Google Ads Data Hub: a Revolution in Privacy-First Analytics
Due to GDPR policies, first-party marketing data aggregation across different platforms and sources can be a challenging task. But not with Google’s Ads Data Hub (ADH).
Google released this solution to upgrade its Google Marketing Platform (GMP), which suffered heavy losses after the rollout of GDPR regulations.
Unfortunately, GDPR crashed this idea, and Google had to find new alternatives for marketing data management. That’s how the company’s Ads Data Hub was born.
In this post, you’ll get a complete overview of Ads Data Hub, including its features and use cases, and you’ll learn how it helps large enterprises in their day-to-day marketing tasks.
What is Ads Data Hub?
💡Ads Data Hub is a software tool that offers marketing data analysts capabilities tailored to their business objectives. Created by Google, the solution is GDPR-compliant and doesn’t threaten user data privacy. 💡
To be more precise, Google Ads Data Hub is a data clean room that helps you aggregate marketing data from different sources, such as:
- Google Ads
- DV360 and Campaign Manager
💡 What is a data clean room?
Clean rooms are locations where large companies keep their aggregated marketing data. Advertisers then fill these clean rooms with first-party data to merge it with aggregated data. This allows marketers to understand, for example, whether they’re overspending on ads for an unpromising audience.
The main feature of data clean rooms is that they are highly private and don’t allow businesses to pull or view any customer-level insights. Many companies build their own data clean rooms to set up attribution (for example, Unilever).
What’s new with Ads Data Hub for marketers?
So, what’s so special about Ads Data Hub, and why couldn’t Google Marketing Platform comply with GDPR in its initial form?
Before Ads Data Hub, Google rolled out two ambitious solutions:
- Attribution 360
- Audience Center 360
The first one promised to address marketers’ age-old problem: attribution. There were great expectations for Attribution 360. At some point, Google even promised to attribute data from TV commercials and offline ads on an equal basis with paid ads and organic traffic.
As for the Audience Center 360, Google decided to create its own DMP platform. Google wanted to outperform Adobe, which also offers a wide array of marketing analytics tools. Previously, Google didn’t have a DMP solution, unlike Adobe. So, with all its data and engineering capabilities, Google decided to close the gap with Adobe through Audience Center 360.
However, not everything went smoothly for Google. These tools transferred logs with sensitive data between Google’s platforms. Attribution 360 could also accept log files from the web analytics software, internal data warehouses, CRM systems, DMPs, and more.
Since these marketing logs could be used to identify users, GDPR introduced strict regulations regarding cross-platform transfer of log data. In other words, it was almost entirely banned. That’s the main reason why Audience Center 360 and Attribution 360 no longer function.
But, Google didn’t stop trying to create a measurement and data aggregation solution. A short time later, the company rolled out Ads Data Hub.
Unlike its predecessors, Ads Data Hub doesn’t transfer the logs of each user. Instead, it uses logs as keys between two different blocks of data.
Ads Data Hub’s privacy checks ensure that you only receive data on 50 or more users. This prevents excessive amounts of individual information from being disclosed, while still providing enough context for most queries. In other words, analysts still get ads data that’s attributable to impressions.
It’s also important to know that Ads Data Hub is an API that connects two different BigQuery projects.
The first project belongs to Google and contains sensitive data that is subject to GDPR regulations.
The second project belongs to marketing analysts. It contains data on all marketing activities gathered by Google’s tools. Unlike the first project, all data here is click-based.
Finally, the Ads Data Hub API allows analysts to query data from both of these projects at the same time. Since the API is based on BigQuery, it uses SQL to execute queries. The API also offers a sandbox where you can test queries before running them. All insights can be loaded to a real-time Google Data Studio dashboard when it’s time for visualization.
Why does Ads Data Hub matter?
In light of recent privacy updates from Apple, Ads Data Hub has become more relevant due to its focus on privacy. Let’s single out all of the strengths of this solution.
The industry is constantly changing. Large companies regularly roll out their privacy updates and make marketers’ lives harder. For example, with the iOS 14.5 update, Apple disabled mobile app tracking. Later on, app acquisition costs increased ten times. iOS 15 blocked email open rate tracking, making it much harder to optimize email marketing campaigns.
Marketers will have to switch from cookie-based tracking to cookieless attribution shortly because companies are trying to preserve user data. Ads Data Hub might be one of the leading tools for privacy-oriented marketing in the next several years.
