What is Google Ads Data Hub?
Google Ads Data Hub is a secure data analysis tool designed for advertisers, agencies and measurement partners. It enables you to unearth valuable insights beyond what’s available in your ad platform.
What set’s Google ADH apart from other data analysis platforms is the ability to use first-party data in a privacy-safe environment. Campaign performance can be aggregated and measured from the following Google-owned channels:
- Google Ads
- YouTube Reserve
- Campaign Manager 360
Advertisers can then combine their first-party data – such as purchase data from a customer database – with aggregated data from the channels listed above, to get a clearer picture of performance alongside more in-depth insights.
How does Google Ads Data Hub work?
Let’s get into the specifics of how Google Ads Data Hub actually works.
What powers Ads Data Hub is BigQuery – a Google-owned cloud database (built on Google Cloud Platform) that enables data processing and analysis.
As mentioned above, Google ADH takes data from DV360, CM360, YouTube, and Google Ads. This platform-side data is then stored on the cloud as a Google-owned BigQuery project.
First-party personal data is hashed (which makes it privacy-safe) and then combined with data from ad platforms to provide important insights into audience behaviour and campaign performance.
Scheme showing what Ads Data Hub can do | Croud
The output of this combined data is that it can then be downloaded, plugged into a dashboard – such as Google’s Looker Studio – or even sent back to ad platforms for data activation.
How to run a query in Google Ads Data Hub
Here are the steps involved in creating and running a new query in Ads Data Hub:
- To create a query in Ads Data Hub, start by navigating to the Queries tab.
- Click on the "+ Create query" button to open the Analysis query templates page.
- Expand to preview the template SQL before selecting a template. You can use custom tables to create the query. Although one thing to note, it’s now best practice to take some of the syntax out and use temporary tables instead of just using the template tables.
- Next, choose the template you want to use by clicking on the "Use template" button or select the "Blank" option to create a query from scratch.
- Give your report a name that will help you easily identify it.
- Write or modify the query using BigQuery compatible SQL. You can use the available tables and fields provided in the Google tables tab.
- If necessary, configure parameters to further customize your query.
- You can also configure the filtered row summary if needed.
- Once you're done with your query, click on the "Save" button to save it.
For more information on how to run queries in Ads Data Hub, here is a resource from Google.
Benefits of Using Ads Data Hub
We’ve touched upon some of the benefits of using Google Ads Data Hub above, but here’s a recap of some of the main benefits.
A lot can be said for the online advertising landscape today concerning user privacy. It’s been a hot topic for many years and I expect it’ll continue to be for years to come. So one of the main benefits of using Ads Data Hub is that it’s GDPR-compliant and safe from a privacy perspective.
Google states marketers and measurement partners will benefit from rigorous privacy checks that protect the personal data of users online while still being able to perform comprehensive analytics.
In many ways navigating user privacy in digital marketing is becoming more challenging – from GDPR to the game-changing iOS14 update. The fact Google Ads Data Hub respects privacy is a big plus.
2. Combined data
On its own, platform-side data can be insightful since its rigorous event tracking makes it possible to understand campaign performance and successfully optimize campaigns. However, combining platform data with owned first-party data has the benefit of essentially supercharging your learnings.
Even with seamless event tracking, Google ADH will provide an even better understanding of performance and user behaviour. It bridges a gap in data that many businesses and advertisers struggle to connect. Suddenly insights will become clearer and more valuable and in theory decision-making as a marketer will become easier.
3. Audience behaviour
After combining platform and first-party data, we are left with even more insight into audience behaviour. It’s possible to get clarity over how audiences interact with ads across various channels, as well as how audiences behave across different devices. It makes it easier to understand which audience segments convert best in general.
And this richer insight into audience behaviour – something that is often a grey area in advertising – is one of the key benefits of using Google ADH.
Last but not least (and in my opinion the main benefit of using Google ADH) are the gains that come after pulling reports and analyzing data. Joining up data, getting more in-depth insights and better understanding audience behaviour is all well and good. But it’s what you do with this knowledge that matters most.
Following data analysis, advertisers can use this data to make optimizations and improve performance. Whether it’s doubling down on what drives high-value customers, or pulling back on areas that underperform to improve ROAS.
Use Ads Data Hub for Insight into Overspending
Google Ads Data Hub isn’t designed to manage ad spend, nor is it designed to prevent your campaigns from overspending. However, by providing insight into current and past performance, as well as audience behaviour, it can provide direction in terms of where best to spend.
Advertising strategies can therefore be refined and advertisers can determine where best to spend to maximise ROAS.
