Description of Methodology

The following is a summary of industry standards for measuring video impressions and viewability.

The current Media Rating Council (MRC) accreditation certifies that:

YouTube Reserve video impression and video viewability measurement as reported in the YouTube Reserve Video Viewability Report adheres to the industry standards for video impression and viewability measurement.
The processes supporting these technologies are accurate. This applies to Google’s measurement technology which is used across all device types: desktop, connected TV, mobile and tablet, in both browser and mobile apps environments.

You'll find a summary below of the video measurement process employed by YouTube Reserve.

What is Google accredited for?

The accreditation certifies that Google's Video impression and viewability measurement technology adheres to the industry standards for counting video advertising impressions and measuring viewability rates.

Video Viewability Metrics have been accredited for Desktop, Mobile App and Mobile Web environments only.

What is included in the audit process?

Only the 'YouTube Reserve Video Viewability Report' is accredited for MRC Video Metrics. The definitions of these metrics are presented in the Glossary section of the Video Viewability report.

The audit includes all measurement, aggregation and processing related to YouTube Reserve Video Viewability report, which includes Skippable in-stream, Non-Skippable in-stream and Bumper video advertising sold via direct reservation.

What is not included in the audit process?

Google's non-video impression-based advertising solutions, such as Google Marketing Platform, and systems which measure clicks for non-commercial purposes (such as Google search) are outside of the scope of this audit. Other systems outside the scope of this audit include related support and management systems such as Google Analytics. In addition, the following items are not part of accreditation:

Other client reports (Unique Reach, Brand Lift, Placement and others) for YouTube reservation
Targeting, Brand Safety
Other device types
Click metrics
YouTube TV, Sponsorships, YouTube Kids and Mastheads

Video impression and viewability measurement methodology

Google uses an internal booking tool to plan and reserve YouTube campaigns on behalf of advertisers who opt for the direct reservation service.

Google’s proprietary Interactive Media Ads Software Development Kit (IMA SDK) is integrated directly into the YouTube video player, the YouTube mobile app or video partner sites and apps to facilitate communication between the video players and the ad server for video measurement.. The IMA SDK is a Video Ad Serving Template (VAST) (versions 2.0, 3.0 or 4.0) with a compliant tag implementation used to measure both linear and non-linear video ad content to serve and track digital video ads. The IMA SDK also supports Video Player Ad-Serving Interface (VPAID) (version 2.0) that allows the video ad and video player to communicate with each other, as well as Video Multiple Ad Playlist (VMAP) that allows multiple ads to be played within the video ad content.

All measured YouTube video ads included in the video viewability report are delivered in-stream. For video ad impressions, measurement utilises the count-on-begin-to-render methodology. The Google Ads IMA SDK solutions are consistent with the Video Impression Guidelines requirements regarding post-buffering initiation of the measurement event. YouTube Reserve Video viewability report uses a combination of user-agent and mobile app SDK data from internal and external sources to classify device types. YouTube Reserve Video viewability report does not rely on any third party to perform classification.

In some instances, continuous play is a factor, such as when Auto-play is active or the user is viewing a video in a playlist. When this is the case, certain rules are followed. When using Wi-Fi, continuous play will stop automatically after 4 hours. When using a mobile network, continuous play will stop if you have been inactive for 30 minutes. Approximately 17% of video traffic is autoplay. Refer to the Connected TV section for cases when the device is powered off.

For video viewability, Google Ads utilises the Active View description of methodology to measure viewability as reported within the YouTube Reserve reporting platform. YouTube Reserve Video Viewability report counts a viewable video impression when at least 50% of the video ad creative appears within the viewable area of a user’s browser/app for two continuous seconds.

Filtration methodology

Google tries to identify and filter both general and sophisticated invalid traffic continuously through data-based identifiers, activities and patterns. This identification and filtration is done across video impressions, and includes non-human activity and suspected fraud. However, because user identification and intent cannot always be known or discerned by the publisher, advertiser or their respective agents, it is unlikely that all invalid traffic can be identified and excluded from the reported results proactively. In order to protect invalid traffic filtration processes from becoming compromised or reverse engineered, no details of specific filtration procedures, beyond those detailed here, will be disclosed, other than to auditors as part of the audit process.

Both specific identification (including obeying robot instruction files, filtration lists and publisher test activity) and activity-based filtration methods (including analysing multiple sequential activities, outlier activity, interaction attributes and other suspicious activity) are utilised in filtration.

In addition, the following parameters apply to the filtration methodology:

Third-party filtration is not used by Google.
Sources used for identification of non-human activity: Google uses the IAB/ABCe International Spiders & Robots List, as well additional filters based on past robotic activities. The IAB Robots List exclude file is used.
Activity-based filtration processes: Activity-based identification involves conducting certain types of pattern analyses, looking for activity behaviour that is likely to identify non-human traffic. Google's Ad Traffic Quality team has systems in place to determine any suspicious activities and does such activity-based filtering appropriately.
All filtration is performed 'after-the-fact' and passively. That is, the user (browser, robot or others) is provided with their request without indication their traffic has been flagged or will otherwise be filtered and removed as Google does not want to provide any indication to the user agent that their activity has triggered any of Google's filtering mechanisms. In some cases front end blocking is also utilised, when it is likely that the resulting ad request may lead to invalid activity. Historically, less than 2% of ad requests are blocked.
Processes have been implemented to remove self-announced pre-fetch activity.
When inconsistencies or mistakes are detected, processes exist to correct this data and provide refunds to advertisers. These refunds are reflected in the billing summaries. The corruption of log files is extremely rare, but in cases where this may occur, processes exist to recover them.
Processes have been implemented to remove activity from Google-internal IP addresses.
Filtration rules and thresholds are monitored continuously. They can be changed manually, and are updated automatically on a regular basis.

