What is Defined as A Podcast Download and What is RAD?

The term “Download” might be a little misleading as it is not just related to physically downloading the file to a device. In simplest terms, a download is when a listener actively downloads your podcast or episode to their device and / or when a listener actively streams your episode by pressing the play button within the app or website. The latter allows for more accurate listening data, since data is sent to the podcast’s host servers where data can be parsed in real-time. However, there are some limitations with just focusing on “Downloads.” For one, it is difficult, not impossible, but difficult to get data for those that download the episodes to their phone and computer. Did they listen to it? When did they listen to it? Did they skip the ads? etc.

Collectively, the industry is trying to figure out how to solve for some of these problems. Enter RAD.

Photo by William Iven on Unsplash

The Industry’s Attempt to Standardize Tracking Data

At the end of 2018, NPR released a new standard for podcast listening measurement. They call this Remote Audio Data or (RAD).

Remote Audio Data (RAD), a method for sharing listening metrics from podcast applications straight back to publishers, with extreme care and respect for user privacy.

NPR worked for over a year with 30 other podcast companies and open sourced it for others to leverage.

“RAD is not intended to replace download statistics as a point of measurement for the on-demand audio industry, but is designed to provide data on listening events to complement download statistics.”

Three groups must work in collaboration to successfully implement RAD:

  • Audio publishers add the specified tags to audio files.
  • Mobile client applications implement the specification to watch for and report events.
  • Publishers or analytics services provide a tracking URL to a server that is equipped to accept reported RAD events and make the data available for analytics.

Downloads or Listens?

In a perfect world, the go to “Download” metric would be replaced by “Listens”. If podcast apps, websites and publishers used the RAD method, we could answer many more questions and do it in a more accurate way.

For example, How many total monthly listens did you receive? How many of them were unique? Where did they come from? How many were done offline? How many were streamed? When did listeners leave my episode? What content should I focus on for the next episode?

How does RAD Work?

From the NPR RAD website:

Podcasters mark within their audio files certain points (quartile or some time markers, interview spots, sponsorship or advertising messages, etc.) with RAD tags (ID3 tags) and indicate an analytics URL. A mobile app is configured to read these RAD tags and when listeners hit those locations in the file, bundle and send anonymized information to that analytics URL. The publisher can then use that data, from all devices, to get holistic listening statistics.

Here are some of the listening events. (Event labels in quotes.)

“podcastDownload”

The eventTime for events with this label should always equal “00:00:00.000”. This event indicates that a podcast has been downloaded by the client, but does not confirm the episode has been played.

“podcastStart”

The eventTime for event with this label should always be set to “00:00:05.000”. This event confirms that the episode has started to play.

“adStart”

The eventTime matches the start time for the piece of sponsorship with the “adId” indicated in the event object.

Multiple adStart events may included in the “events” array to match the number of sponsorship pieces included in this episode.

“adEnd”

The eventTime for “adEnd” corresponds to the end time for the piece of sponsorship with the “adId” indicated in the event object.

Multiple adEnd events may included in the “events” array to match the number of sponsorship pieces included in this episode.

“podcast98”

The eventTime for events with this label should be equal to 98% of the duration of this episode.

Overall, we think a decentralized standardization of podcast metrics is better for the industry as a whole. Listeners will have a better user experience over time, podcasters will have better data to make better decisions and advertisers will be able to better quantify their podcast ROI.

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