We estimate the distribution of television advertising elasticities and the distribution of the advertising return on investment (ROI) for a large number of products in many categories…We construct a data set by merging market (DMA) level TV advertising data with retail sales and price data at the brand level…Our identification strategy is based on the institutions of the ad buying process.
Our results reveal substantially smaller advertising elasticities compared to the results documented in the literature, as well as a sizable percentage of statistically insignificant or negative estimates. The results are robust to functional form assumptions and are not driven by insufficient statistical power or measurement error.
The ROI analysis shows negative ROIs at the margin for more than 80% of brands, implying over-investment in advertising by most firms. Further, the overall ROI of the observed advertising schedule is only positive for one third of all brands.
[Keywords: advertising, return on investment, empirical generalizations, agency issues, consumer packaged goods, media markets]
…We find that the mean and median of the distribution of estimated long-run own-advertising elasticities are 0.023 and 0.014, respectively, and 2 thirds of the elasticity estimates are not statistically different from zero. These magnitudes are considerably smaller than the results in the extant literature. The results are robust to controls for own and competitor prices and feature and display advertising, and the advertising effect distributions are similar whether a carryover parameter is assumed or estimated. The estimates are also robust if we allow for a flexible functional form for the advertising effect, and they do not appear to be driven by measurement error. As we are not able to include all sensitivity checks in the paper, we created an interactive web application that allows the reader to explore all model specifications. The web application is available.
…First, the advertising elasticity estimates in the baseline specification are small. The median elasticity is 0.0140, and the mean is 0.0233. These averages are substantially smaller than the average elasticities reported in extant meta-analyses of published case studies (Assmus, Farley, and Lehmann (1984b), Sethuraman, Tellis, and Briesch (2011)). Second, 2 thirds of the estimates are not statistically distinguishable from zero. We show in Figure 2 that the most precise estimates are those closest to the mean and the least precise estimates are in the extremes.
…6.1 Average ROI of Advertising in a Given Week:
In the first policy experiment, we measure the ROI of the observedadvertising levels (in all DMAs) in a given week t relative to not advertising in week t. For each brand, we compute the corresponding ROI for all weeks with positive advertising, and then average the ROIsacross all weeks to compute the average ROI of weekly advertising. This metric reveals if, on the margin, firms choose the (approximately) correct advertising level or could increase profits by either increasing or decreasing advertising.
We provide key summary statistics in the top panel of Table III, and we show the distribution of the predicted ROIs in Figure 3(a). The average ROI of weekly advertising is negative for most brands over the whole range of assumed manufacturer margins. At a 30% margin, the median ROI is −88.15%, and only 12% of brands have positive ROI.Further, for only 3% of brands the ROI is positive and statisticallydifferent from zero, whereas for 68% of brands the ROI is negative and statistically different from zero.
These results provide strong evidence for over-investment in advertising at the margin. [In Appendix C.3, we assess how much larger the TV advertising effects would need to be for the observed level of weekly advertising to be profitable. For the median brand with a positive estimated ad elasticity, the advertising effect would have to be 5.37 times larger for the observed level of weekly advertising to yield a positive ROI (assuming a 30% margin).]
6.2 Overall ROI of the Observed Advertising Schedule: In the second policy experiment, we investigate if firms are better off when advertising at the observed levels versus not advertising at all. Hence, we calculate the ROI of the observed advertising schedule relative to a counterfactual baseline with zero advertising in all periods.
We present the results in the bottom panel of Table III and in Figure 3(b). At a 30% margin, the median ROI is −57.34%, and 34% of brands have a positive return from the observed advertising schedule versus not advertising at all. Whereas 12% of brands only have positive and 30% of brands only negative values in their confidence intervals, there is more uncertainty about the sign of the ROI for the remaining 58% of brands. This evidence leaves open the possibility that advertising may be valuable for a substantial number of brands, especially if they reduce advertising on the margin.
…Our results have important positive and normative implications. Why do firms spend billions of dollars on TV advertising each year if the return is negative? There are several possible explanations. First, agency issues, in particular career concerns, may lead managers (or consultants) to overstate the effectiveness of advertising if they expect to lose their jobs if their advertising campaigns are revealed to be unprofitable. Second, an incorrect prior (i.e., conventional wisdom that advertising is typically effective) may lead a decision maker to rationally shrink the estimated advertising effect from their data to an incorrect, inflated prior mean. These proposed explanations are not mutually exclusive. In particular, agency issues may be exacerbated if the general effectiveness of advertising or a specific advertising effect estimate is overstated. [Another explanation is that many brands have objectives for advertising other than stimulating sales. This is a nonstandard objective in economic analysis, but nonetheless, we cannot rule it out.] While we cannot conclusively point to these explanations as the source of the documented over-investment in advertising, our discussions with managers and industry insiders suggest that these may be contributing factors.