Note: The GIVT and SIVT decision rate for YouTube Reservation traffic is 100%.

Machine learning

Google uses supervised AI techniques¹ through methods such as Classification (for example, Neural Network approach), in which the model will predict invalid traffic (IVT) by making a yes/no decision about whether an event is invalid, and Logistic Regression, in which the model scores various activities and then an IVT decision is made based on score thresholds. Supervised Google’s AI models may also use tree methods and graph methods.

Data sources used for Google AI include logs of queries and interactions ('ads logs'), non-logs data that can be joined with ads logs, and a variety of other supplementary proprietary signals. Google relies on hundreds of data sources of varying sizes: the total number of records per data source ranges from thousands to trillions, depending on the data source. Traffic-based models are required to be evaluated with a minimum 7 days of traffic as input data.

For active defences Google maintains monitoring procedures over the traffic signals (training data) feeding into the models, which trigger alerts for human intervention if certain threshold bounds are not met. As a result minimal, if any, reduced accuracy is expected.

Models are continuously retrained when appropriate and practical, and model performance is regularly or continuously assessed. As a result (similar to our monitoring procedures above) minimal, if any, reduced accuracy is expected.

Biases in Google AI training and evaluation data are minimal and if they are material the IVT defense would not be approved. All the Google AI projects ('launches') go through a cross-functional review process before they are approved. As part of this process, bias for the model(s) and corresponding data are evaluated, and projects must meet predetermined ad traffic quality criteria before being approved. Continuous monitoring is in place to detect the emergence of bias in models, which in turn trigger alerts and model evaluation, analysis and updates.

Google applies a mix of AI and/or human intervention/review techniques on all traffic. For some defenses Google relies on AI-based lead generation followed by human review. Other defenses start with human review data and use Google AI to generalise. Our application of AI and human intervention/review techniques is evolving, and our usage shifts according to multiple criteria, including alerts, escalations and organic fluctuations in types of invalid traffic that may emerge. As a result, the distribution is not in steady state, and the 'level' of reliance on either Google AI or human intervention/review fluctuates over time.

¹Supervised Google AI relies on labelled input and output data, meaning that there is an expectation for what the output of an AI model will be.

Business partner qualification

YouTube platform level ad policies apply to all parties. Learn more about the ad policies for advertisers.

Google filters for invalid traffic on an ongoing basis, and will review any business partners that receive high amounts of invalid traffic. Partners who continually receive high amounts of invalid traffic may have their account suspended or closed.

Video data reporting

Different types of YouTube reservation reports (such as Unique Reach, Brand Lift and others) are made available to the advertisers by their Google sales point of contact.

For the purposes of MRC accreditation, only the metrics listed in the video viewability report are in scope. An immaterial amount of YouTube TV traffic may be present in the video viewability report for accredited Desktop and Mobile environments. Other reporting of video impression and viewability metrics across the other available YouTube Reservation reports are excluded from accreditation audit.

The metrics in the YouTube Reserve video viewability report are reported Total Net of SIVT across Desktop, Mobile Web and Mobile In-App environments. Approximately 23% of total invalid video impressions traffic is estimated to be general invalid traffic.

The following video ad formats are measured and reported in the YouTube Reserve video viewability report. All other ad formats not described below are excluded from the YouTube Reserve video viewability report, including ads served into the YouTube Kids mobile application.

Skippable in-stream ads: In a skippable video ad, viewers are given the choice to skip the ad after the initial 5 seconds. After the view of a skippable ad, it will increment the YouTube view count at the 30 seconds mark or when the ad has been watched completely (creatives must be at least 12 seconds long to increment view counts). Skippable video ads can be a maximum of 6 minutes long.
Bumper ads: Short video ads that are approximately 6 seconds in length that appear before, during or after YouTube video content. Bumper ads are non-skippable ads.
Non-skippable in-stream video ads: In a non-skippable video ad, viewers are not given the choice to skip the ad. Note that non-skippable video ads do not increment the view count. Non skippable ads can be a maximum of 15 seconds or 20 seconds, depending on the region where the Ad is being shown.

The reporting time zone for the 'YouTube Reserve Video Viewability Report' will be the same as the time zone of the related campaign. A watermark tick is included in the 'YouTube Reserve Video Viewability Report' for quality check on the base metrics (Impressions, Invalid impressions, Measurable Impressions, Non-measurement impressions, Viewable impressions, Non-Viewable impressions) as well. You can compare the total numbers of these base metrics in the watermark with related numbers in the reporting table. In case of any discrepancies, reach out to your Google sales point of contact. The 'YouTube Video Viewability' report is available based on customisable date ranges. Contact your Google sales point of contact to request the report.

Connected TV

We have submitted Connected TV (CTV) for MRC accreditation and these metrics will be added to the Video Viewability Report upon receiving accreditation. CTV devices that are certified to carry YouTube must inform the app when the app isn’t visible (for example, user switched HDMI inputs, user turned device off). This ensures that YouTube stops video playback (and by extension, ads aren’t served) when the app isn’t visible. In rare instances, Google is not able to determine if a TV device is off. There are no latency measurement limitations.

Communication around change in methodology

Any changes in the methodology is communicated via Google Ads Help Announcements.

Placement Position Reporting Disclosure

At this time, Google doesn’t report on the placement position of video ads. In-stream ad position in Google’s video ads is driven partly by optimisation by Google AI on behalf of advertisers and partly by available inventory from our users’ fluctuating behaviour patterns. Google’s serving algorithms allocate ads according to these factors for each individual Impression. This is unlike spot position reporting on linear TV, where the spot is fixed for every viewer.

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