This document is a summary of the measurement and reporting of Ads Data Hub and its associated processes.
Introduction
Ads Data Hub follows industry standards for accessing and querying campaign level data. This data is sourced from upstream platforms such as YouTube and in-stream video inventory of Google Video Partners purchased through Google Ads, Display & Video 360, and YouTube Reserve services. These platforms are accredited by the Media Rating Council (MRC). This accreditation certifies that Google's measurement technology adheres to the industry standards for counting interactive advertising metrics, and that its processes supporting this technology are accurate.
What's included in the audit process?
The audit is focused on querying event level data sets through the Ads Data Hub user interface and the integration of non-Google data for the purpose of retrieving aggregated YouTube in-stream video campaign impressions and viewability results. This includes:
- Reporting of impressions and viewability related metrics across desktop, mobile web, and mobile application environments net of general invalid traffic1 and sophisticated invalid traffic techniques served using the Google Ads, Display & Video 360, and YouTube Reserve platforms.
- Processes to facilitate the matching of unique device identifiers across Google first-party and Google client data.
The following table describes in-scope and out-of-scope traffic.
| In-scope | Out-of-scope | |
|---|---|---|
| Environments |
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| Buying flows |
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| Ad format |
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The following viewability metrics are included in the audit:
- Gross impressions
- Viewable impression distribution
- Viewable impressions
- Viewable rate
The following invalid traffic (spam) metrics are included in the audit:
| Metric | Google Ads | YouTube Reserve | Display & Video 360 |
|---|---|---|---|
| Impressions (net of general invalid traffic) | |||
| Total valid impressions | |||
| Total invalid impressions | |||
| Measurable impressions | |||
| Unmeasurable impressions (net of general invalid traffic) | |||
| Viewable impressions (net of general invalid traffic) | |||
| Non-viewable impressions (net of general invalid traffic) | |||
| Viewable percentage (net of general invalid traffic) |
Business Partner Qualification
Ads Data Hub business partners include Advertisers, Agencies, Partners, and Third-Party Ad Tracking or Ad Serving Vendors2. Google vets the use cases from all Partners and vets initial qualifications to access the product. All partners that use Ads Data Hub have read-only access and are able to query Google ads data or join their own first party data with event level ad campaign data. Additionally, all partners must agree to Google's terms and conditions prior to accessing the platform. Google filters invalid traffic on an ongoing basis.
Mobile identifiers
In the context of Ads Data Hub, matching through device IDs is utilized as a mechanism to map YouTube ad activity served through Google Ads, Display & Video 360, and YouTube reserve (e.g., impressions, clicks, ad interactions) to a list of device IDs maintained by Agency and Advertiser clients.
Measurement methodology
Ads Data Hub allows Advertisers, Agencies, and Third-Party Ad Tracking or Ad Serving Vendors to input their data into BigQuery and join it with event level ad campaign data. An Ads Data Hub query is aggregated over a group of users, which allows Google to provide more complete data and still maintain end-user privacy. Ads Data Hub is two BigQuery projects, connected by an API. Google uploads and manages Google ads data in one of the projects, while the customer uploads and manages their own data in their project.
While Ads Data Hub enables customers to do user-level analysis in a privacy-centric way, this access is very sensitive, subject to ecosystem changes, and is therefore unusual among Google products. Ads Data Hub can ensure counting events like impressions and clicks matches other Google reporting, but counting distinct user identifiers in Ads Data Hub may not exactly match unique counting metrics in upstream Google reporting platforms due to specific precautions implemented in Ads Data Hub for privacy-related reasons, given the allowance of event-level data manipulation.
Regarding user matching in Ads Data Hub, users who opt-out of advertising cannot be matched. Match requests sent from apps are unlikely to match, because mobile devices isolate app traffic and identifiers. For iOS events, match data must originate from apps on iOS 14.5+ from users who have granted permission under Apple's App Tracking Transparency framework. The launch of iOS 14.5 in late April 2021 has not, on its own, had a significant impact on Ads Data Hub cookie or RDID matching on the whole, but Google expects matching rates to vary among users segmented by preferred browser, where browsers such as Safari or Firefox actively manage cookies. Ads Data Hub will continue to evolve given Safari, Firefox, and Edge have already deprecated 3rd party cookies and Chrome is anticipated to deprecate 3rd party cookies in the future.
