Custom Podcast Player with Ad Performance Tracking for Enterprise Clients

The Client

Listen Network is a paid podcast distribution platform that delivers guaranteed downloads from surgically targeted audiences. Their clients include agencies and enterprise brands like PRX, Content Allies, PwC, Meta, Zendesk, and Gusto. The company uses LinkedIn and Google Display targeting to reach specific demographics down to the job-function level—“HR managers at aerospace companies over 1,000 people”—filling the “paid media” gap in the PESO marketing model for podcasts.

Organic podcast growth is slow and unpredictable. Apple and Spotify are walled gardens with no meaningful attribution. Listen Network solves this by delivering measurable, guaranteed downloads with hyper-detailed reporting on listener demographics—all compliant with IAB Podcast Measurement Technical Guidelines v2.2. But to deliver on that promise, they needed a core piece of technology they didn’t have: a custom podcast player that could actually measure ad performance.

The Challenge

Podcast advertising has a fundamental attribution problem. When someone listens to a podcast on Apple Podcasts or Spotify, the advertiser gets almost nothing back—no engagement data, no listen-through rates, no way to connect an ad impression to an actual listen. Standard podcast players weren’t built for measurement. They were built for consumption.

Listen Network needed a player they controlled—one that could embed tracking to measure exactly how listeners interacted with podcast content and ads. Did they listen through the ad? Did they skip? How far into the episode did they get? This data is what enterprise clients pay for, and there was no off-the-shelf solution that could provide it while integrating with the various ad-tech APIs that Listen Network’s distribution model depended on.

The company also needed to move fast. The podcast advertising market was growing rapidly, and this player was the foundation of their entire service offering—without it, they couldn’t deliver the measurement data that differentiated them from competitors. Beyond the player itself, the engineering team was junior and needed mentorship to build and maintain ad-tech integrations at the quality level enterprise clients expect.

Our Solution

We joined Listen Network in January 2023 to build the custom podcast player with embedded ad performance tracking that would become the core of their product offering.

The player was purpose-built for measurement. We designed it to capture granular listener behavior—play, pause, skip, seek, listen-through rates, ad segment engagement—and feed that data into analytics pipelines that could generate the hyper-detailed reporting Listen Network’s clients demand. The player hooked into various ad-tech APIs to connect ad impressions with actual listening behavior, closing the attribution loop that standard podcast platforms can’t provide.

This wasn’t just a media player with some analytics bolted on. The tracking had to be reliable enough for enterprise clients to make budget decisions on, compliant with IAB Podcast Measurement Technical Guidelines v2.2, and performant enough to not degrade the listening experience. We shipped it as an MVP first, validated it with real campaign data, then iterated based on what clients and their ad-tech partners actually needed.

Beyond the player, we mentored the junior development team and established code review practices, architectural decision-making frameworks, and development workflows—building a team that could maintain and extend the platform independently.

The Impact

The custom podcast player became the technological foundation of Listen Network’s entire service offering. It’s what enables them to guarantee measurable downloads to enterprise clients like PRX, PwC, and Meta—the ad performance data that budget holders need to justify podcast advertising spend. Without it, Listen Network would be selling the same unmeasurable impressions as everyone else.

The junior developer mentorship created lasting value beyond the player itself. By establishing engineering practices and investing in team growth, the engagement built internal capability that continues to pay dividends. The team gained the skills to maintain the player, extend its tracking capabilities, and integrate with new ad-tech partners as Listen Network’s client base grows.

The right architectural decisions meant Listen Network could focus engineering resources on what actually differentiated their offering—measurement and attribution—rather than chasing features that wouldn’t move the needle.