The only way to merge first-person and audience data
Ads Data Hub provides marketers with a unique ability to combine first-person data and audience data. First-party data are insights you own as an organization—or that your clients own, if you’re a marketing agency. They include website analytics, social media performance, CRM data, and other types of data.
On the other side, analysts have audience data. This includes information about the audiences they target in Display & Video 360 or Google Audiences.
Ads Data Hub allows them to merge these two data sets in a privacy-first way and get analysis-ready user-level insights.
Reach and frequency reporting
Basically, reach and frequency reporting isn’t anything new. Campaign Manager and Google Ads offer the same functionality.
In case you’re not familiar with this concept, reach and frequency reporting helps analysts understand how many people have seen your ads and how frequently the same group of people see the same ads over time. This information provides a clearer image of your marketing campaigns.
So, what is new about reach and frequency in Ads Data Hub? The API allows you to compile reports on a grander time scale. For example, Campaign Manager offers historical data for just 93 days, while ADH lets you look back at data for up to 13 months. This could be extremely important for industries with longer buyer cycles.
Since Ads Data Hub was originally conceived as a solution for YouTube marketing reporting, it offers comprehensive features for reporting on video inventory purchased through Google Ads, Display & Video 360, and YouTube services.
With the help of the ADH API, marketers can conduct audits of video marketing campaigns. Here are the ad types that fall into the audit scope:
- YouTube view in-stream ads
- Standard in-stream ads
- Bumper ads
- In-stream select ads
- Non-skippable in-stream video ads
The audit shows analysts the following metrics:
- Display & Video 360 impressions (net of general invalid traffic)
- Gross YouTube view views
- Display & Video 360 viewable percentage (net of general invalid traffic)
- Measurable impressions
- Non-measurable impressions
Google provides ADH documentation with the full list of metrics.
YouTube campaign reporting
As of January 14, 2021, Google doesn’t support third-party tracking pixels for YouTube measurement. In order to use third-party measurement tools, such as Adobe Analytics or Nielsen, analysts have to enable Ads Data Hub measurement first.
In other words, there’s no other way of tracking YouTube campaigns with third-party pixels. That’s why Google Data Hub has become the only option for marketers who need detailed, event-level reports on YouTube Ads data.
Explore how you can stream marketing data from Google Ads to any BI or data warehouse and calculate cross-channel ROI in a single dashboard.
How does Ads Data Hub work?
The process of querying data with ADH can be broken down into a chain of smaller steps. We’ll go through each of them to understand the tool from within.
- SQL query. Analysts start by building an SQL query in Google BigQuery. ADH requires marketing analysts to be tech-savvy and have expertise with programming languages for database management.
- Testing stage. Analysts log in to the testing environment to test their queries. The sandbox environment has a month’s worth of data that analysts can test their queries on. This stage allows newcomers to test the software, while experienced analysts can perform testing to understand where they may hit privacy restrictions in terms of the query output.
- Execution stage. Run queries on the Ads Data Hub instance. If the query successfully passes the testing stage, it can be executed on the ADH instance.
- Privacy checks. Before outputting the query result, Google runs privacy checks. ADH provides three different types of privacy checks:
- Static Privacy Check. This one takes care of immediate privacy concerns in queries even before running them in the Ads Data Hub API or UI. If something goes wrong, the platform throws an exception immediately.
- Aggregation Privacy Check. At this phase, Google ensures that every row of the ADH query result includes enough users to protect the end-user privacy. On average, analysts receive aggregated data for 50 users and above. However, when retrieving clicks and conversions data, the data set may include just ten users. All rows that don’t comply with the aggregation check will be removed.
- Differential privacy check. This privacy check ensures that analysts don’t gather information about specific individuals by running the same query multiple times for the overlapping data range. That’s why, when trying to aggregate the same set of users simultaneously, a few rows might be excluded from the final output unless you mention 'Filtered Row Summary'.
Google provides extensive documentation where you can learn everything about privacy checks in ADH.
- Data aggregation. After all privacy checks are complete, you can combine first-party data with Google’s proprietary data. The aggregated results return to your BigQuery data warehouse where you can perform a hit-level analysis of acquired insights.
- Data visualization. BigQuery connects with Google Data Studio, where you can create customizable real-time dashboards to monitor campaign performance with the help of insightful charts and graphs.
How to perform an analysis in Ads Data Hub
Analysts can research marketing insights by running query jobs in the ADH environment. The platform offers two options:
- Pre-built queries provided by ADH itself
- Custom queries on top of aggregated data
Data schemas in ADH are nearly identical to the schemas provided by the BigQuery Data Transfer Service. You can get through all of the table schemas in Ads Data Hub’s UI or with the help of Google’s documentation.