Here are some practical ways that Ads Data Hub can be used for smarter spending and to reduce overspend:
- Analyze audience dimensions and segments such as age, location, device, schedule and interests – essentially all data segments that are available to you and relevant to your objective. Try to uncover how to spend more efficiently on these dimensions and segments and optimize campaigns accordingly.
- Similarly, analyze placement performance to identify which websites, apps, videos and other placements perform as well as those that underperform. Following this, refine your campaigns by pulling back spend on the worst-performing placements and instead focus on the top performers
- Keyword and search query performance can be analyzed in the same way to pull back spend on poor-performing keywords. For example, a keyword may do an excellent job at driving leads, however by combining this with customer data, it’s possible to understand which leads convert into customers. In this example, use this insight to reduce spend on keywords that generate poor-quality leads
- Understand the channel performance and determine the channels that perform best. For example, you might find that YouTube retargeting generates more loyal customers compared to Display retargeting. If this is the case, prevent overspending on Display and instead invest more budget into YouTube
- Find your best audiences by leveraging first-party purchase data, such as past purchases, repeat purchases and high-value purchases, and match that back to specific channels, campaigns, placements and audience segments. Again, work out how to optimize ad spend so that you focus on your most profitable customers
- Model new audience segments based on the past performance of your audiences. Then re-energize your acquisition strategy by focusing ad spend on these modelled audience segments. Using real first-party data to define audiences is a smarter way of investing budget
Use the insights for multichannel marketing optimization
You can go one step further when optimizing your Google Ads campaigns. Whether you’re running a Performance Max campaign or Google Shopping, you can use your product feed enriched with performance data from Ads Data Hub to segment your products or even your bid strategies.
By creating custom labels that incorporate performance data into your feed, you can better group your campaigns, which will improve overall performance.
Examples of custom labels | Google
Examples of segmenting campaigns that use custom labels include allocating more spend to best selling products, or products that are high in-stock, and tailoring your feed for your best-performing audience. Read more on the most helpful custom labels to apply to shopping campaigns.
Other Google Ads Data Hub use cases
There are numerous other use cases for Ads Data Hub that can enhance data insights and learnings, again benefiting you with ways to make spend more efficient and boost revenue:
- Build custom reporting across different browsers and mobile apps
- Run cross-publisher basic custom attribution across browser and mobile app touchpoints
- Measure incrementality and understand how each touchpoint in a customer journey influences conversions
- Get insight into how various campaigns overlap with one another
- Better understand video performance of YouTube campaigns, with reporting minus the use of tracking pixels
3 Google Ads Data Hub Case Studies
Google Ads Data Hub can be used in a multitude of ways to supercharge your data and empower your decision-making. But don’t take my word for it.
Let’s look at some case studies of how three well-known brands used Ads Data Hub to their advantage, generating impressive results.
EE Case Study
UK mobile network EE gained a granular picture of campaign performance using Ads Data Hub and combining platform data with first-party data. They worked out which customers were most likely to upgrade their phone plans and then used this insight to fine-tune their acquisition strategy. The result was a +57% increase in ROAS.
From a spend perspective, Ads Data Hub enabled EE to spend on the right acquisition channels and therefore not overspend on areas that were less likely to yield new phone contracts.
Rituals Case Study
Bath and body retailer Rituals increased both online and offline sales using Ads Data Hub, achieving a massive 85% increase in conversions with a 15% decrease in CPA.
They achieved this by using first-party data from Google Marketing Platform, their CRM and point-of-sale transactions. Paired with Google Cloud’s machine learning technology, Rituals were able to make predictions around the likelihood of customers purchasing both in-store and online.
Following the creation of audience segments using these learnings, a campaign was created in DV360 targeting specific groups that matched their customer modelling, with tailored messaging.
Domino’s Case Study
Domino’s pizza – the Canadian division – combined data from several different sources to find out when customers were most likely to order again, so they could be best prepared for them in the future.
In the process of analysing data, Domino’s uncovered an interesting insight: customers who ordered at least twice online in the past 30 days made up 35% of their total revenue.
This was a significant discovery for Domino’s Canada because they’d previously underestimated the value of this audience segment, which they subsequently focused their attention on.
Google Ads Data Hub is a powerful tool for gaining a more comprehensive understanding of your advertising campaigns and audience behaviour, therefore enabling data-driven decisions using learnings and insights. Use these insights to optimize your campaigns, such as leveraging DataFeedWatch to update custom labels for further campaign segmentation.
Though Ads Data Hub is more advanced and extra effort is needed to implement it, the impact it can have on performance makes the extra effort worthwhile as highlighted in the case studies above. This is especially true for the big players with an abundance of data that want to streamline their multichannel marketing efforts.
Read also about Google Ads Attribution Models in 2023