocking ads both on a network—and on a visual level. Even the built-in ad blocker of Google Chrome uses this list.
While EasyPrivacy users are now “invisible” to our service as well, we recently integrated our solution under the first party domain on a popular German IT news website. As a consequence of this first party integration the statistics about ad blocker usage were sent to a different URL, which was initially not being blocked by EasyPrivacy. It took about two weeks for the EasyPrivacy community to put the statistics URL of the first party domain on a filter list again.
These two weeks of unfiltered data allow us to get an idea of how many people use an ad blocker with EasyPrivacy activated (be it Adblock Plus/
Adblock where the user manually activated EasyPrivacy or uBlock Origin where EasyPrivacy is activated by default).
Our data suggests that over 25% of all users with active ad blocking software on desktop devices use EasyPrivacy and are thus invisible to major web analytics software. In this specific case the true ad blocking rate on desktop was 37% while analytics software that is blocked by EasyPrivacy would only report what corresponds to 27% of ad blocking. Or from a different perspective: 10% of the total desktop traffic on this website is not analyzed and counted by common third party analytics software. Historical data from the time where our service was initially added to EasyPrivacy suggests similar proportions on other sites and verticals.
20170704-20180103-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20170704-20180103)”, Gwern Branwen ( )
20170101-20171015-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20170101-20171015)”, Gwern Branwen ( )
2017-sinha.pdf: “Anti-Ad Blocking Strategy: Measuring its True Impact”, (2017-08-14; ):
The increasing use of ad blocking software poses a major threat for publishers in loss of online ad revenue, and for advertisers in the loss of audience. Major publishers have adopted various anti-ad blocking strategies such as denial of access to website content and asking users to subscribe to paid ad-free versions. However, publishers are unsure about the true impact of these strategies2, 3. We posit that the real problem lies in the measurement of effectiveness because the existing methods compare metrics after implementation of such strategies with that of metrics just before implementation, making them error prone due to sampling bias. The errors arise due to differences in group compositions across before and after periods, as well as differences in time-period selection for the before measurement. We propose a novel algorithmic method which modifies the difference-in-differences approach to address the sampling bias due to differences in time-period selection. Unlike difference-in-differences, we choose the time-period for comparison in an endogenous manner, as well as, exploit differences in ad blocking tendencies among visitors’ arriving on the publisher’s site to allow cluster specific choice of the control time-period. We evaluate the method on both synthetic data (which we make available) and proprietary real data from an online publisher and find good support.
20170104-20170703-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20170104-20170703)”, Gwern Branwen ( )
2017-pagefair.pdf: “The Hidden Cost of Adblock: Adblock's impact on website traffic”, (2017-02; ):
This whitepaper presents the primary findings of new research by Professor Benjamin Shiller (Brandeis University), Professor Joel Waldfogel (University of Minnesota and the National Bureau of Economic Research), and Dr. Johnny Ryan (PageFair).
Research of 2,574 websites over 3 years reveals that adblock has a hidden cost: it not only reduces small and medium publishers’ revenue, it also reduces their traffic.
Studying the changing rate of desktop adblock usage and traffic rank from April 2013—June 2016 reveals that adblock usage is undermining many websites’ ability to invest in content. Affected websites then attract fewer visitors, and so their traffic declines. The full paper is available from NBER, the U.S. National Bureau of Economic Research.
This is the adblock paradox: users may avoid ads in the short term, but ultimately undermine the value they can derive from the web. To reverse this phenomenon, publishers must listen to users’ legitimate grievances about online ads and respond by fixing the problems. Once they have remedied the users’ grievances, publishers can choose to serve their ads using technology that adblock companies cannot tamper with.
20160704-20170103-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20160704-20170103)”, Gwern Branwen ( )
2017-shiller.pdf: “Will Ad Blocking Break the Internet?”, Ben Shiller, Joel Waldfogel, Johnny Ryan ( )
20160104-20160703-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20160104-20160703)”, Gwern Branwen ( )
20150703-20160103-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20150703-20160103)”, Gwern Branwen ( )
2015-lewis.pdf: “The Unfavorable Economics of Measuring the Returns to Advertising”, (2015-07-06; ):
25 large field experiments with major U.S. retailers and brokerages, most reaching millions of customers and collectively representing $2.8 million in digital advertising expenditure, reveal that measuring the returns to advertising is difficult. The median confidence interval on return on investment is over 100 percentage points wide. Detailed sales data show that relative to the per capita cost of the advertising, individual-level sales are very volatile; a coefficient of variation of 10 is common. Hence, informative advertising experiments can easily require more than 10 million person-weeks, making experiments costly and potentially infeasible for many firms. Despite these unfavorable economics, randomized control trials represent progress by injecting new, unbiased information into the market. The inference challenges revealed in the field experiments also show that selection bias, due to the targeted nature of advertising, is a crippling concern for widely employed observational methods.
