Analysis, frameworks, and field notes for streaming programmers.
Retention data across hundreds of episodes shows a consistent cliff at 90 seconds. What drives it, what programming choices flatten it, and how to measure your own threshold.
Read articleNetworks price ad inventory on CPM without knowing how many listeners each break drives away. A framework for computing break-adjusted yield and finding the break count that maximizes net revenue.
Retention curves differ dramatically between host-led and format-led shows. Analyzing 60 streaming shows reveals which audience segments follow hosts and which follow topics.
Most analytics tools are built for data teams, not programmers. The metrics that change scheduling decisions, the view hierarchy that fits a weekly editorial meeting, and what to cut.
Traditional ratings tell you how many; retention tells you how long and why. A practical guide for making the shift to segment-level retention.
Listener behavior shifts by quarter, week, and even commute window. Retention data surfaces the seasonal rhythms programmers can exploit.
Unsubscribes lag reality by weeks. Retention curves, skip rates, and segment completion patterns signal audience disengagement earlier and more precisely.
A step-by-step process for isolating content quality issues from distribution and promotion variables in your retention data.
Retention curves of consistently high-performing episodes share structural patterns: cold open length, segment pacing, outro timing. Observable, measurable, replicable.
When real-time data changes outcomes and when it just adds noise. A practical look at data latency for programming teams.
Retention data gives sales teams a new inventory narrative and a way to justify premium placement pricing beyond raw impressions.
Early observations from Shoutcast data across a range of streaming formats. Common drop-off triggers, show structures with consistently better retention, and what surprised us.
