Rejourney is an open-source observability platform that identifies revenue leakage in web and mobile apps through session replays and AI analysis, while suggesting solutions.
Imagine this: A user in your shopping mall app reaches the checkout stage but suddenly leaves the screen. Why did they leave? Was it a server error? Or was the checkout button not visible? We have long agonized over countless graphs and dashboards, but it has been difficult to know exactly ‘which user’ stopped at ‘which moment.’
It’s like customers entering a store but disappearing near the checkout counter. Revenue leakage happens quietly like this. But what if AI could hide behind the counter, watch directly why customers are leaving, and write a report for us? The recently released open-source project ‘Rejourney’ has stepped up to play that exact role.
Why is this important?
Corporate revenue is not determined solely by selling many products. In fact, many companies suffer from ‘invisible revenue leakage.’ Revenue leakage usually occurs in blind spots, such as discrepancies with forecasts, situations where transactions clearly marked as ‘in progress’ have actually stalled, or post-management processes where no one takes responsibility Source: Is Revenue Leakage Hiding in Your Forecast?.
| To solve these problems as a developer or planner, one had to analyze thousands of user sessions one by one. Rejourney automates this process, helping teams that should be focused on growth concentrate on ‘actual recovery’ instead of staring at dashboards [Source: Revenue Recovery Analytics | Rejourney](https://rejourney.co/revenue-recovery-analytics). |
Easy to understand
| To understand Rejourney easily, think of it as ‘CCTV watched by AI.’ When we build an app, users use it. Rejourney provides a ‘Session Replay’ function (a technology that plays back what a user clicked and what screens they saw in the app) to record this process [Source: ReJourney:OpenSourceSentry Alternative | OpenAltFinder](https://openaltfinder.com/tools/rejourney). |
But it would be impossible for a human to watch all these videos, right? That’s where AI comes in.
- Observation: AI meticulously examines numerous user videos.
-
Analysis: It finds ‘holes in the funnel’ (the process a user goes through until purchase), such as the app suddenly closing during the checkout stage or users hesitating at a specific button [Source: AI Funnel Leak Detection Rejourney](https://rejourney.co/). -
Suggestion: Instead of just saying “there is a problem,” it grades how much this issue affects revenue and even creates a ‘solution package’ so that PMs (Product Managers) or developers can fix it immediately [Source: AI Funnel Leak Detection Rejourney](https://rejourney.co/).
Simply put, even if we don’t watch the CCTV every day, AI informs us: “Today, five customers at checkout counter #3 couldn’t find the payment button and left. Moving the button here should solve it!”
Current Status
| Currently, Rejourney is an open-source observability platform that can be used on both web and mobile apps Source: Rejourney - GitHub. Designed with a priority on being lightweight and performant, it minimizes the impact on app speed while detecting errors in real-time and providing journey mapping (visualizing the path users took in the app) Source: Rejourney - Self-hosted software [Source: ReJourney:OpenSourceSentry Alternative | OpenAltFinder](https://openaltfinder.com/tools/rejourney). |
| Self-hosting is possible, so companies that value security can consider adopting it based on their technical capabilities Source: GitHub - rejourneyco/rejourney. However, the service is just beginning to be known to the world, and developers are continuing to evolve the platform through sophisticated technical documentation such as mobile session replay or GPU replay structures [Source: Engineering Log - Technical articles | Rejourney](https://rejourney.co/engineering). |
What will happen next?
| The future of data analysis is shifting from ‘numbers’ to ‘behavior.’ Rather than agonizing over why the bar graph on a dashboard has changed, checking and correcting ‘evidence’—the actual user behavior—will be the key to growth [Source: Revenue Recovery Analytics | Rejourney](https://rejourney.co/revenue-recovery-analytics). |
As AI tools like Rejourney become widespread in the future, it is expected that developers and planners will be able to find user inconveniences much faster and more accurately, allowing them to spend more time creating ‘seamless app journeys’ where users stay.
MindTickleBytes’ AI Reporter Perspective
We live in an era where it is easy to get lost in a sea of complex data. Rejourney reminds us again that ‘there are people behind the data.’ It is very interesting that AI is evolving beyond simple summarization or translation into a practical partner that patches the ‘holes’ in our business.
References
-
[AI Funnel Leak Detection Rejourney](https://rejourney.co/) - GitHub - rejourneyco/rejourney: Rejourney is a open source, self-hostable/hosted observability tool for mobile apps. Focus on lightweight and performance. · GitHub
-
[Is Revenue Leakage Hiding in Your Forecast? Clari](https://www.clari.com/blog/what-is-revenue-leak-and-how-can-you-prevent-it/) -
[Revenue Recovery Analytics Rejourney](https://rejourney.co/revenue-recovery-analytics) - Rejourney - GitHub
- Rejourney - Self-hosted software
- rejourney/README.md at main · rejourneyco/rejourney · GitHub
-
[Engineering Log - Technical articles Rejourney](https://rejourney.co/engineering) -
[ReJourney:OpenSourceSentry Alternative OpenAltFinder](https://openaltfinder.com/tools/rejourney)
- Manual analysis of financial statements
- Combining session replay with AI analysis
- Conducting customer surveys
- Focus on heavy and complex features
- Lightweight and performance optimization
- Offline-only tool
- Transactions clearly recorded as revenue
- Well-managed marketing channels
- Blind spots or discrepancies between forecasts and actual situations