Launching today
Banyan AI Lite
AI detecting & preventing SaaS churn
73 followers
AI detecting & preventing SaaS churn
73 followers
Churn is the #1 killer of SaaS. Up to 50% of SaaS struggle with high churn. Banyan AI is here to help. Our tool enables you to detect churn before it happens and prevent it. With Banyan AI, you can unify your most critical revenue data (CRM, billing, support, product usage) into a single interface. Based on this data, you can identify churn risks and expansion opportunities (customers ready to buy). Time to value: minutes. Results: measurable and quantifiable. Churn prevented, revenue saved.
Banyan AI Lite
Hey Product Hunt 👋,
I’m Davit, co-founder of Banyan AI, and we’re excited to launch here for the first time.
Did you know that a 5% monthly churn rate can reduce your annual revenue by nearly half? Or that many SaaS companies lose 2–5% of their revenue to leakage? New leads matter, but your existing customers are your real treasure.
If you're struggling with churn or finding it hard to expand revenue, we’ve got you covered. Welcome to Banyan AI 🌳🚀
Our platform unifies data across your tool stack (billing, CRM, product analytics, support) and detects signals that are scattered across those tools. Banyan AI automatically detects:
Customers likely to churn
Hidden revenue leaks
Expansion opportunities
Instead of digging through dashboards, you get clear AI insights about your revenue health and what needs attention. Check out our website or our blog.
I’ll be here in the comments all day. Thanks for checking out Banyan AI 🙏
In education, churn looks different than in typical SaaS — a student finishes a course and leaves, which is not churn, it is a natural end. But someone who stops halfway through is a completely different signal. How well does the detection handle that difference, where inactivity does not always mean risk?
Banyan AI Lite
@klara_minarikova Thanks for the question Klara! Well, if natural end can be counted as churn, all of us have 100% churn guarantee :D But jokes aside: a very good question. If customer was less active during last week, is he about to churn, or is he in vacation? You can add seasonality as variable, simply ask AI to calculate how customer behaviour might be related to seasonal behaviour in respective country. And then, most importantly, when you just watch one data stream (f. e. only billing, CRM, or product usage) then your insights are limited. But now add other info layers, f. e. how many support tickets during last week? Any failed payments? And you get clearer picture:
failed payment + less usage = churn signal
failed payment + less usage + many critical support tickets = strong churn signal
less usage + seasonality effect (summer) + recent upgrade = no churn signal
Banyan AI Lite
@klara_minarikova Great point, that’s exactly where a lot of generic churn models break.
Banyan doesn’t treat inactivity as a universal risk signal. It looks at behavior in the context of expected lifecycle. In your example, completing a course and dropping off is “healthy”, while stopping mid-way is not.
In practice, this means we define expected patterns first. Things like typical course duration, completion rates, and engagement milestones. Then we compare each user against that path rather than against a global average.
So inactivity after completion is ignored, while inactivity before key milestones gets flagged. Same signal, very different interpretation depending on context.
This is also why we let teams adjust logic based on their model, because education, SaaS, and marketplaces all behave very differently here.
this is a useful space to be building in. a lot of saas teams only realize churn is becoming a real problem once the damage is already visible, so bringing billing, product, crm and support signals together makes a lot of sense.
curious, what kind of signal ends up being the strongest early warning most of the time, product usage drop, support issues, or something else?
Banyan AI Lite
@akshay_kumar_hireid Thanks Akshay, product usage drop is one of major signals. If usage was low all the time but customer was paying, it is in fact less of the problem, compared to when usage dropped significantly over short period of time. This is one of major red flags (unless customer is simply in vacation ;) )
@davitausberlin Totally agree, Davit. The sudden drop matters much more than low usage in isolation. A customer can stay consistently low-usage and still be healthy, but when engagement falls sharply over a short period, that usually points to something changing in value perception, priorities, or internal adoption.
Banyan AI Lite
@akshay_kumar_hireid Appreciate that, exactly the problem we’re seeing across most teams.
There’s no single “silver bullet” signal, it’s usually the combination that matters. That said, the most reliable early warning tends to be a drop in product usage relative to that customer’s own baseline, not absolute usage.
On its own, usage can be misleading. Some customers are naturally low-activity. It becomes powerful when paired with other signals, like:
declining engagement + no recent support interaction (silent churn risk)
usage drop right after a negative support ticket
reduced activity from key users or admins
Interestingly, support spikes are often a late signal, unless you look at sentiment and resolution patterns.
So in practice, Banyan looks at how these signals move together over time rather than picking one. That’s usually where the real early warning shows up.
@konstantinalikhanov Well put, Konstantin. That’s exactly what I was trying to get at — the baseline shift seems far more telling than absolute numbers alone. And I agree, the real insight probably comes from how usage, support, and key-user activity move together over time rather than treating any one of them as a standalone churn signal.
nice one honestly. most teams talk a lot about getting new customers, but keeping the existing ones is where so much revenue gets lost quietly. i like that this seems focused on helping teams spot the risk earlier instead of just reporting on it later.
curious, are people finding more value in the churn detection side first or the expansion opportunity side?
Banyan AI Lite
@nayan_surya98 Thanks Nayan! Most are interested in Churn indeed, since this is a silent killer nr. 1 of many SaaS companies. Have been there, seen that, that's why we have started this project. Expansion is a nice bonus.
Banyan AI Lite
@nayan_surya98 Thanks, that’s exactly how we think about it. most teams are very reactive here.
In the beginning, churn detection usually clicks first. It’s more urgent and easier to grasp. If you show a list of accounts at risk with clear reasons, teams act on it immediately.
What’s interesting is what happens after. Once they trust the signals, they start paying more attention to expansion. Not just who might churn, but who is underutilizing the product, who is close to limits, or showing patterns that typically lead to upgrades.
So churn is the entry point, expansion is where a lot of the upside comes from over time.
Good luck team! Cool idea. How long does the setup usually take if someone wants to connect their SaaS tools?
Banyan AI Lite
@steffen_rehmann Thanks for asking Steffen. Normally it takes just minutes. Most tools can be connected via OAUTH2 or bearer token. Hardest part is (in some tools) finding these tokens. But once you have them, it takes a minute, give or take
Banyan AI Lite
@steffen_rehmann Thanks a lot, appreciate it!
Setup is usually pretty quick. Most teams are up and running in under an hour. Connecting core tools like billing, CRM, and support is straightforward, and you start seeing first insights shortly after.
Sounds interesting! How does it alert you about who is about to churn — through email, in-app notifications, or something else?
Banyan AI Lite
@rati_soselia Rati, thanks for a good question. You chose it, you can add Slack to your workspace and get notifications via Slack. Or add email, or Teams or whatever, you name it, we either have it or you can integrate within 10 minutes.
Banyan AI Lite
@rati_soselia Thanks!
Right now it’s mainly in-app, you get a clear view of accounts at risk, what changed, and why they’re flagged.
On top of that, we support alerts via email and can push signals into tools like Slack or your CRM, so the right people get notified where they already work.
Banyan AI Lite
Hey Product Hunt 👋,
I’m Konstantin, CTO and co-founder of Banyan AI.
If you want to detect customers who are about to churn, or identify those with the highest expansion potential and real impact on your revenue, forget about building your own dashboards, stitching together dozens of APIs, or copying data into Excel.
We built Banyan AI so you don’t have to do all of that. With our tool, everything works within minutes — fast time to value, no technical skills needed. You ask, Banyan answers.
It sounds simple, but there’s a lot of engineering behind the scenes.
Happy to answer any technical questions here 👇