I built Flidget after watching MRR shrink while churn rate looked perfectly fine.
Three months ago I was staring at a 3% churn rate thinking everything was fine. MRR kept going down anyway.
Took me a while to figure out what was actually happening. High value customers were quietly leaving. Nobody was upgrading. And when someone did cancel I had zero idea why because my cancel button was just a confirmation dialog. No conversation. Nothing.
Turns out churn rate is kind of a lie. It treats a $29 customer and a $299 customer exactly the same. It tells you nothing about the 3 weeks of silence before someone decides to leave.
So I built Flidget. It does two things. Drift detection scores every user as Healthy, Risky, or Drifting based on actual usage so you can reach out before they ever hit cancel. And when someone does click cancel, a short friendly chat opens right there on their page and captures the real reason in seconds, text or voice, without any redirects.
Both signals live in one dashboard. No spreadsheets, no chasing survey replies three days later.
Setup is one script tag. Works with any stack. Free to start.
Would love to hear from anyone who has dealt with the same MRR vs churn rate confusion. What was your moment of realizing you were measuring the wrong thing?
flidget.com
I actually know a few early-stage SaaS founders personally, they've definitely dealt with this exact issue. I'd be happy to ask them if they'd answer some of your questions for free.
That would mean a lot honestly. At this stage real conversations with founders who have lived through it are worth more than any survey tool.
If any of them are open to a quick chat even 15 minutes I would genuinely appreciate it. No pitch just trying to understand the problem better.
I'm part of a community called "replyz" where people genuinely help each other out, and I think you'll find the right people there. You can post your question, specify who you'd like answers from, and get detailed responses from members with relevant experience. The only ask is that everyone contributes back by helping others too. Let me know if you want to know more!
The point about churn rate hiding revenue quality is interesting. Losing one high-value customer quietly can matter more than losing ten smaller inactive users.
The “3 weeks of silence before cancel” part also feels very real — most teams only start listening after the decision has already been made.
This hits close to home. The churn rate number feels safe until you realize your $299 customers are the ones quietly walking out and your dashboard is still showing green.
That 3 weeks of silence before cancel is the real killer. By the time someone clicks cancel the decision was made weeks ago. You are just processing the paperwork at that point.
I run DocMetrics (docmetrics.io) and we dealt with the exact same thing. What changed it for us was Flidget's drift detection. Knowing who is slipping before they decide to leave is a completely different game than reacting after the fact.
If you want to see it on your own data happy to set you up with 30 days on the Pro plan no strings attached. Just drop a mail at [email protected] and we will get you sorted.
You are completely right that by the time someone cancels the decision was already made weeks earlier. The cancel button is just the confirmation of something that happened in their head long before they clicked it.
That drift detection angle is interesting. The silent slide from engaged to disengaged before any visible action is exactly the same problem I am solving on the proposal side. Someone goes quiet after reading a proposal and most salespeople only notice when the deal officially dies not when the interest first started fading.
Different stage of the funnel but the same underlying problem. Behavior changes before decisions get announced.
I will check out Flidget properly. Always curious how other founders are solving the invisible disengagement problem.
Exactly this. The proposal going quiet is the same signal as a user stopping their logins. The decision is forming, you just cannot see it yet because nobody has said anything out loud.
The underlying problem is the same across the whole funnel. Behaviour shifts before anyone announces a decision. Most tools only catch the announcement.
Would love to hear what you find after trying Flidget. And honestly, if there is a way the two could work together at different stages, that would make for an interesting conversation.
You just articulated something I have been trying to explain for months
in one sentence. Behaviour shifts before anyone announces a decision.
Most tools only catch the announcement.
That is the entire problem. Whether it is a proposal going quiet or a
user stopping their logins the signal exists in the behaviour long before
it surfaces as a visible action. By the time the action happens the window
to intervene has already closed.