Nevertheless, ADH is different from BigQuery in terms of query settings. ADH offers two additional fields:
- Parameters. This setting allows the running of queries with arguments transferred via the UI or API.
- Row merge configuration. This setting takes care of rows that contain the data of fewer than 50 users. It either adds the values of excluded rows or assigns them a certain value (constant).
When your queries are ready, you can execute them as jobs. Jobs require additional information, which can be provided in the ADH UI:
- Query type (Analysis or Audience)
- Query name
- Ads data from (selection of the account with advertisement data)
- Destination table (a specific location in your BigQuery project where the aggregated data will be stored)
- Start date
- End date
- Time zone (to align start date and end date)
- Arguments (if any of them were added to the query)
Users can monitor the query execution process right in the ADH UI.
Use cases for Ads Data Hub
Even though ADH is a relatively new tool, it has already been used by a range of companies for their marketing needs. Let’s review some of the most successful use cases.
Customer journey mapping with ADH by Jellyfish
Jellyfish worked on this map for a consumer finance company. For example, if the customer applied for a credit card, the map shows the probability of the same customer applying for a car loan. Eventually, the company found new upsell opportunities and realized that spending on one product might result in sales of another one.
What’s the role of ADH here? The same map can be compiled with the help of Google Campaign Manager. However, in Campaign Manager, users are limited in terms of previous conversions measurement. Ads Data Hub was a perfect fit for this task.
The final results of the customer journey mapping showed that 17% of journeys cross-sold to another journey and 18%+ of mapped journeys started one week prior to the conversion.
Telco EE uses Ads Data Hub to target the right customers
EE is one of the largest Internet and mobile network providers in the UK. The company needed more confidence in its marketing efforts and a more granular picture of its campaigns’ performance.
Together with Essence, EE’s marketing agency, they decided to use Ads Data Hub to join first-party data with Google’s ad data in a privacy-first environment.
The analysis of insights revealed which group of customers were more likely to purchase new plans. Having analyzed this data, the analysts came up with more advanced bidding strategies and gained a 57% increase in return on ad spend.
Swiggy decreases CPA by 29% with Ads Data Hub
Swiggy, India’s largest food delivery app, faced high competition when COVID-19 emerged. The company needed a competitive advantage, so its advertising team decided to change its approach to marketing campaigns.
Swiggy implemented Ads Data Hub integration across Google Ads and Google Marketing Platform to aggregate Google’s private media data with insights from Swiggy’s CRM. Analysts picked audiences with the help of market segments (Affinity and In-market segments) in ADH and considered their potential profitability.
This move resulted in a 29% decrease in cost-per-acquisition (CPA) and an improved campaign performance.
Countless companies now use Ads Data Hub to improve their marketing analytics, including Booking, Integral Ad Science, Domino’s Pizza, and more.
For example, here’s what Marc-Antoine Lacroix, senior data manager at Booking.com, thinks of Ads Data Hub:
“With Ads Data Hub, we don’t actually have direct access to the customer information. What we obtain through these queries is aggregated information—information that we can use to improve, optimize, or make better decisions about our Google marketing campaigns.”
Streamline ADH marketing insights to your data warehouse
Google loads your ADH data exclusively to BigQuery. But, what should you do if you use Snowflake, S3, or any other data warehouse for the rest of your marketing data?
You can merge ADH data with the rest of your insights by integrating a marketing and sales ETL system into your data infrastructure. For example, Improvado aggregates data from 300+ marketing and sales sources, including CRMs, analytics solutions, social media, attribution platforms, and more.
The platform uses the APIs of different vendors to get up-to-date performance data on your marketing campaigns and load it to the data warehouse of your choice. And more importantly, the platform automatically normalizes all of the data rows and columns in your future report.
For example, if Ads Data Hub returns an empty row due to privacy restrictions, Improvado will automatically cleanse it so as to not confuse the final data view in the data warehouse.
Once you receive your analysis-ready insights in your preferred storage solution, the platform streamlines them to real-time dashboards that you can create in 15+ visualization tools. Tableau, Power BI, Google Data Studio, and other platforms automatically get fresh data, so you can track even the slightest changes in your digital marketing campaigns.
Schedule a call to learn how Improvado merges ADH insights with other ad providers, and what benefits it can bring to your business.