Podsights is a marketing attribution platform for podcast advertising.
What that means is we strive to help marketers understand the effects of buying advertising on podcasts. We answer questions like:
Unlike digital marketing, marketers have extremely limited data on the performance of their campaigns. They use tools like discount codes, custom URLs or surveys. All rely on the user telling the brand that they listened to the podcast. Because of this, fewer than 50% of all brands return for a second podcast campaign.
Podcast advertising works, but without proper measurement, it’s impossible to know its impact. This guide outlines how Podsights collects data and how we use it to make sense of podcast advertising.
Most digital tracking is based on cookies, small identifiers saved on the user’s web browser that are passed to a server on every request. Every time a user logs on to a site a cookie is placed to identify that device. On mobile apps, developers use mobile advertising identifiers (MAID) to replicate cookies. It’s a unique identifier to your device that never changes unless the user resets it or reinstalls the OS.
There are no unique identifiers in podcasting which is why attribution is hard. Podcast players don’t pass any unique identifier, so instead Podsights relies on many signals to statistically attribute downloads to visits.
First, we need to get the data.
When a user downloads a podcast episode on the Apple Podcast app they aren’t downloading the episode from Apple; instead, they are downloading it from the podcast’s hosting provider. This is how your podcasting host can measure downloads.
Over the years hosting providers have built Analytics Prefixes into their platforms. This allows a third party service like Podsights to hook in to those downloads, receiving the same data as the hosting provider.
An Analytics Prefix is a redirect that sits between the podcast app and the podcast’s hosting provider. Every time a user downloads an episode through their app, it is first received by Podsights’ Analytics Prefix where we capture the request information like IP, User Agent, and other data pertinent to the download.
We store this data securely for processing.
Podsights still uses user input data when available. If the Podcast tells the user to go to example.com/podcastname, it’s still a useful signal. Discount codes are another excellent signal for after the user converts.
Both, however, are leaky. The following is a screenshot of Google’s auto-suggest for ZipRecruiter:
You can see Serial’s custom URL as an option.
This is a screenshot off of Honey.com. You can see the Off Book podcast’s custom discount code listed here:
We don’t take user input data as gospel because of the above, but it still has its place in the data pipeline.
The last signal that Podsights uses is from third-party digital identity graphs. The best way to explain this is through user behavior. You as a user have a pattern that you go through on your day to day routine. You wake up and check your phone for sports scores, news, and social updates. You then get in a car and drive to work. At work, you check those same sites from your phone and laptop. Identity graphs use all these touch points as signals to tie all these actions back to a home IP address.
Imagine you download a podcast at home, listen to it on the way to work and then visit the brand’s site at work. Podsights, through these identity graphs, can properly attribute your actions back to the home IP and to the podcast.
Podsights takes all these signals and creates a model for each podcast and brand to attribute the value of each campaign to the brand.
As you can see from the above, without participation from the podcast players, it’s impossible to track a user definitively from download to purchase. Podsights approach is the most statistically accurate model in the market for podcast attribution.