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).
Stratification on PS quintiles
Stratification on PS deciles
PSM—NN after CEM pruning (1)
PSM—NN after CEM pruning (2)
Table 2: Estimated average treatment effects of ad blockers on online shopping (number of purchases in 6 months). [ATT: average treatmenteffect on the treated. PSM: propensity score matching. NN: nearestneighbor. KM: kernel matching. PS: propensity scores. CEM: coarsenedexact matching. LCI: lowerconfidence interval.UCI: upper confidenceinterval. (1) CEM pruning by using use of Internet apps covariates.(2) CEM pruning by using socio-demographic covariates.]
Evidence across social science indicates that average effects of persuasive messages are small. One commonly offered explanation for these small effects is heterogeneity: Persuasion may only work well in specific circumstances. To evaluate heterogeneity, we repeated an experiment weekly in real time using 2016 U.S. presidential election campaign advertisements. We tested 49 political advertisements in 59 unique experiments on 34,000 people. We investigate heterogeneous effects by sender (candidates or groups), receiver (subject partisanship), content (attack or promotional), and context (battleground versus non-battleground, primary versus general election, and early versus late). We find small average effects on candidate favorability and vote. These small effects, however, do not mask substantial heterogeneity even where theory from political science suggests that we should find it. During the primary and general election, in battleground states, for Democrats, Republicans, and Independents, effects are similarly small. Heterogeneity with large offsetting effects is not the source of small average effects.
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.
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.
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.
Design/methodology/approach: 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 (eg. one 60 s or two 30 s advertisements). The second independent variable manipulated when the choice was given to online viewers (ie. 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.
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.
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.
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.
The article shows the main factors of adblocking software usage. The study was based on data obtained by a web questionnaire. The research was focused on evaluation of ad blocking (adblock) software usage factors in five categories: (1) gender, age, and education; (2) use of advertising and sources of knowledge about advertising; (3) technical and social reasons for blocking online advertisements; (4) usage of an adblock-wall; and (5) type of online advertisement. An evaluation of adblock usage factors revealed four main technical reasons for adblock usage connected with website technology and web development problems—interruption, amount of ads, speed, and security; and one social reason for adblock usage, namely, the problem of privacy.
[Keywords: adblock software, web advertisement, website, security, privacy]
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]
Digital platforms, such as Facebook, Uber, and AirBnB, create value by connecting users, creators, and contractors of different types. Their rapid growth, untraditional business model, and disruptive nature presents challenges for managers and asset pricers. These features also, arguably, make them natural monopolies, leading to increasing calls for special regulations and taxes. We construct and illustrate a approach for modeling digital platforms. The model allows for heterogeneity in elasticity of demand and heterogeneous network effects across different users. We parameterize our model using a survey of over 40,000 US internet users on their demand for Facebook. Facebook creates about 11.2 billion dollars in consumer surplus a month for US users age 25 or over, in line with previous estimates. We find Facebook has too low a level of advertising relative to their revenue maximizing strategy, suggesting that they also value maintaining a large user base. We simulate six proposed government policies for digital platforms, taking Facebook’s optimal response into account. Taxes only slightly change consumer surplus. Three more radical proposals, including ‘data as labor’ and nationalization, have the potential to raise consumer surplus by up to 42%. But a botched regulation that left the US with two smaller, non-competitive social media monopolies would decrease consumer surplus by 44%.
Does advertising revenue increase or diminish content differentiation in media markets? This paper shows that an increase in the technically feasible number of ad breaks per video leads to an increase in content differentiation between several thousand YouTube channels. I exploit two institutional features of YouTube’s monetization policy to identify the causal effect of advertising on the YouTubers’ content choice. The analysis of around one million YouTube videos shows that advertising leads to a twenty percentage point reduction in the YouTubers’ probability to duplicate popular content, ie., content in high demand by the audience. I also provide evidence of the economic mechanism behind the result: popular content is covered by many competing YouTubers; hence, viewers who perceive advertising as a nuisance could easily switch to a competitor if a YouTuber increased her number of ad-breaks per video. This is less likely, however, when the YouTuber differentiates her content from her competitors.
