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Amelia Coomber

Head of Growth

Channel-surfing: Finding the Right Avenues of Growth

The Rise of Pixel-Based Attribution in Podcast Advertising

After starting her career as a computer scientist, Amelia found her path as a data-driven growth marketer. She now helps brands measure podcasting ROAS with Podscribe.

In This Article:

As our lives become more and more digitized, podcasts have grown greatly. A study in 2022 showed that 38% of the US population listened to a podcast in the past month, over 3x the number from the decade before.

With such strong penetration, it’s no surprise that podcast advertising has become a must-have for many businesses’ marketing mix.

There are different podcast ad formats that fall under the umbrella of podcast advertising. These range from pre-recorded ads, to host-read sponsorships read by the podcast host, and so on. And then there are the formats: pre-roll, mid-roll, post-roll, or even native ads where hosts discuss the products being featured.

As with all other forms of marketing, measuring performance and understanding how revenue is being attributed across channels is critical for businesses to keep tabs on their marketing efforts. However, the podcast landscape is vastly different from other media. In this piece, we will go into detail on podcast attribution, with a particular focus on pixel-based attribution.

The challenges with Podcast Attribution

It is a common assumption that podcasts are digital or online—you’re probably listening to them on your phone or smart speaker, after all. Which means attribution should be no different than other digital marketing channels right?

It may surprise you to learn, then, that podcasts are actually an offline channel.

They are essentially radio shows that you download from the internet. Once it moves from the server to your device, tracking interactions and engagement post-download is limited.

Once offline, advertisers and platforms lose the ability to track listener activity (such as if they skipped segments, or your ads) post-download. Podcast attribution is thus different and more challenging than other digital channels.

Several methods have been introduced and used to try and track podcast attribution. These methods include coupon codes, vanity URLs, post-purchase surveys, and pixel-based attribution.

ADOPTER did an amazing job covering these in the previous piece on Podcast Attribution 101, but it’s our belief at Podscribe that pixel-based attribution and tracking is the closest we’ll come to tracking spend, impressions, and both top of funnel and bottom of funnel metrics connecting direct ad exposures to activity on the advertisers site or mobile app.

Moreover, pairing pixel-based attribution with the power of coupon codes and post-purchase surveys, is what we believe forms the ideal recipe for attribution.

The benefits of pixel-based attribution

Before we dive into podcast attribution, we must first understand how podcasts work.

Unlike other digital marketing channels and streaming, podcasts are accessed through a Really Simple Syndication (RSS) feed.

What exactly is an RSS feed?

It is a formatted text document that contains all the important information about the show. It's hosted on a server and (usually) has a public URL/link so anyone can view or access its contents.

It contains all the information about your show and episodes. This includes the show’s title, description, episode titles, links to the audio files for the episodes, and more. The RSS feed provides a way for anyone to subscribe to the show, or stream or download your episodes.

Without an RSS feed, podcasts would just be blog posts and audio files. There would be no way for people to subscribe and get new episodes unless they visit their website or get a direct download link.

RSS feeds are essential for the distribution of podcasts. However, this downloads the episode locally to the listener's device, complicating tracking and attribution.

Methodology for pixel-based attribution in podcasting

Pixels are the key to bridge the gap between who listened and who took action. Due to the offline nature of podcasts, one of the few data points available is the user’s Internet Protocol (IP) address as well as some others like the user-agent pairs (but those are less important).

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