A GitHub app that suggests code fixes for conversion failures
Hi Hacker News! I’m Rashid, a first-year student at UT Austin and the person leading development of Rejourney, a web/app analytics and session replay tool. Recently, a handful of our users emailed us that they only care about a few user recordings (replays) that show conversion issues, and didn't want to waste time watching all the session replays they got. And no matter how many filters/smart capture rules we put in, the deterministic methods of choosing what to capture always missed important session recordings or overshot and recorded everything. Our users also used other…
What it does
In the maker’s words, at launch
Hi Hacker News! I’m Rashid, a first-year student at UT Austin and the person leading development of Rejourney, a web/app analytics and session replay tool. Recently, a handful of our users emailed us that they only care about a few user recordings (replays) that show conversion issues, and didn't want to waste time watching all the session replays they got. And no matter how many filters/smart capture rules we put in, the deterministic methods of choosing what to capture always missed important session recordings or overshot and recorded everything. Our users also used other platforms like Clarity or Posthog's AI that summarized all the replay issues, but those also either missed important sessions or focused on a few sessions rather than the overall picture in terms of revenue, growth, and conversion leaks. We started to think about the unit of analysis and segmentation strategies for failure patterns across many sessions. That led us to build Marlin (big fish), a GitHub app that looks for recurring conversion failures, connects them to session, console, network, and repository context, and suggests a code change for a developer to review. The cool thing is that if you setup the analytics properly, it will understand which sessions have the "leaks" that impact business metrics and growth significantly. The link is a public demo using a simulated leak detection over a set of replays. It's still pretty early and what we are trying to understand is how well does this perform. Theoretically, it has all the context and feedback it could possibly get in the best possible translated forms for it to understand from the App/website code to the actual user sequence and recordings. Let us know what y'all think!
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