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ADH: Next Generation Privacy First Ads Measurement Platform

To make campaign execution & set-up easier Google is heavily automating campaigns. Hence these days it is more focused on automated campaigns such as UAC & Performance Max. This way marketers get to spend more time understanding their users by analyzing data. Through automation, Google aims to enhance simplicity on the dashboard/reporting tools while compromising on visibility.  

Limited data is available in ad platforms such as Google Ads, DV360, and DCM. Even though we can create custom reports on these platforms, there are various restrictions on the dimensions and metrics. Data is always presented within a predefined format. 

Marketers are unable to create a report by combining data from multiple platforms. To analyze data according to our needs, we will need data in the raw form. However, this is a violation of user privacy. 

How can one access and analyze this data?  

As a solution, Google has introduced Ads Data Hub (ADH).  

Ads Data Hub is a privacy-first data warehousing solution built on the Google Cloud Platform. It provides tools to create custom reports that do not contain personally identifiable user information.  

ADH Privacy Considerations 

privacy consideration

Think of Ads Data Hub as a bridge. In the same way that a bridge links two cities, Ads Data Hub links your data to Google's. And like the safety guardrails on a bridge, Ads Data Hub incorporates safety features to make sure the reports you create do not contain information that can be used to identify specific users. 

Ads Data Hub provides user data in an aggregated form, giving you an unprecedented level of insight into how users engage with your ads across Google products. 

ADH imposes certain checks and restrictions which have been designed to help prevent the transmission of data about individual users in the data that you get out of the platform. 

Let's look at how Ads Data Hub works 

Introduction to ADH

Source: Google 

Google uploads data from your various ad products such as YouTube, Google Ads, and Display & Video 360 into a Google-managed data set, which you can only access in aggregate.  

Then you upload your personally managed data into a separate BigQuery project. Now, we use Ads Data Hub to connect these two data sets.  

 For example, let's say you want to measure the reach and frequency of a campaign. First, Send your first-party conversion data to BigQuery.    

Next, write and run a query in Ads Data Hub to measure campaign performance with first-party data. Finally, Ads Data Hub links your data with Google's data and sends you privacy-safe actionable insights through BigQuery.

Some basic use cases with ADH  

Reach and Frequency Analysis 

The objective of this analysis is to identify the optimal frequency for driving conversions. Through this analysis, we can obtain frequency-wise reach distribution with the breakdown for Campaign, City, and specific platform/publisher. The optimal frequency garnering us the maximum conversion can be determined with all the breakdowns. 

What this analysis will do:

• We can identify the optimal frequency for campaigns. 

• We can identify the over-exposed and under-exposed cities. 

• All these data points can be segregated using campaigns as a breakdown. 

• ADH data helps the media team to optimize campaigns. 

• We can shift our budgets from over-exposed demographics to under-exposed demographics and maintain optimum frequency.

What the results will look like: 

Results 1Results 2Results 3

Overlap between users 

This analysis helps identify overlap between brand and performance campaigns audience and calculates the lift in performance campaigns when exposed to branding campaigns.

What this Analysis will do: 

• Identifying overlap between Brand and performance campaigns. 

• Measure lift in conversions caused by performance campaigns when users are exposed to brand + performance campaigns.  

• Continuous learning process on the efficacy of media platforms and audience targeting. With data from campaigns, we are able to take data-backed decisions on the choice of media/targeting. 

• Selection is based on ADH data where we prioritize the ones which have higher overlap with performance campaigns. 

What the results will look like:

Overlap analysis results

Audience Profiling  

The aim of this analysis is to Identify optimum performing buckets in terms of age, gender, affinity, and in-market segments. 

What this Analysis will do:  

•Identify the best and worst performing affinity and in-market segments. 

• Identify the under and over-exposed segments in terms of demographics and affinity and in-market segments to highlight the buckets where we would have hit the plateau. 

• Through this we get the buckets which have higher KPI(CVR) and ideally, we should include the same in our targeting. This is done with the breakdown of campaigns. 

• Based on the insights from ADH data, we remove the buckets with minimal CVR and include the segments where maximum CVR is observed. 

What the results will look like: 

Profiling Results 1Profiling Results 2

In conclusion, the shift in consumer behaviour has changed for good. Marketers need tools to help them make sense of their data, and Ads Data Hub is a key step forward. 

Ads Data Hub gives you more impression-level Ads data than you've ever seen before, all while respecting individual users' privacy. Welcome to the future of the Privacy First ads measurement world!  

To learn more, check out its official site: https://developers.google.com/ads-data-hub