Download PDF file: How to save advertising dollars on Facebook and YouTube.
2015-09-28 Update thanks to Rubén Cuevas,
Fake views of ads by "bots" cost advertisers more than $6.3 billion US globally during 2015. Data show, video fraud-detection on DailyMotion, vimeo, YouTube and others fails to filter out invalid traffic properly. Here I distill our knowledge into 3 takeaways.
Check out what Sir Martin Sorrell WPP has to say about the matter.
According to Media Rating Council (MRC) and IAB (Interactive Advertising Bureau) standards, a viewable impression of a digital ad occurs when 50 percent of an ad’s pixels are on screen for one second.
In December 2014 Google published data regarding display ads in browsers (desktop and mobile). The study revealed that 56 percent of the display ads it served on its own and others’ sites never appeared within view on someone’s screen.
Nobody really knows for sure how Google or any other video platform or ad server come up with these numbers. For instance, Google provides explanations of what one should look for in these numbers it serves advertisers about their ads. How it collects them is, however, not explained.
1. What is the challenge?
The US Association of National Advertisers (ANA) released a report in December 2014, which estimated that
- 23 percent of video ads, and
- 11 percent of display ads
are viewed by “bots”. These are computer programs that mimic the behaviour of an Internet user.
The ANA estimated that this would cost advertisers about $6.3 billion US globally in 2015. This is a concern for two reasons.
1. Adertisers are spending ever larger amounts of money across both display and video advertising (see graphic below), and
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2. Spending for video ads is estimated to grow 21.9 percent compound annually from 2015 to 2020 (US data) (see also online video celebrities – chart below).
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2. Google and Facebook want a larger slice
Google and its YouTube platform want to garner the largest share possible of this growth in video advertising. Nonetheless, the competition will surely want to prevent this.
In April 2015, Facebook boasted it had over 4 billion video views each day. This number continues to grow.
For Google, display and video ads create tons of cash for the company, but things are changing. For instance, the rate for pay-per-click ads has been dropping (view chart as shown below). Google explains this was lower rates on YouTube than its other platforms.
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Some suggest that in the US, millenials spend nearly 60 percent of their time watching movies on either a smartphone, tablet or desktop/laptop.
To keep advertisers pouring more money into video ads, however, Google and Facebook have to up their game. Accordingly, both must provide strong evidence that their fraud detection systems work. Until fraud detection works, three things must be addressed as outlined below.
3. Focus on not getting charged for invalid video views
Each video platform wants to charge advertisers for video ads according to whatever the market will bear. In turn, advertisers want to keep costs for ads down, but this is becoming a challenge.
Apparently, some companies offer tens of thousands of YouTube views for as little as $5 US. Such data could in part help explain why 23 percent of video ads are viewed by fake consumers.
Of course, no advertiser wants to pay for these “views”.
How does one avoid paying for fraudulent views?
That is difficult to say, because…
Filtering invalid traffic before advertisers are ever charged is not getting easier.
Recent research sheds light on this important issue. Researchers uploaded two videos to each of five video platforms (YouTube, DailyMotion, Myvideo.de, TV UOL and Vimeo).
They bought ads on these platforms, which targeted the videos they had previously uploaded. Then, they directed their “bots” to these videos.
What are bots?
[su_box title=”EXPLANATION: What are bots?” box_color=”#86bac5″ title_color=”#ffffff”]
Bots are used by DrKPI, Google and Qwant Search to crawl the web.
They are little programs that allow DrKPI to collect data about blog entries (e.g., text, data of blog entry, etc.).
Google uses bots to index webpages. Bots can also be computer programs that mimic the behaviour of internet users viewing, e.g., a video ad.
About 60% of internet traffic is due to bots.[/su_box]
Each platform’s two videos were visited by the bots about 150 times. The researchers explain in their paper that the bots used were far from sophisticated tools as cyber criminals might use. Nevertheless, the results are worrisome for advertisers.
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If detection mechanisms work properly, marketers do not have to pay for ads on YouTube viewed by robots.
Data show that YouTube seems to have the best fraud-detection mechanism of the five platforms tested. It was followed by DailyMotion.
YouTube’s fraud detection tool identified 25 of the 150 bot visits to a video as real users viewing the video.
This means in 16.67 percent of cases, YouTube wrongfully identified a bot or robot to be a human watching a video.
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What is most unsettling, however, is that Google charged the researchers for 90 of the registered fake views. This is a 60.67 percent error rate!
So what is the bigger problem:
1. That YouTube wrongfully identified 25 robot “views” to be humans out of 150 times the video ads were “seen” = 16.67 percent error rate, or
2. Google AdWords, instead of charging for the 16.67 percent of views wrongly identified as humans by YouTube, deciding to charge for 90 views done by robots = a 60.67 percent error or false positive rate?