The funnel framing is interesting because you are right that it is the
same underlying problem at different stages. I am catching the signal at
the pre-sale stage when a prospect is evaluating. You are catching it at
the post-sale stage when a customer is disengaging. Both are the same
invisible drift happening before anyone says anything out loud.
I will try Flidget properly on real data and come back to you with honest
feedback. And yes the conversation about how the two could work together
at different stages of the funnel is worth having. Pre-sale intelligence
feeding into post-sale retention intelligence is a complete picture of
customer behaviour that neither of us has alone right now.
Let us keep this conversation going.
That framing of pre-sale and post-sale being two sides of the same problem is something I had not thought about that clearly before. You just connected a dot I was missing.
The full picture really is the complete customer journey. The signal exists at every stage. Most tools only look at one slice of it.
Please try Flidget on real data and be candid with the feedback. That is genuinely more useful to me right now than a positive review.
And yes, let us have that conversation properly. Drop me a line at [email protected] whenever you are ready.
Just sent you an email with my honest impressions. Looking forward to the conversation.
Got it, just read through everything. Really appreciate you taking the time to go this deep — the OTP timing point especially landed. That is the kind of thing that is easy to miss when you are inside the product every day.
Let us talk properly. Replying to your email now.
Launched on Product Hunt today — https://www.producthunt.com/products/docmetrics?launch=docmetrics — would appreciate your support if you have a moment.
This is a really important distinction—aggregate churn rate hides revenue concentration risk completely. The moment your best customers have a different churn pattern than your median customers, the single metric becomes misleading.
Segmenting churn by cohort and contract size is the fix. What signals were you able to find that predicted the silent high-value churn before it happened? That early warning layer seems like the real moat for Flidget.
Exactly that. The signal we found most reliable was feature adoption gap — high value customers who never used one or two core features were 3x more likely to churn than customers who did. Not login frequency, not session length. Specifically whether they reached the actions that correlate with long term retention. Drift detection in Flidget scores users on this so the Drifting queue is basically a list of accounts worth a personal email this week before they ever reach the cancel page.
The realization that standard churn rates ignore the massive difference between a $29 and a $299 user is a vital lesson in revenue health. By identifying drifting users before they cancel, you are moving from reactive firefighting to proactive account management. Capturing the real reason for leaving through a quick chat is far more effective than staring at a generic confirmation dialog.
Which specific behavior has been the most surprising indicator that a high-value user is about to drift away?
Honestly the most surprising one was invite behavior. High value users who never invited a teammate churned at almost 4x the rate of users who did. Even one invite changed everything. It was not about how much they used the product themselves, it was whether they had made it part of their team's workflow. Solo users who loved the product still churned. Users who brought in even one colleague almost never did. That single action turned out to be the strongest retention signal we track in Drift now.
Team invites turn a personal trial into a shared necessity, creating a social moat that solo users just can't match. Bunzee.ai applies this logic by using real market data to validate "must-have" features before the first line of code is written. Since you have a sharp eye for retention, would you mind giving Bunzee a quick look and sharing your feedback?
That's such a sharp observation. Solo users who love the product still churn but the moment someone brings in a teammate it becomes sticky. We track exactly this in Flidget's drift scoring and invite behavior consistently comes up as one of the strongest early signals.
Bunzee looks interesting, will check it out properly.
And since you're clearly thinking deeply about this stuff, if you ever want to see which users in your own product are hitting or missing those invite moments, Flidget shows that out of the box. Free to start and happy to help you get set up personally if useful. [email protected] or just flidget.com
I’m thrilled to hear you’re checking out Bunzee! I’ll definitely be looking into Flidget having that level of visibility into invitation behavior is exactly the kind of "telemetry" needed to make sure a product is actually sticking.
Telemetry is exactly the right word for it.Telemetry is exactly the right word for it. The invite signal only became obvious once we could see the full behavioral timeline, not just what users did but when they stopped doing it.
Would love to hear what you find once you're in. And if anything looks off or unclear just reach out directly, happy to walk through it.