[Keywords: advertising, content differentiation, economics of digitization, horizontal product differentiation, long tail, media diversity, user-generated content, YouTube]
…The analysis of around one million YouTube videos shows that an increase in the feasible number of ad breaks per video leads to a twenty percentage point reduction in the YouTubers’ probability to duplicate popular content. The effect size is considerable: it corresponds to around 40% of a standard deviation in the dependent variable and to around 50% of its baseline value.
The large sample size allows me to conduct several sub-group analyses to study effect heterogeneity. I find that the positive effect of advertising on content differentiation is driven by the YouTubers who have at least 1,000 subscribers, ie., the YouTubers whose additional ad revenue is likely to exceed the costs from adapt-ing their videos’ content. In addition, I find heterogeneity along video categories: some categories are more flexible in terms of their typical video duration than others, hence, exploiting the ten minutes trick is more easy (eg., a music clip is typically between three and five minutes long and cannot be easily extended). A battery of robustness checks confirms these results.
…Moreover, I show that ad revenue does not necessarily improve the YouTubers’ video quality. Although the number of views goes up when a video has more ad breaks, the relative number of likes decreases…Table 5 shows the results. The size of the estimates for δ′′(columns 1 to 3), though statistically-significant at the 1%-level, is negligible: a one second increase in video duration corresponds to a 0.0001 percentage point increase in the fraction of likes. The estimates for δ′′′ in columns 4 to 6, though, are relatively large and statistically-significant at the 1%-level, too. According to these estimates, one further second in video duration leads on average to about 1.5 percent more views. These estimates may reflect the algorithmic drift discussed in Section 9.2. YouTube wants to keep its viewers as long as possible on the platform to show as many ads as possible to them. As a result, longer videos get higher rankings and are watched more often.
In those early days, the company, just like almost everybody else in Washington, primarily produced Red Delicious apples, plus a few Goldens and Grannies—familiar workhorse varieties that anybody was allowed to grow. Back then, the state apple commission advertised its wares with a poster of a stoplight: one apple each in red, green, and yellow. Today, across more than 4,000 acres of McDougall apple trees, you won’t find a single Red; every year, you’ll also find fewer acres of the apples that McDougall calls “core varieties”, the more modern open-access standards such as Gala and Fuji. Instead, McDougall is betting on what he calls “value-added apples”: Ambrosias, whose rights he licensed from a Canadian company; Envy, Jazz, and Pacific Rose, whose intellectual properties are owned by the New Zealand giant Enzafruit; and a brand-new variety, commercially available for the first time this year and available only to Washington-state growers: the Cosmic Crisp.
…The Cosmic Crisp is debuting on grocery stores after this fall’s harvest, and in the nervous lead-up to the launch, everyone from nursery operators to marketers wanted me to understand the crazy scope of the thing: the scale of the plantings, the speed with which mountains of commercially untested fruit would be arriving on the market, the size of the capital risk. People kept saying things like “unprecedented”, “on steroids”, “off the friggin’ charts”, and “the largest launch of a single produce item in American history.”
McDougall took me to the highest part of his orchard, where we could look down at all its hundreds of very expensively trellised and irrigated acres (he estimated the costs to plant each individual acre at $60,000 to $65,000, plus another $12,000 in operating costs each year), their neat, thin lines of trees like the stitching over so many quilt squares. “If you’re a farmer, you’re a riverboat gambler anyway”, McDougall said. “But Cosmic Crisp—woo!” I thought of the warning of one former fruit-industry journalist that, with so much on the line, the enormous launch would have to go flawlessly: “It’s gotta be like the new iPhone.”
…Though Washington State University owns the WA 38 patent, the breeding program has received funding from the apple industry, so it was agreed, over some objections by people who worried that quality would be diluted, that the variety should be universally and exclusively available to Washington growers. (Growers of Cosmic Crisp pay royalties both on every tree they buy and on every box they sell, money that will fund future breeding projects as well as the shared marketing campaign.) The apple tested so well that WSU, in collaboration with commercial nurseries, began producing apple saplings as fast as possible; the plan was to start with 300,000 trees, but growers requested 4 million, leading to a lottery for divvying up the first available trees. Within three years, the industry had sunk 13 million of them, plus more than half a billion dollars, into the ground. Proprietary Variety Management expects that the number of Cosmic Crisp apples on the market will grow by millions of boxes every year, outpacing Pink Lady and Honeycrisp within about five years of its launch.
