What does RAD mean for me?

If you are an app developer

The RAD SDKs for Android and iOS are a great starting place to learn more about how a RAD implementation might work in your client app. Check out our GitHub project to learn more and find out how you can contribute.

Why should you implement this spec?

RAD will allow publishers to receive organized, enhanced listening metrics on editorial, sponsorship and/or advertising messages they care about. It reduces the need for each platform to have a detailed analytics dashboard and allows for information to be aggregated in a third-party location. RAD is not intended to track specific user behavior; instead, RAD uses a session ID combined with IP addresses. The SDK was created to be lightweight and configurable to your application. If there is something you’d like to see added to the project, you are welcome to contribute.

If you are a podcast creator

The tag writing tool will enable you to get started immediately with RAD, regardless of what system you use to publish your podcasts.

Why should you use RAD?

These metrics help you better understand your audience across a range of platforms. You will be able to produce more informed, engaging content and, over time, develop improved data for your sponsors and advertisers.

If you are a listener of podcasts

RAD will help your favorite shows to create better content in response to user feedback.

How does RAD impact your listening experience?

It doesn’t. Content delivery will not be inhibited by RAD tags, and this information will not be used to re-target or track listeners. This information will be anonymized, aggregated statistical data.

If you are a brand/sponsor/advertiser

RAD brings a new level of transparency and depth to podcast measurement.

What does RAD provide you access to?

With this technology, publishers - and by extension their advertisers - can have access to a wider range of metrics about on-demand listening across platforms: Downloads, starts/stops, completed ad/credit listens, partial ad/credit listens, ad/credit skips and content quartiles. This data will not give you detailed user profiles, but it will begin to show what is actually being listed to across podcast episodes.