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, 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?
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,etc) for YouTube Reservation
- Targeting, Brand Safety
- OTT and 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 utilizes 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 Autoplay 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 four hours. When using a mobile network, continuous play will stop if you have been inactive for 30 minutes. Approximately 14% of video traffic is autoplay. Please refer to https://support.google.com/youtube/answer/6327615 for the latest and most accurate details on this feature.
For video viewability, Google Ads utilizes 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.
- 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 behavior 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, etc.) 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 frontend blocking is also utilized, 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.
Google uses supervised machine learning techniques 1 through methods such as Classification (e.g., 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 machine learning models may also use tree methods and graph methods.
Data sources used for machine learning 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 defenses 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 machine learning training and evaluation data are minimal and if they are material the IVT defense would not be approved. All machine learning 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 machine learning and/or human intervention/review techniques on all traffic. For some defenses Google relies on ML-based lead generation followed by human review. Other defenses start with human review data and use ML to generalize. Our application of machine learning 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 machine learning or human intervention/review fluctuates over time.
1 Supervised machine learning relies on labeled input and output data, meaning that there is an expectation for what the output of a machine learning 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
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 35% of total invalid video impressions traffic is estimated to be general invalid traffic.
- 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 Ad is being shown.
The reporting time zone for the “YouTube Reserve Video Viewability Report” will be the same as the timezone of the related campaign. A watermark check 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 customizable date ranges. Please contact your Google sales point of contact to request the report.
Over-the-top / Connected TV
We have submitted Over the Top (OTT) / Connected TV (CTV) for MRC accreditation and these metrics will be added to the Video Viewability Report upon receiving accreditation. At this time, Google is not able to determine if a TV device is off. There are no latency measurement limitations. The same Auto-play and continuous play rules listed above apply in CTV/OTT environments.
Communication around change in methodology
Any changes in the methodology is communicated via Google Ads Help Announcements.