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.
Measuring the causal effects of digital advertising remains challenging despite the availability of granular data. Unobservable factors make exposure endogenous, and advertising’s effect on outcomes tends to be small. In principle, these concerns could be addressed using randomized controlled trials (RCTs). In practice, few online ad campaigns rely on RCTs and instead use observational methods to estimate ad effects. We assess empirically whether the variation in data typically available in the advertising industry enables observational methods to recover the causal effects of online advertising. Using data from 15 U.S. advertising experiments at Facebook comprising 500 million user-experiment observations and 1.6 billion ad impressions, we contrast the experimental results to those obtained from multiple observational models. The observational methods often fail to produce the same effects as the randomized experiments, even after conditioning on extensive demographic and behavioral variables. In our setting, advances in causal inference methods do not allow us to isolate the exogenous variation needed to estimate the treatment effects. We also characterize the incremental explanatory power our data would require to enable observational methods to successfully measure advertising effects. Our findings suggest that commonly used observational approaches based on the data usually available in the industry often fail to accurately measure the true effect of advertising.
A randomized experiment with almost 35 million Pandora listeners enables us to measure the sensitivity of consumers to advertising, an important topic of study in the era of ad-supported digital content provision. The experiment randomized listeners into 9 treatment groups, each of which received a different level of audio advertising interrupting their music listening, with the highest treatment group receiving more than twice as many ads as the lowest treatment group. By keeping consistent treatment assignment for 21 months, we are able to measure long-run demand effects, with three times as much ad-load sensitivity as we would have obtained if we had run a month-long experiment. We estimate a demand curve that is strikingly linear, with the number of hours listened decreasing linearly in the number of ads per hour (also known as the price of ad-supported listening). We also show the negative impact on the number of days listened and on the probability of listening at all in the final month. Using an experimental design that separately varies the number of commercial interruptions per hour and the number of ads per commercial interruption, we find that neither makes much difference to listeners beyond their impact on the total number of ads per hour. Lastly, we find that increased ad load causes a substantial increase in the number of paid ad-free subscriptions to Pandora, particularly among older listeners.
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.
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.
A long tradition of scholarship, from ancient Greece to Marxism or some contemporary social psychology, portrays humans as strongly gullible—wont to accept harmful messages by being unduly deferent. However, if humans are reasonably well adapted, they should not be strongly gullible: they should be vigilant toward communicated information. Evidence from experimental psychology reveals that humans are equipped with well-functioning mechanisms of epistemic vigilance. They check the plausibility of messages against their background beliefs, calibrate their trust as a function of the source’s competence and benevolence, and critically evaluate arguments offered to them. Even if humans are equipped with well-functioning mechanisms of epistemic vigilance, an adaptive lag might render them gullible in the face of new challenges, from clever marketing to omnipresent propaganda. I review evidence from different cultural domains often taken as proof of strong gullibility: religion, demagoguery, propaganda, political campaigns, advertising, erroneous medical beliefs, and rumors. Converging evidence reveals that communication is much less influential than often believed—that religious proselytizing, propaganda, advertising, and so forth are generally not very effective at changing people’s minds. Beliefs that lead to costly behavior are even less likely to be accepted. Finally, it is also argued that most cases of acceptance of misguided communicated information do not stem from undue deference, but from a fit between the communicated information and the audience’s preexisting beliefs.
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.
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.
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.
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, ie., the impact observed during the experiment. However, the short-term effect is not always predictive of the long-term effect, ie., 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.
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 (eg., 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.
Classical theories of the firm assume access to reliable signals to measure the causal impact of choice variables on profit. For advertising expenditure we show, using 25 online field experiments (representing $3.50$2.82013 million) with major U.S. retailers and brokerages, that this assumption typically does not hold. Statistical evidence from the randomized trials is very weak because individual-level sales are incredibly volatile relative to the per capita cost of a campaign—a “small” impact on a noisy dependent variable can generate positive returns. A concise statistical argument shows that the required sample size for an experiment to generate sufficiently informative confidence intervals is typically in excess of ten million person-weeks. This also implies that heterogeneity bias (or model misspecification) unaccounted for by observational methods only needs to explain a tiny fraction of the variation in sales to severely bias estimates. The weak informational feedback means most firms cannot even approach profit maximization.
