Flidget : Find out why users cancel in real time
Hey IH,
I want to share something I have been building for the past few weeks.
It started with a simple frustration. Every time a user cancelled, I had no idea why. I would send a follow up email, get a vague reply or nothing at all, and just move on. The churn column stayed empty and I kept guessing.
The real problem is timing. By the time you reach out, the user has already moved on mentally. The honest reason they had at the moment they clicked cancel is gone forever.
So I built Flidget.
When a user clicks your cancel button, a small chat appears right there on the page and asks why. They reply in text or voice in a few seconds without being taken anywhere else. The real reason lands in your dashboard instantly.
It also runs drift detection in the background and labels every user as Healthy, Risky, or Drifting based on actual usage. So you can see who is quietly heading toward cancel before they even get there.
One script tag. Free to start. Live in under two minutes.
Would love honest feedback from this community, especially from anyone who has tried exit surveys before and found them useless.
Do you feel indiehackers is better than reddit on advertising?
Not really advertising, just sharing what I built and asking for honest feedback. IH feels like the right place for that. People here are building too, so the conversations are actually useful. Reddit is great for reach but this isn't about reach right now, it's about finding people who get the problem.
The timing point is everything here. There's a huge difference between catching someone mid-cancel and asking them three days later — by then the emotional context is gone and you just get vague answers like "it wasn't the right fit."
The drift detection layer is what really interests me though. Labeling users as Healthy, Risky, or Drifting based on actual usage patterns means you can intervene before someone even reaches the cancel button — which is a completely different (and more valuable) problem to solve.
Curious: when you detect a "Drifting" user, does Flidget alert the founder automatically, or does it just show up in the dashboard for them to monitor? And are you planning any kind of suggested intervention — like recommended re-engagement messaging based on the drift pattern?
Both actually. Drifting users show up in the dashboard with a priority queue so you always know who needs attention first, and you can set up automatic alerts so you get notified the moment someone crosses into Drifting territory without having to check manually.
On the intervention side yes, that is already in there. Once someone is flagged as Drifting you can trigger a rescue email directly from the same view, either manually or fully automated based on the drift action that fired. The email suggestions are shaped by the actual signals so if someone stopped using a specific feature the outreach reflects that rather than sending a generic check-in.
The goal was always to close the loop fully. See the risk, understand why, act on it, all from one place without jumping between tools.
This hits on something most SaaS founders get backward. They optimize the cancellation flow instead of the cancellation moment.
The way you've written this post is also doing exactly what Flidget does. It captures the honest reason before the reader moves on to the problem statement, the frustration, and the build. Everything is structured to land before skepticism sets in. That's not accidental, and it's definitely not easy to pull off.
The "one script tag, free to start, live in two minutes" line at the end is doing a lot of heavy lifting, too. It collapses the objection before it forms. That's tight copywriting, I must say.
Would love to see more behind how you're thinking about the content side of this launch, especially the drift detection angle, which feels under-explained and could be its own post entirely.
This is genuinely one of the better pieces of feedback we have gotten on the post itself, thank you for breaking it down like that.
You are right that it was intentional. The structure mirrors the product logic, lead with the pain before the skepticism kicks in, same way the widget catches the reason before the user mentally moves on. Good to know it landed.
On drift detection being under-explained, completely agree. It is honestly the more interesting layer and we compressed it into two lines because we did not want the post to feel like a feature dump. But you are right that it deserves its own post. The idea that you can see someone quietly heading toward cancel weeks before they get there, based on actual usage patterns not just login frequency, is a different conversation entirely.
Going to write that one next.
That parallel between the post structure and the product logic is worth making explicit in your content strategy. Most SaaS founders write about their product. The best ones write like their product. There's a real difference, and it compounds over time.
On the drift detection post, the angle I'd push on is the emotional reframe. Right now, churn prediction sounds like a defensive metric. But catching someone three weeks before they cancel and actually pulling them back is definitely a retention story, not a risk story. This "retention story" framing entirely changes the distribution network.
I'm relatively new to the SaaS space, but I have been studying how the best founders communicate their products. Flidget's structure is genuinely one of the cleaner examples I've come across. Happy to think through the content angle with you if that's ever useful :)
The reframe from risk to retention is exactly the right angle and honestly cleaner than how we have been thinking about it internally. Writing about it defensively limits who it resonates with. Going to carry that into the drift post.
And yes, happy to think through the content side together - feel free to reach out directly.
I am really glad that the reframe landed. It's the kind of shift that seems small on paper but changes the entire energy of a post. Also, looking forward to seeing how the drift piece comes together.
Will reach out shortly.
Appreciate it. Looking forward to the conversation.
Really looking forward to it. What's the best way to reach you: LinkedIn or email?
hello@flidget.com works, feel free to reach out there.
Done :)
Churn analysis is one of the most underrated levers in SaaS. Most founders only look at it when it's too late. The real-time angle is smart — knowing why someone cancels at the moment it happens vs weeks later changes everything. What's the most surprising cancellation reason you've seen so far?