The "silent exit" is often a much louder signal than the successful click, yet it's the one most founders completely ignore. Seeing exactly where a user hesitates or stops provides the kind of brutal honesty that a standard analytics dashboard usually hides. It turns the invitation process from a "black box" into a solvable engineering problem by highlighting the specific moment friction wins over intent.
I’m excited to dive in and see that behavioral timeline in action it’s the ultimate diagnostic tool for fixing a leaky growth funnel.
In your experience, is the drop-off usually a technical UX hurdle or a sudden "social friction" moment where they rethink the invite?
Mostly social friction in our experience. The UX is usually fine, the button is right there. What stops people is the mental calculation of whether their teammate will actually use it or whether it will create more work than value. The users who invite without hesitation already have a win to share. They are not inviting a product they are inviting a result. So the fix is less about the invite flow and more about making sure the user has something worth sharing before you ever ask them to share it.
"They are not inviting a product, they are inviting a result" that is a total lightbulb moment.
It makes so much sense. No one wants to be the person who adds "just another tool" to their team's plate. But everyone wants to be the person who shares a win. We spend so much time obsessing over the UX of the button, when the real "click" happens in the user's head only after they realize they’ll look like a hero for sharing it.
We need to focus on giving them the "trophy" first before we ask them to invite the audience.
In Flidget, what is that specific "trophy" or data point that usually signals a user is finally ready to brag to their teammates?
For Flidget the trophy moment is usually the first time a founder sees a specific fixable reason in their dashboard instead of a vague "too expensive." That shift from guessing to knowing is the thing worth sharing. It is not a metric, it is a feeling of finally having clarity. That is when they pull in a co-founder or a PM because they want someone else to see what they are seeing.
That’s the right problem.
You’re not measuring churn.
You’re measuring revenue decay before churn becomes visible.
That distinction is the product.
“Flidget” is light, but the problem is not.
You’re sitting closer to revenue retention infrastructure than a lightweight widget layer.
As soon as this moves upmarket, the product likely outgrows the current name.
Vroth.com would carry this much better.
Shorter, harder, and better aligned with retention / revenue-risk infrastructure than something that still sounds like a UI component.
Appreciate the thought on positioning. The name was a deliberate choice actually - retention tools that sound like "infrastructure" often feel intimidating to the exact founders who need them most. Flidget is meant to feel approachable. The problem is serious, the setup should not feel like it.
That said the upmarket point is worth thinking about over time. For now the focus is on making it dead simple for early stage SaaS founders.
That makes sense for early-stage founders.
Approachable lowers setup resistance.
The thing I’d watch is whether “approachable” starts getting read as “lightweight” once the product is tied to revenue retention.
That’s the tradeoff.
If the buyer is thinking:
“help me ask better churn questions”
Flidget works.
If the buyer starts thinking:
“help me protect revenue before churn shows up”
the name may start underpricing the product.
So I’d keep it for now, but watch when the sales conversation shifts from ease of setup to revenue risk.
This is a really strong insight — especially the “churn rate hides who is leaving” part.
One thing I’d be curious about from a user perspective:
how natural does the “cancel → chat opens” moment feel?
That interaction can be powerful, but also a bit sensitive.
If it feels like:
“I just want to cancel and now I’m being intercepted”
some users might react negatively or rush through it.
But if it feels like:
“okay, this is a quick, low-friction way to say why I’m leaving”
then you get much more honest input.
Feels like the tone and framing of that moment could have a big impact on how useful the data actually is.
Curious what you’re seeing so far.
This is exactly the thing we obsessed over in early testing. The framing makes all the difference. If the first message sounds like "wait don't go!" users feel trapped and rush through it. If it sounds like "hey mind telling us what changed?" it feels like a genuine conversation.
What we found is that tone and timing matter more than anything else. The chat opens after the cancel click, not before, so the user has already made the decision. At that point most people actually want to say something. They just never had anyone ask.
The completion rate surprised us. Users who feel heard give honest answers even when they are leaving.
That’s really interesting — especially the part about users actually wanting to say something once the decision is made.