How could YouTube’s false positive-rate be so inflated? The process of counting views (i.e. public view counter and number of counted and monetized views) is opaque on YouTube.
Thanks to Rubén Cuevas for pointing out: “YouTube has two different mechanisms in place to discount views for the:
– public view counter, and also the
– monetised view counter”
Important is here to understand as Rubén pointed out to me, the public view counter seems to be more strict in the detection of fake views.
This is to say YouTube increases the count, and therefore, charges the advertiser for even more fake views than the public view counter would suggest.
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Read the research findings in detail:
Marciel, Miriam; Cuevas, Ruben; Banchs, Albert; Gonzales, Roberto; Traverso, Stefano; Ahmed, Mohamed and Azcorra, Arturo (July 2015). Understanding the detection of fake view fraud in Video Content Portals. Retrieved September 23, 2015 from http://arxiv.org/abs/1507.08874
Check out the FT article for non geeks, including comments by the researchers left here:
Cookson, Robert (September 23, 2015). Google charges marketers for ads on YouTube even when viewed by robots. Financial Times, p. 1. Retrieved, September 23, 2015 from http://www.ft.com/intl/cms/s/0/53ac3fd0-604e-11e5-a28b-50226830d644.html
[su_box title=”3 takeaways: Focus on verifiability of video views to fight off deception.” box_color=”#86bac5″ title_color=”#ffffff”]
1. Better process transparency for fraud detection
Having over 15 percent of bot views identified as “real” is a high error rate. While this is bad, YouTube is better than the rest.
YouTube seems to use a sufficiently discriminative fake view detection mechanism, but this applies only to the public view counter.
For the monetized view counter (i.e. those for which advertisers get billed), YouTube seems to ignore this mechanism for discounting fake views (see section 3 above – verifiability).
This is, of course, unacceptable for advertisers. Moreover, it makes the process of how YouTube detects these deceptors totally intransparent for advertisers.
Bottom line: With the help of third party verification, this challenge should be resolved quickly.
2. Improve measurement and use a set of standardized metrics
Even with third parties verifying numbers for advertisers, if our KPIs (key performance indicators) are not comparable we are stuck. For instance, Facebook defines a “view” as someone watching a video for three seconds or more. Others like YouTube talk about around 30 seconds before counting.
These different standards make it difficult for advertisers to get a clear feel and comparable numbers across platforms. Thus, even focusing on numbers, as Google suggests, is of limited value.
Bottom line: Define and agree upon the metrics used by the advertising industry. Make them comparable across social networks and video platforms.
3. Establish third party collecting, verifying and auditing of numbers
Facebook has followed the practice of self-reporting on viewability of ads, pages, reader engagement, and so forth. But as Volskwagen’s #dieselgate shows, self-reporting is always vulnerable to misuse, sloppiness and abuse of the system.
Bottom line: We need third party collecting and verification of numbers. Such efforts must in part focus on minimising charges for advertisers when ads are viewed by robots.
Eliminating fraud in online advertising is key
You are supposed to count the actual number of measured views of a video ad. Ergo, filter out invalid traffic from bots.
In December 2014, the ANA/White Ops study identified 23 percent of video ad impressions as bot fraud. Combine that number with the results from data reported here, and this means:
Google AdWords takes at least 60.67 percent of the 23 percent bot fraud views on YouTube and charges advertisers for them.
Thus, it follows that advertisers pay for at least 14 percent of video ads not viewed by humans!
The lack of transparency, standardized metrics and a regular audit of how video platforms handle fake ad views costs advertisers dearly.
Accurate metrics matter. For the first time ever worldwide mobile advertising will overtake print in 2016 ($71 billion US versus print shrinking to $68 billon US).
As well, social media advertising will top $25 billion US this year. Facebook is expected to take the biggest slice, more than $16 billion US. Instagram will account for “just” $600 million US.
Advertisers are justifiably wary and suspicious. Based on the above predictions, we better make sure that we pay only for those imprints, views, etc., that were executed by humans and not robots. Will #GoogleAW2015 tell us more about how YouTube plans to address this issue? Not really.
a) More content about advertising and viral content
b) Google: hidden ad costs
c) IAB’s efforts to establish a more trustworthy supply chain
d) YouTube frozen views
e) YouTube search for counted views – zero information provided
f) Facebook partners with Moat to verify video ad metrics
What is your opinion?
Now that you have read “Epic fail: Video view fraud detection“, I would like to ask you a question or two.
– As an advertiser, how do you deal with this issue? Please share!
– What type of video advertising works best for your business?
– What do you know about Facebook’s handling of this challenge?
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This post is also available in: Englisch