Learn more about how Ads Data Hub works
Ads Data Hub provides access to data from multiple Google products (Display & Video 360, Google Ads and YouTube Reserve), all of which adhere to the appropriate industry standards for click, viewability, and impression measurement. As such, Ads Data Hub shares click, viewability, and impression counting methodology with the products whose data we provide. The details can be found here:
- Google Ads Description of Methodology
- Display & Video 360 Description of Methodology (Including Active View Methodology)
- YouTube Reserve Description of Methodology
Google dynamically optimizes ad placement position to meet advertising goals and user interaction / consumption patterns.
Filtration methodology
Ads Data Hub provides a privacy-centric way for Advertisers and Agencies to query event level data and get aggregated results. Ads Data Hub utilizes the same AdSpam logs as upstream products such as Google Ads, Display & Video 360 and YouTube Reserve. Please refer to the “Filtration" sections within the above Description of Methodology for each of the upstream products.
The filtration methodology reporting is performed with two separate processes, one that occurs at log-processing time and one that occurs at post-processing time.
- When processing ad events from ad logs initially, real time joining is
performed with joined spam logs to annotate events as spam.
- Empirically tested methods confirm that this real time joining operation captures about 98% of spam events.
- The post-processing pipeline further performs corrections on processed ad
event data. The post processor pipeline looks back a sufficient number of
days to ensure that sufficient spam events are captured. This post processor
correction pipeline is running daily. This is because there may be
situations in which discrepancies may arise in the levels of AdSpam
reflected within Ads Data Hub in comparison to upstream platforms due to
timing differences. However, internal analyses conducted estimate on average
an immaterial level of discrepancy (<1%) at an event level.
- Empirically tested methods confirm that this post processor pipeline captures virtually 100% of all available spam event corrections within 7 days time.
- When an event is marked as spam, it is excluded from the official reporting stats query results.
Additionally, when taking all spam processing described above as a whole, the impact of ongoing spam revision to MRC metrics is minimal. Empirically tested methods show sampling despammed impressions one day after the ad events changes by less than 1% when resampling the same event day on subsequent reporting days. And again, revision typically ceases in a few days and virtually 100% ceases within 7 days.
Google monitors spam processing accuracy and impact on a regular cadence.
Google upstream products such as Google Ads, Display & Video 360 and YouTube Reserve have a decision rate of 100% (based on the reviewed sampled data). Since Ads Data Hub utilizes the same AdSpam logs as upstream products, the decision rate for Ads Data Hub Google first-party traffic is 100%.
Privacy check filtration
Ads Data Hub's privacy checks apply to the collection of MRC-accredited metrics. Rows that aren't aggregated enough to protect end-user privacy (must contain data on 50 or more users), or don't meet Ads Data Hub's other privacy checks, will be dropped. This applies to filtered row summaries within the query and API results.
However, the immutable queries that produce MRC-accredited metrics are less likely to be filtered than custom queries. This is due to the queries using only a few key slicing dimensions, such as full days or device-type, such that re-running the queries with variations in parameters is unlikely to accidentally isolate small groups of users.
The likelihood of the MRC-accredited metrics being filtered from your results may increase if:
- The campaigns you're measuring have a low number of events, such as in the case of campaigns with low budgets or narrow targeting.
- Custom queries are run and re-run prior to running an MRC query over the same events.
Changes to the methodology
In the event of changes to the measurement methodology, Ads Data Hub will notify customers via the release notes, in addition to Account Manager and Support communications.
Ads Data Hub reporting
For general information regarding how Ads Data Hub reports data, refer to the overview.
Find instructions on retrieving viewability and IVT data via the Ads Data Hub API or UI for various buying frontends here.
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Source events will be revised for up to 7 days as part of invalid traffic filtration (Google Ad Traffic Quality). While both Ads Data Hub and upstream Google platforms use the same primary sources to revise invalid traffic over a period of days, until revision has settled the exact amount of invalid traffic observed at a given moment may differ. ↩
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Third-party Ad Tracking or Ad Serving Vendors are included in the scope of this audit, whereas Third-Party Brand Measurement vendors are not. ↩