Spotify Streams Drive 1.5x More Consumption Time Than YouTube for Podcasts, New Podstock Data Shows

Podstock, the leading multi-platform analytics and management solution, today released new benchmarking data showing that a single Spotify stream generates, on average, 1.5x more consumption time than a YouTube view of the same podcast episode. The same analysis also shows that as episodes are exposed to broader, unfamiliar audiences, average time spent per view or stream decreases.

Podstock analyzed thousands of episodes which were released in video on both Spotify and YouTube for an apples to apples comparison. The results were strikingly consistent- in 95% of episodes, Spotify audiences spent more time with content than viewers on YouTube for the same episode. This demonstrates that on average a Spotify stream is a stronger signal of audience engagement than a YouTube view- and likely more valuable to advertisers.

While time spent is an important metric for determining how well a show reaches its core audience, it can be misleading when focusing on discovery and reaching new audiences. According to Podstock, as episodes are surfaced to new audiences, the average time spent per stream/view tends to decrease because new listeners are more likely to sample content before deciding whether to continue, which lowers average time spent as total reach increases.

This dynamic helps explain the 5% of episodes where YouTube outperformed Spotify on time spent. In many of these cases, episodes experienced significant discovery spikes on Spotify, driving stream counts well above a show’s typical baseline. That influx of new listeners lowered average time spent per stream as overall streams increased.

“Today, Spotify streams are a more valuable indicator of fandom and advertiser value than YouTube views,” said Michael Paretzky, CEO of Podstock. “But that’s only half the story. Time spent is incredibly valuable for understanding engagement with your core audience, but it often moves in the opposite direction when you’re investing in growth. The best podcast companies of the future will be able to seamlessly operate across multiple metrics in order to serve existing fans, expand to new audiences, and drive performance for advertisers.”

According to Podstock, the takeaway is not that one platform or metric is inherently more valuable, but that performance depends on context and goals. It’s important that creators and publishers investing in discoverability expect time spent to fluctuate and to interpret it alongside reach and distribution metrics.

“We’re increasingly focused on metrics like average view duration and time spent to understand the quality of engagement across platforms,” said Andy Hodgson, CFO at Goalhanger. “Looking at time spent relative to total deliveries or content length gives us a much clearer view of how audiences are actually consuming our shows. Being able to track and compare those metrics through Podstock is critical for how we evaluate performance and make decisions about growth.”

As podcasting continues to expand across platforms and formats, the need for consistent cross-platform measurement is becoming more important. In today’s fragmented media landscape, time spent is emerging as a more meaningful indicator of value than reach alone.

To learn more about Podstock’s analytics suite email hello@podstock.io.

Methodology

Podstock’s findings are based on an anonymized analysis of thousands of full-length video podcast episodes published on both Spotify and YouTube within the same release window. The analysis compares Spotify minutes per stream against YouTube minutes per view over the first 30 days after release, limited to episodes that had completed a full 30-day measurement window on both platforms. Each matched episode was treated as one observation, and results were reviewed using both median and mean values to reduce the risk that large shows, viral spikes, or other outliers drove the overall finding. Episode matching was powered by Podstock’s content association engine, which maps the same underlying podcast episode across platforms, and analyzed through Podstock’s cross-platform analytics suite.