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Unleashing the Power of First-Party Data: An ADH Integration Adventure

As we move towards a cookie-less world, first-party data becomes an absolute necessity to capture user behavior.  

What is First Party data?

First Party data is the data that an organization collects from its users. Due to this reason, the organization has more control over how the data was obtained and is more accurate than Third-party data, which is the aggregated data from other sources.

First Party data can be obtained from both online and offline sources, such as customer interactions on a website or app, as well as sales calls, store visits, and trade shows.

Offline data can be just as valuable as online data but requires onboarding to be useful in marketing campaigns, which involves bringing offline information into the digital realm. This process takes effort but is highly advantageous in the long run.

With the advent of Customer Data Platforms (CDPs), it is possible to merge various types of data from different digital channels.

Why First Party data?

First-party data offers personalized insights on your audience which can help target the right audience for the brand. 

It is easier to obtain consent as the customer is asked to share this data and is well aware of how the data would be used – Transparency.

The insights you gain are more relevant to the brand and give thorough information about the consumers’ interests, the problems they are facing, and their needs and preferences.

ADH & First-Party Data

Uploading first-party data into Google's Ads Data Hub (ADH) BigQuery Project and combining it with ADH data can lead to the discovery of valuable insights, enhance advertising efficiency, facilitate the achievement of data-driven business objectives and result in better campaign optimization. Through this process, advertisers can leverage the capabilities of Google's ADH without compromising the privacy of their own data.

Ads Data Hub offers the capability to link various types of first-party data, including mobile app data, offline transaction data, and data from a connected ID space.

With Ads Data Hub, it is possible to run queries that combine the first-party data uploaded to BigQuery with ADH data. This can provide a more comprehensive understanding of user engagement with your ads campaign, leading to more accurate attribution and better insights.

To link your data with Google's data in Ads Data Hub, it is necessary to have a join key that is tracked by the platform. This key serves as a common attribute between the two datasets and enables their integration. Joining can be done using various keys such as Resettable Device IDs (RDIDs), Custom Floodlight variables, Cookies, and LiveRamp RampIDs (currently in beta). It is important to note that RDIDs are not available for vendors.

The choice of join key in Ads Data Hub should be based on how your data is structured (e.g., app-based versus offline), rather than the specific use case, although some keys may be better suited to certain scenarios. For instance, both RDIDs and cookies can be used for customer segmentation, but advertisers who primarily receive traffic from apps may benefit more from joining with RDIDs than those whose sales come from other sources.

In general, regardless of the matching option chosen, the benefits of combining your data with ADH's data are consistent: it enables you to obtain more valuable insights about your advertising and create more effective audiences. Nonetheless, each matching option has unique data access and limitations and requires varying degrees of effort for setup and upkeep.


Unlock first-party perception with Emerald

Emerald is our solution which enables optimized data-driven decision-making by joining first-party data with ADH’s capabilities.

Some business problems that Emerald can help you solve:

Which type of audience or creative assets bring in audiences with higher lifetime value?

Post-conversion Value Analysis: Check which creatives are targeting high LTV audiences

Is there a different product price preference for audience segments? For example, the younger age group prefers low-priced products, while elders prefer high-priced products.

Persona-based Price Preferences Analysis: Check how the sale changes with respect to demographics.

Many more use cases can be developed based on the problem statement.

Business value:

* Creative optimization & personalization                                                     

* Accurate audience targeting

* Increase LTV


The transition from third-party to first-party analytics might be challenging and take some time but this effort will allow the organization to bring in more advantages.

Leveraging ADH and first-party allows marketers a better understanding of their audience and creates more engaging experiences.

Take personalization to another level by providing a “360” view using ADH and First-party data.