20150103-20150702-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20150103-20150702)”, Gwern Branwen ( )
20140703-20150103-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20140703-20150103)”, Gwern Branwen ( )
2015-pagefair.pdf: “The cost of ad blocking: PageFair and Adobe 2015 Ad Blocking Report”, (2015; ):
In the third annual ad blocking report, PageFair, with the help of Adobe, provides updated data on the scale and growth of ad blocking software usage and highlights the global and regional economic impact associated with it. Additionally, this report explores the early indications surrounding the impact of ad blocking within the mobile advertising space and how mobile will change the ad blocking landscape.
Table of Contents: · 1. Introduction · 2. Table of Contents · 3. Key insights · 4. Global ad blocking growth · 5. Usage of ad blocking software in the United States · 6. Usage of ad blocking software in Europe · 7. The cost of blocking ads · 8. Effect of ad blocking by industry · 9. Google Chrome still the main driver of ad block growth · 10. Mobile is yet to be a factor in ad blocking growth · 11. Mobile will facilitate future ad blocking growth · 12. Reasons to start using an ad blocker · 13. Afterword · 14. Background · 15. Methodology · 16. Tables · 17. Tables
Key Insights: More consumers block ads, continuing the strong growth rates seen during 2013 and 2014. The findings:
- Globally, the number of people using ad blocking software grew by 41% year over year.
- 16% of the US online population blocked ads during Q2 2015.
- Ad block usage in the United States grew 48% during the past year, increasing to 45 million monthly active users (MAUs) during Q2 2015.
- Ad block usage in Europe grew by 35% during the past year, increasing to 77 million monthly active users during Q2 2015.
- The estimated loss of global revenue due to blocked advertising during 2015 was $21.8B.
- With the ability to block ads becoming an option on the new iOS 9, mobile is starting to get into the ad blocking game. Currently Firefox and Chrome lead the mobile space with 93% share of mobile ad blocking.
2015-hohnhold.pdf: “Focusing on the Long-term: It’s Good for Users and Business”, (2015; ):
Over the past 10+ years, online companies large and small have adopted widespread A/
B testing as a robust data-based method for evaluating potential product improvements. In online experimentation, it is straightforward to measure the short-term effect, i.e., the impact observed during the experiment. However, the short-term effect is not always predictive of the long-term effect, i.e., the final impact once the product has fully launched and users have changed their behavior in response. Thus, the challenge is how to determine the long-term user impact while still being able to make decisions in a timely manner.
We tackle that challenge in this paper by first developing experiment methodology for quantifying long-term user learning. We then apply this methodology to ads shown on Google search, more specifically, to determine and quantify the drivers of ads blindness and sightedness, the phenomenon of users changing their inherent propensity to click on or interact with ads.
We use these results to create a model that uses metrics measurable in the short-term to predict the long-term. We learn that user satisfaction is paramount: ads blindness and sightedness are driven by the quality of previously viewed or clicked ads, as measured by both ad relevance and landing page quality. Focusing on user satisfaction both ensures happier users but also makes business sense, as our results illustrate. We describe two major applications of our findings: a conceptual change to our search ads auction that further increased the importance of ads quality, and a 50% reduction of the ad load on Google’s mobile search interface.
The results presented in this paper are generalizable in two major ways. First, the methodology may be used to quantify user learning effects and to evaluate online experiments in contexts other than ads. Second, the ads blindness/
sightedness results indicate that a focus on user satisfaction could help to reduce the ad load on the internet at large with long-term neutral, or even positive, business impact.
[Keywords: Controlled experiments; A/
B testing; predictive modeling; overall evaluation criterion]
20140103-20140702-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20140103-20140702)”, Gwern Branwen ( )
20130703-20140102-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20130703-20140102)”, Gwern Branwen ( )
2014-goldstein.pdf: “The Economic and Cognitive Costs of Annoying Display Advertisements”, (2014; ):
Some online display advertisements are annoying. Although publishers know the payment they receive to run annoying ads, little is known about the cost that such ads incur (e.g., causing website abandonment). Across three empirical studies, the authors address two primary questions: (1) What is the economic cost of annoying ads to publishers? and (2) What is the cognitive impact of annoying ads to users? First, the authors conduct a preliminary study to identify sets of more and less annoying ads. Second, in a field experiment, they calculate the compensating differential, that is, the amount of money a publisher would need to pay users to generate the same number of impressions in the presence of annoying ads as it would generate in their absence. Third, the authors conduct a mouse-tracking study to investigate how annoying ads affect reading processes. They conclude that in plausible scenarios, the practice of running annoying ads can cost more money than it earns.
