AI funnel leaksLeak detectionProduct teams, Growth teams, Engineering

AI funnel leak detection that starts from the leaks page

Use the Rejourney leaks page to rank conversion drops, rage taps, crashes, API failures, and journey loops with the replay evidence needed to fix them.

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Rejourney leaks page showing ranked issue detection and funnel leak evidence — Turn the leaks page into a repair queueStart with the ranked leak, then inspect the sessions, journeys, failures, and context that explain why users dropped.

The leaks page turns raw sessions into a ranked repair queue

A funnel leak is not just a chart that went down. It is the repeated moment where users lose intent: a dead checkout button, a loop between screens, a slow API call, a rage tap cluster, a crash, or a path that looks healthy until replay shows confusion.

Rejourney's leaks page groups those signals into ranked issues, then keeps the replay, journey, technical context, and affected user evidence close enough for product and engineering to act together.

That means teams can move from 'conversion dropped' to 'this path, this screen, this request, these sessions, this likely fix' without stitching together separate dashboards.

Open the leaks page before opening random sessions

The leaks page is the triage surface for repeated friction. Instead of starting in a replay archive, the team starts with grouped issues that already point to a funnel drop, rage tap cluster, crash, API failure, journey loop, or blocked product path.

That changes the review from clip hunting to issue ranking. Product can ask which leak costs the most intent, growth can look for revenue impact, and engineering can open the exact sessions that prove the failure.

Repeated product and technical signals grouped together.
Replay evidence attached to the issue instead of stored elsewhere.
Affected paths, sessions, and context ready for the owner.

Rejourney issues feed with replay-backed leak signals — Issue feedReview repeated issues with enough context to decide what deserves repair.

Rank leaks by user impact and fixability

A useful leaks page should not treat every signal as equal. A one-off confusing session, a repeated checkout failure, and a release-specific crash all need different priority.

Rejourney keeps the issue list tied to user impact and supporting evidence, so the team can see whether a leak is repeated, whether it blocks conversion, and whether the available context is enough for someone to repair it.

Prioritize repeated leaks over isolated oddities.
Check the path and session sample before assigning work.
Keep technical signals close enough to prove the likely cause.

Rejourney issue detection dashboard with fix-ready context — Leak contextKeep session, product, and technical context beside the issue.

Inspect the replay evidence behind the issue

The issue summary is only the door. The proof is still the session: what the user saw, which path they took, where they hesitated, what request failed, and whether a crash or UI state shaped the outcome.

When the replay evidence stays attached, the team can verify that the issue description matches reality before it becomes roadmap work or an engineering ticket.

Rejourney journey ribbons showing funnel paths behind leaks — Journey evidenceUse paths to understand whether the leak repeats across a flow.

Use journey paths to prove the leak repeats

Some leaks are path-shaped. Users branch, loop, or drop after a transition rather than at a single obvious button. Journey ribbons make those patterns easier to spot and easier to explain.

From the leaks page, the journey evidence helps the team decide whether the issue is a repeated funnel problem or just one strange session. That context matters before assigning priority.

Rejourney session replay with timeline and user context — ReplayWatch the real session before deciding what the metric means.

Package the fix context for engineering or an AI workflow

Once a leak is real and repeated, the handoff should include the path, expected behavior, observed behavior, affected sessions, relevant events, failed requests, release or device context, and replay links.

That gives an engineer or coding agent enough evidence to begin reproducing the issue instead of asking someone to rewrite the same investigation in a ticket.

Expected and observed behavior.
Replay links and affected session examples.
Events, requests, crashes, devices, releases, and journey context.
A short fix hypothesis that can be tested.

Implementation notes

These are the checks another engineer should be able to use before trusting the feature in production.

Do not file a leak until the replay evidence supports the issue summary.
Keep grouped issues tied to route, screen, release, platform, and affected user context.
Use journey ribbons when the failure is a path or transition problem.
Package expected behavior, observed behavior, replay links, and technical context before handing the leak to engineering or an AI coding workflow.

When to use a lighter signal

Your team only needs high-level conversion rates and does not investigate the sessions behind them.
You already have a reliable issue queue that links funnel drops to replay, journey, API, and crash context.
Your product does not need product and engineering teams to share the same evidence when prioritizing fixes.

Questions teams usually ask

What does the Rejourney leaks page show?

It shows ranked product and technical issues such as funnel drop-offs, rage taps, crashes, API failures, and repeated journey loops, with replay evidence and context for each item.

How is AI used in funnel leak detection?

AI helps cluster repeated session signals into fixable issues and prepare context packages, but the workflow stays grounded in real replay, journey, event, crash, and request evidence.

Who should use the leaks page?

Product teams use it to prioritize conversion leaks. Engineering teams use it to reproduce the cause. Growth teams use it to connect leak repair with revenue and retention movement.