One thing I’d be curious about then:
how structured are the responses you’re getting?
Because there’s a big difference between:
– emotional / one-off feedback
and
– patterns you can consistently turn into product decisions
Do you already see clear signals emerging,
or does it still require interpretation on your side?
Feels like that step — turning feedback into actionable insight — is where a lot of tools start losing value.
Happy to take a closer look at that flow if useful — it seems like there’s a lot of potential there beyond just collecting feedback.
That's the right question honestly.
What we see is that responses are more consistent than you'd expect. When 8 out of 20 users say some version of "pricing felt too high" that is already a pattern. Auto tagging helps a lot too since every response gets labeled so you can filter without reading every transcript.
Would love to show you the dashboard if you want to take a look. Happy to get you access.
That would be great — I’d love to take a look.
I’m especially curious to see:
– how those patterns are surfaced in the dashboard
– how easy it is to go from raw feedback → clear product decisions
– and where it might still require manual interpretation
If helpful, I can go through it from a fresh-user perspective and map out where the flow feels strong vs where insight might get lost.
Love that, go ahead and sign up at flidget.com and poke around. Would love to hear what you find.
Took a quick look and went through the onboarding + dashboard.
First impression — the idea is strong, but the first “value moment” feels a bit delayed.
Right now the flow is:
sign up → configure → land on an empty dashboard
And as a new user I’m not fully sure what to do next or how to see it working quickly.
For example, I found myself looking for a way to trigger something (like a test interaction or demo signal), just to understand how the system actually behaves.
Also small thing:
there are a couple of moments where the UI feels slightly unclear (e.g. duplicate actions leading to the same place), which adds a bit of hesitation early on.
Overall it feels like the value is there, but it takes a bit too much effort to reach that “aha, this is useful” moment.
Happy to dig deeper if helpful — I think there’s a lot of potential in tightening that first interaction.
This is genuinely useful, thank you for actually going through the flow and not just leaving a surface level take.
The empty dashboard problem is real. We have heard it a couple of times now and it is clearly the thing to fix next. The idea of a test interaction or a demo signal is something we have been thinking about and you naming it directly makes it easier to prioritize.
The duplicate actions thing I would love to dig into more if you remember where exactly. Even one specific spot would help.
Appreciate you taking the time. This kind of feedback is worth more than a hundred upvotes.
Yeah, one small example I noticed:
on the “You’re in” screen there are two actions that feel like they do the same thing:
– “Go to dashboard” button
– and “Continue to dashboard” below
It made me pause for a second like:
“is there a difference or am I missing something?”
Also on the dashboard itself, I found myself looking for:
“what do I do right now to see this working?”
There’s no obvious first action, so I ended up trying the AI input just to figure out the next step.
Feels like the main gap is not the feature itself, but the lack of a clear “first action → visible result” loop.
Something like:
– trigger a test interaction
– instantly see a conversation / tag / insight appear
would probably make the value click much faster.
If helpful, I can map out the first 2–3 minutes of the user journey in a bit more structured way — where exactly hesitation appears vs where it feels clear.
Love this. You basically reverse-engineered the problem from the symptom instead of the metric. That's rare.
Most people fix the cancel flow. You fixed the silence before the cancel flow. That's the actual churn.
How long did the drift detection take to calibrate on real users?
Exactly that. The metric was clean but the signal underneath it was broken.
Drift calibration honestly took a few weeks of watching real usage patterns before the scoring felt reliable. The tricky part was figuring out which actions actually predicted churn versus which ones just looked like engagement. Login frequency alone is basically useless. What matters is whether users are hitting the features that create real value in your product.
Once we anchored scoring around those specific actions the Healthy, Risky, Drifting labels started making intuitive sense. Now when someone shows up as Drifting the reason is always something concrete like "9 days inactive, never used invite teammate" rather than just a number you have to decode.
Still tuning it as more data comes in. Would love to hear what signals you track if you have dealt with this.
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