20130103-20130702-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20130103-20130702)”, Gwern Branwen ( )
20120703-20130102-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20120703-20130102)”, Gwern Branwen ( )
20120103-20120702-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20120103-20120702)”, Gwern Branwen ( )
20110702-20120102-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20110702-20120102)”, Google Analytics ( )
20110228-20110702-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (20110228-20110702)”, Google Analytics ( )
2011-lewis.pdf: “Here, there, and everywhere: correlated online behaviors can lead to overestimates of the effects of advertising”, (2011-03; ):
Measuring the causal effects of online advertising (
adfx) on user behavior is important to the health of the WWW publishing industry. In this paper, using three controlled experiments, we show that observational data frequently lead to incorrect estimates of
adfx. The reason, which we label “activity bias,” comes from the surprising amount of time-based correlation between the myriad activities that users undertake online.
In Experiment 1, users who are exposed to an ad on a given day are much more likely to engage in brand-relevant search queries as compared to their recent history for reasons that had nothing do with the advertisement. In Experiment 2, we show that activity bias occurs for page views across diverse websites. In Experiment 3, we track account sign-ups at a competitor’s (of the advertiser) website and find that many more people sign-up on the day they saw an advertisement than on other days, but that the true “competitive effect” was minimal.
In all three experiments, exposure to a campaign signals doing “more of everything” in given period of time, making it difficult to find a suitable “matched control” using prior behavior. In such cases, the “match” is fundamentally different from the exposed group, and we show how and why observational methods lead to a massive overestimate of
adfxin such circumstances.
[Keywords: advertising effectiveness, browsing behavior, causal inference, field experiments, selection bias]
201010-201102-gwern.net-analytics.pdf: “Gwern.net: Google Analytics Semi-Annual Traffic Report (201010-201102)”, Google Analytics ( )
2010-dix.pdf: “Television Advertising Avoidance: Advancing Research Methodology”, (2010-03-12):
New technologies have led to increased television advertising avoidance. In particular, mechanical avoidance in the form of zipping and zapping has gained momentum in recent years. Channel switching or “commercial zapping” studies employ diverse methodologies, including self reports, electronic monitoring, laboratory, and in-home observation which has led to a diversity of reported results. This article proposes advancing and standardizing the methodology to comprise a two-phase hidden observation and survey method. A number of research phases have led to the development of this method to collect both mechanical and behavioral avoidance data. The study includes a detailed outline of the hidden observation approach. The survey phase opens up the potential for the collection of viewer data that may further illuminate television advertising avoidance behavior.
[Keywords: advertising, commercials, consumer behavior, television, zapping, zipping]
2009-kaiser.pdf: “Do media consumers really dislike advertising? An empirical assessment of the role of advertising in print media markets”, (2009-03-01):
This paper uses data on German consumer magazines observed between 1992 and 2004 to analyze the extent to which consumers (dis-)like advertising. We estimate logit demand models separately for the six most important magazine segments in terms of circulation. We find little evidence for readers disliking advertising. On the contrary, we show that readers in many magazine segments appreciate advertising.
Readers of Women’s magazines, Business and politics magazines as well as Car magazines—market segments where advertisements are relatively more informative—appreciate advertising while advertising is nuisance to readers of Adult magazines, a segment where advertisements are particularly uninformative. Demand for interior design magazines is not well identified. Our logit demand estimates are confirmed by logit demand models with random coefficients and by magazine-specific monopoly demand models.
[Keywords: two-sided markets, advertising, mean group estimation, random coefficients model, media markets, nuisance]
2007-mccoy.pdf: “The Effects Of Online Advertising: Consumers’ first impressions (and loyalties) are made in the opening moments of a Web site visit and the degree to which that visit may be intruded by pop-ups, pop-unders, and banner ads”, (2007; ):
We conducted an experiment with different forms and types of ads. An artificial Web site was created for the experiment that contained images, prices, and descriptions of familiar products and product categories. The products were those that would be carried by a general store and included food, health care, and household products. Nine search tasks were assigned to participants that would force them to traverse a variety of portions of the site…The experimental websites were accessed over the Internet in a controlled laboratory setting by 536 undergraduate students.
…This study provides clear support for an assertion that users will adopt more negative intentions when a site displays advertisements than when the site does not. It is also clear that advertisements interfere with retention of site content and that features of advertisements also have important effects on retaining both site and ad content. Inline ads permit both site and ad content to be remembered more clearly than popups and popunders, a finding that is most interesting because it suggests the action of closing the advertisement window distracts users from the site, and further, it is visible for a shorter time. When ads are markedly different from the content of the site, they theoretically stimulate more effort as users work toward an important goal, and users remember more about both the Web site and the advertisement. It is interesting to note that while these effects might on the surface appear small, they are quite consistent and highly statistically-significant. Extrapolating to millions of site visitors, even small differences can amount to an urgent problem for management. Finally, it is also clear that popups and popunders are considered to be more intrusive than inline ads. Users seem to prefer not to have to divert their attention from their searching task or take additional steps to close the popup or pop-under windows.
