I have found this update especially useful when reviewing experiment outcomes that show a clear pattern in the numbers but do not fully explain the reason behind it.
For the exact release notes and rollout context behind this update, you can refer to PostHog’s official changelog.
Quick summary
PostHog can now summarize session replays from an experiment and compare behavior patterns across variants. This helps product, CRO, and analytics teams investigate why the numbers changed without watching every recording first. The summary is a starting point, not a replacement for the experiment statistics or a review of representative recordings.
Turning Experiment Results into Usable Insight
When I’m reviewing experiment results, I usually do two things: confirm the deltas/statistical outcome, then jump into session recordings to understand what people actually did. The problem is volume: I don’t want to watch a pile of replays just to find the pattern.
Now, inside experiment results, there’s an option to summarize session replays tied to the experiment. The way I’m using it is straightforward: I open the experiment, click a variant in the results visualization to open the detail panel, and from there I either view recordings or click “Summarize session replays.” PostHog AI then analyzes recordings across variants and surfaces behavior patterns, specifically calling out differences in how users interact with each variant.
What I like about this is that it’s not replacing the experiment statistics—it’s giving me the “why” layer right where I’m already making the decision. I still validate by opening a few representative recordings, but now I’m doing that with a hypothesis in mind instead of hunting blindly.
Frequently asked questions
Can I act on an early experiment result surfaced alongside replay analysis?
Do not make a decision from incomplete experiment data; even an early statistically significant result may change in the final results.
How should I set the experiment’s run length?
Calculate how long the A/B test should run before it starts and stick to that duration, because a test can hit “significance” early and later converge back to neutral.
Which inputs are needed to calculate test duration?
Use the current count or conversion rate for the metric, the minimum detectable effect, and the desired level of confidence.
Why use a phased rollout before exposing the remaining users?
Run a phased rollout with a small group of users for a few days; restarting after many users have seen the change can produce biased results.
Which metrics should accompany the primary experiment result?
Monitor product health metrics such as retention, session duration, and daily active users during and after the experiment.
Which Session Replay controls can help focus an experiment review?
Use URL matching to focus on specific pages and event triggers for moment-based recording; Session Replay settings give you granular control over what gets captured.
Can an event-triggered replay show what happened before the trigger?
No. Event-triggered recording does not show what happened before the event.
What should I check before enabling additional capture options?
If your app shows user emails, payment details, or other sensitive information in the UI, understand what each option captures before enabling it.
