Reporting on Responsive Display Ad Assets?
Hello all, is anyone familiar with reporting on Responsive Display ads? I am trying to put together a report on which are driving the most activity, but it seems like they just give you performance based on a “bad/good/better/best” scale, search assets are able to provide specific metrics for each asset. I checked the sub’s history and there was a question on this four years ago that nobody seemed to have the answer to. Hopefully someone has found it in that time? Thanks!
The short answer is:
Unfortunately, native Google Ads reporting for Responsive Display Ads (RDAs) still doesn’t offer specific per-asset metrics like those available for search assets; the “bad/good/better/best” performance ratings remain the standard in the UI.
The solution is to use a combination of the Google Ads API, the Google Analytics Data API, or even server-side tracking via tools like Stape or Google Cloud Platform with Google Tag Manager to create a custom, comprehensive reporting solution in a tool like Looker Studio.
This approach allows you to join Google Ads data with more granular event data (like page_viewadd_to_cartpurchase) to indirectly but effectively determine which asset combinations are driving the most desired activity, offering a cost-effective and deep-dive alternative to the native UI limitations.
The long answer is:
That question from four years ago was spot on, and unfortunately, Google hasn’t fundamentally changed the way it reports on Responsive Display Ad assets in the main UI or even in standard reporting tables.
Because RDAs dynamically combine multiple headlines, descriptions, and images, Google reports on the combination’s performance and assigns the “bad/good/better/best” rating to individual assets based on their contribution to those combinations, but doesn’t expose granular impressions, clicks, or conversion metrics for the assets themselves.
However, you absolutely can build a system to track this activity effectively and cheaply.
The key is moving beyond the Google Ads UI and leveraging APIs for a custom solution.
The Google Ads API is the starting point because it allows you to pull highly detailed data about your ads, campaigns, and conversions – crucially, you can often pull more granular data than what’s immediately visible in the UI.
You’d use this to get ad-level or ad group-level performance.
Next, you integrate event tracking via Google Tag Manager (GTM).
When a user engages with an RDA, GTM should be set up to fire a custom event containing information about the ad that was clicked, potentially including custom parameters for the specific assets or combination of assets, especially if you have control over the landing page parameters or can use an intermediary tracking solution.
If you use a server-side solution like Stape or Google Cloud Platform, you can collect and process these Standard Events like page_viewadd_to_cartpurchase with much higher fidelity and less risk of browser blocking, ensuring your data is clean.
You then use the Google Analytics Data API to extract this rich, event-level data, which is tied to user interactions.
Finally, you pull all this together into a visualization tool like Looker Studio, which also has an API for advanced integration.
Looker Studio allows you to blend the Google Ads performance data (from the Google Ads API) with the granular event data (from the Google Analytics Data API/server-side tracking).
By blending these two datasets based on a common key – like date, campaign, or even a custom ad identifier – you can build reports that show, for example, “Ad Group X led to 50 purchase events, and based on the custom tracking parameter fired upon the landing page view, the most common image-headline combination driving those purchases was Y.”
This is an excellent and cheap solution because the APIs themselves are mostly free for common usage tiers, and the server-side solutions like Stape or Google Cloud Platform are generally cost-effective, offering a much higher return on investment than expensive third-party reporting tools while giving you maximum control over data granularity.
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