2006-galletta.pdf: “When the Wait Isn't So Bad: The Interacting Effects of Website Delay, Familiarity, and Breadth”, Dennis F. Galletta, Raymond M. Henry, Scott McCoy, Peter Polak ( )
2004-galletta.pdf: “Web Site Delays: How Tolerant are Users?”, (2004; ):
Web page loading speed continues to vex users, even as broadband adoption increases. Several studies have addressed delays in the context of Web sites as well as interactive corporate systems, and have recommended a wide range of ‘rules of thumb’. Some studies conclude that response times should be no greater than 2 seconds while other studies caution on delays of 12 seconds or more. One of the strongest conclusions was that complex tasks seemed to allow longer response times. This study examined delay times of 0, 2, 4, 6, 8, 10, and 12 seconds using 196 undergraduate students in an experiment. Randomly assigned a constant delay time, subjects were asked to complete 9 search tasks, exploring a familiar and an unfamiliar site. Plots of the dependent variables performance, attitudes, and behavioral intentions, along those delays, suggested the use of non-linear regression, and the explained variance was in the neighborhood of 2%, 5%, and 7%, respectively. Focusing only on the familiar site, explained variance in attitudes and behavioral intentions grew to about 16%. A sensitivity analysis implies that decreases in performance and behavioral intentions begin to flatten when the delays extend to 4 seconds or longer, and attitudes flatten when the delays extend to 8 seconds or longer. Future research should include other factors such as expectations, variability, and feedback, and other outcomes such as actual purchasing behavior, to more fully understand the effects of delays in today’s Web environment.
2002-edwards.pdf: “Forced Exposure and Psychological Reactance: Antecedents and Consequences of the Perceived Intrusiveness of Pop-Up Ads”, (2002; ):
This paper explores forced viewing of “pop-up ads” on the Internet to understand better how viewers come to define ads as irritating and decide to avoid them. Perceived intrusiveness was suggested as the underlying mechanism by which the process occurs. Antecedents of intrusiveness were identified that affect perceptions of ads as interruptions, including congruence of the advertisement content with the current task and intensity of cognition at the moment the ad pops up. The consequences of intrusiveness were shown to be caused by feelings of irritation and ad avoidance. The results provide an understanding of how consumers experience forced exposure situations in interactive environments and highlight implications for advertisers seeking to increase the effectiveness of on-line advertising.
2000-bayles-justhowblindarewetoadvertisingbannersontheweb.html: “Just How 'Blind' Are We to Advertising Banners on the Web?”, (2000-07; ):
Moreover, Benway (1998) showed that extremely colorful and obvious banners tend to be ignored by users. When participants in this study were asked to find specific information on a web page, the information was not found if it was embedded in a banner. Benway consequently named this phenomenon “banner blindness.” Benway also found that banners located at the top of the page (away from other links), tended to be ignored more often than banners located lower down the page (closer to other important links). This finding is supported by another study which showed a 77% increased click-through rate for advertisements placed 1⁄3 of the way down the page (Athenia Assoc., 1997).
…In our study, we were curious to simply explore how much users remember about a web page after viewing it—in particular, we were interested in investigating user memory of banner advertisements:
- How well can users’ recall a banner advertisement on a web page?
- How well can users’ recognize a banner advertisement on a web page?
- Does animation affect user recall or recognition of an advertising banner?
…Very few participants were able to complete both the recognition and recall tasks correctly. Only 3 (9%) of participants were able to correctly recall both advertisements, recognize both companies, and correctly recall and recognize the state in which they were presented. On the other hand, participants who were unable to recall anything for either company banner or correctly indicate the animation state of the banner (40%) had a surprisingly high recognition rate of 79% for two correctly recognized ads. Results also show that of the 26% who recognized only one ad, the banner recognized was typically presented in the animated state. In other words, 7 out of 9 times the single banner correctly recognized was in the animated state. This indicates that animation may have some effect on recognition.
Results from this study indicate that recognition of the banner advertisements were fairly high (74% for both banners). In addition, about half of the participants were able to recall at least seeing an advertisement on the page—and many of these actually recalled the name of the company. These results show that most users did notice and remember the banners even though they were not part of the search tasks they were performing.
1991-abernethy.pdf: “Television Exposure: Programs vs. Advertising”, (1991; ):
Although it is generally accepted that television program ratings are greater than the audience’s exposure to the advertising, the key issue is the actual size of the difference. A review of advertising, marketing, communication, and sociology literature yields some indications of the degree of difference between ad and program exposure and factors in the viewing environment which could influence audience commercial avoidance.