Honestly the most surprising one was a Safari bug.
The product worked fine on Chrome, fine on every internal test. But a segment of users on Safari were hitting a silent rendering issue that broke a key part of the flow. They never reported it. They just quietly stopped using it and eventually cancelled.
Nobody said "Safari bug" in a survey. They said "product felt buggy" or just nothing at all. The in-page chat caught someone saying exactly what broke and when. We found three more users who had hit the same thing within the same week.
One bug fix, three potential saves. That one still sticks with me.
I'm doing something similar for my gym except, I track drop-offs in their attendance. So if a user is attending steady 3x a week then drops to 2-1 per week, they get a call with a very personal "how's life man?" The responses vary from "I'm having a bad time at work" to I just broke up with my girl. And guess what's the answer every time...more days at the gym. We also combine it with an appropriate offer like a certain percentage off PT, or even you know what, come in, we'll get you a nice branded shirt, just to remind you, you're part of the family bud. People break down in tears sometimes. It's not just a retention tactic. It's a human tactic.
This is honestly one of the best examples of drift detection I've heard and you're doing it without any software.
The attendance drop is exactly the signal, same logic as what Flidget tracks but for a gym. And the "how's life man" call is the intervention at exactly the right moment, before they've mentally checked out.
What you're describing is retention done right. Not a discount email, not a survey, just a human noticing and reaching out. The fact that people break down in tears tells you everything about how rare that feels.
We're essentially trying to bring that same instinct to SaaS. Catch the signal early, respond like a human, not like a tool. You've clearly figured out the hard part already.
I'm pre-PMF (still hunting first paying customer), so no churn data yet. But I've been thinking about the "exit survey" problem in advance because I want my future churn flow to actually surface honest reasons.
Two concerns about in-page-chat-on-cancel :
Survival bias. Most B2B churn isn't a click on cancel... it's silent disengagement, then a billing email 6 months later. Your in-page chat captures the LOUD churners. How does your drift detection quantify the silent ones early enough to actually intervene ?
The voice input is unique, I haven't seen it elsewhere. What's your adoption rate voice vs text in the wild ? Does the friction of "speak into your laptop in a quiet office" kill it, or does it actually lower the barrier vs typing ?
Looks promising. Free tier really helps for early-stage testing.
Really sharp observations, worth addressing properly.
On survival bias, you're right that the cancel button only catches the loud churners. Drift detection is a separate layer that tracks real usage events, login gaps, key actions not taken and flags users Risky or Drifting weeks before they ever see a cancel screen. In-page chat is the last line, drift catches the silent ones way before that.
On voice, adoption has been better than expected. Roughly 40% of responses come in via voice. The office friction concern is real but cancel is usually a solitary decision, people aren't cancelling in open standups. Text fallback is always there so completion rates stay healthy.
Good luck with the PMF hunt, thinking about churn instrumentation this early is already a good sign.
Thats a great approach to solve one of the biggest pain point of subscription business model?
Just curious, does it also recommend changes according to the patterns detected or just collect them?
We’ve been feeling this pain from another angle with Right Suite — founders guessing on messaging, pricing, and positioning the same way they guess on churn.
Love how Flidget moves the “why did you leave?” conversation to the exact second users hit cancel. Feels like the missing qualitative layer next to the usual dashboards and cohort charts.
Super curious what patterns you’re already seeing across products; we’re seeing something similar when founders finally test messaging with real users instead of relying on intuition.
Thanks for this, really appreciate it.
you are right about the missing qualitative layer. what we keep seeing is that the real reason is almost never what founders expect. pricing comes up way less than people think. it is usually something small that never got fixed because nobody knew it was broken.
and yes the intuition problem is everywhere. founders build features based on what they assume users want and churn tells them something is wrong but not what.
would love to see what Right Suite is surfacing on the messaging side. feels like the two problems are connected, bad messaging brings in the wrong users and then churn looks high even when the product is solid.
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This is probably the sharpest framing of the problem we have heard.
You are right that we treat them as two layers right now and that is a limitation worth being honest about. The cancel moment captures the stated reason. Drift captures the behavioral signal. But neither of them shows you the actual decision forming, which is the thing that would let you intervene meaningfully rather than just respond.
The timeline idea is where we want to go. Not just "this user is drifting" or "this user said pricing" but here is the sequence of moments where trust started eroding, engagement dropped, and the decision quietly closed before they ever clicked cancel.
That is a harder problem than most churn tools are trying to solve. Most are optimizing for better data collection at known moments. What you are describing is making the invisible decision process legible before it becomes a data point at all.
We are earlier on that than we would like to be. But this is exactly the direction. Appreciate you pushing on it this clearly.
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Exactly the direction we are heading. The timeline is specifically about making that sequence legible before it becomes a data point. Right now we catch the signal and the stated reason — the missing piece is the story connecting them, which is where the real intervention lives.
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