We measure the causal effects of online advertising on sales, using a randomized experiment performed in cooperation between Yahoo! and a major retailer. After identifying over one million customers matched in the databases of the retailer and Yahoo!, we randomly assign them to treatment and control groups. We analyze individual-level data on ad exposure and weekly purchases at this retailer, both online and in stores. We find statistically-significant and economically substantial 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. Power calculations show that, due to the high variance of sales, our large number of observations brings us just to the frontier of being able to measure economically substantial effects of advertising. We also demonstrate that without an experiment, using industry-standard methods based on endogenous crosssectional variation in advertising exposure, we would have obtained a wildly inaccurate estimate of advertising effectiveness.
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 adfx in such circumstances.
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.
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]
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.
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.
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.
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.
[‘Subliminal advertising’ was the Cambridge Analytica of the 1950s.] In September 1957, I began what to me was a serious study of contemporary applied psychology at Hofstra College in Hempstead, Long Island. At exactly the same time, in nearby New York City, an unemployed market researcher named James M. Vicary made a startling announcement based on research in high-speed photography later popularized by Eastman Kodak Company.
His persuasive sales pitch was that consumers would comprehend information projected at 1/ 60,000th of a second, although they could not literally “see” the flash. And he sent a news release to the major media announcing his “discovery”.
…And, as a follow-up, toward the end of 1957 Vicary invited 50 reporters to a film studio in New York where he projected some motion picture footage, and claimed that he had also projected a subliminal message. He then handed out another of his well written and nicely printed news releases claiming that he had actually conducted major research on how an invisible image could cause people to buy something even if they didn’t want to.
The release said that in an unidentified motion picture theater a “scientific test” had been conducted in which 45,699 persons unknowingly had been exposed to 2 advertising messages projected subliminally on alternate nights. One message, the release claimed, had advised the moviegoers to “Eat Popcorn” while the other had read “Drink Coca-Cola.”
…Vicary swore that the invisible advertising had increased sales of popcorn an average of 57.5%, and increased the sales of Coca-Cola an average of 18.1%. No explanation was offered for the difference in size of the percentages, no allowance was made for variations in attendance, and no other details were provided as to how or under what conditions the purported tests had been conducted. Vicary got off the hook for his lack of specificity by stating that the research information formed part of his patent application for the projection device, and therefore must remain secret. He assured the media, however, that what he called “sound statistical controls” had been employed in the theater test. At least as importantly, too, he had observed the proven propagandist’s ploy of using odd numbers, and also including a decimal in a percentage. The figures 57.5 and 18.1% rang with a clear tone of Truth.
…When I learned of Vicary’s claim, I made the short drive to Fort Lee to learn first-hand about his clearly remarkable experiment.
The size of that small-town theater suggested it should have taken considerably longer than 6 weeks to complete a test of nearly 50,000 movie patrons. But even more perplexing was the response of the theater manager to my eager questioning. He declared that no such test had ever been conducted at his theater.
There went my term paper for my psychology class.
Soon after my disappointment, Motion Picture Daily reported that the same theater manager had sworn to one of its reporters that there had been no effect on refreshment stand patronage, whether a test had been conducted or not—a rather curious form of denial, I think.
…Technological Impossibility: Vicary also informed the reporters that subliminal advertising would have its “biggest initial impact” on the television medium.
When I learned of this, I visited the engineering section of RCA…I was assured by their helpful and knowledgeable engineering liaison man that, because of the time required for an electron beam to scan the surface of a television picture tube, and the persistence of the phosphor glow, it was technologically impossible to project a television image faster than the human eye could perceive.
“In a nighttime scene on television, watch the way the image of a car’s headlights lingers; that’s called comet-tailing”, the engineer explained. “See how long it takes before the headlights fade away.” Clearly there was no way that even the slower tachistoscope speeds of 1/3,000th of a second that Vicary had begun talking about in early 1958 could work on contemporary television.
…It has been estimated he collected retainer and consulting fees from America’s largest advertisers totaling some $34.16$4.51958 million—about $51.46$22.51992 million in today’s dollars.
Then, some time in June 1958, Mr. Vicary disappeared from the New York marketing scene, reportedly leaving no bank accounts, no clothes in his closet, and no hint as to where he might have gone. The big advertisers, apparently ashamed of having been fooled by such an obvious scam, have said nothing since about subliminal advertising, except to deny that they have ever used it.
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.