So, spent a week doing churn interviews... and our shiny AI recommendation engine? Never came up once.
Users churn because the core workflow is broken. AI on top of a broken workflow is just a faster way to fail. Build it, ship it, tweak it! - but fix the foundation first or you're decorating a condemned building.
Founders stay paralyzed for months protecting an idea nobody's thought about for 30 seconds. You know what actually gets stolen? Products with traction. Proof. Users who talk about it.
So, the fastest way to protect your idea... is to make it worth stealing. PLG or GTFO.
What if the most important PLG signal isn't in your analytics dashboard at all?
We spent months tuning our activation funnel. Events, cohorts, the whole thing. Drop-off at step 3, always step 3. Nobody could explain why until an engineer pointed at a loading bug buried in the code that no event had ever captured.
Code-level data: a product metric.
Every quarter we'd dig into activation data trying to figure out why numbers dropped. Ran user interviews. Rewrote copy. Redesigned the checklist. Sometimes it helped. Usually it didn't. Then a dev casually mentioned they'd refactored the auth flow six weeks earlier.
That was it. That was the whole drop. Six weeks of analysis, and the answer was sitting in a git commit nobody told product about.
Code-level changes are the silent variable in every PLG metric you track. Feature flags, component updates, API changes, they all reshape the user experience before you even know it happened. But we keep building dashboards that start at the session layer and wonder why our insights feel shallow.
The most important metric layer: in the codebase you don't have access to.
We obsess over behavioural data. Clicks, sessions, funnel drop-off. But the moment a developer changes a component name, your entire onboarding flow silently breaks and your metrics just show 'users didn't complete step 3.' Code-level data is the layer that explains the gap between what you shipped and what users actually experienced.
Everything else is just reading smoke signals.
The whole pitch of product-led growth was removing humans from the sales equation. So naturally we responded by adding dashboards, then analysts to read the dashboards, then AI to interpret what the analysts missed, then meetings to discuss what the AI flagged. The product is still just sitting there. Selling nothing. Waiting.
First time: pushed a retention campaign on all churned users, got 8% win-back. Team loved it. Second time: same campaign, 2% win-back. Nothing changed in the email. What changed was we'd stopped filtering by activation status.
The first campaign accidentally targeted users who'd hit our aha moment but churned for pricing reasons. Recoverable. The second batch had never activated at all. No email in the world fixes 'I never understood what this product does.'
Retention tactics only work on users who already got value once. Before that point you're just buying time. If your activation rate is under 40% and you're running win-back flows, you're spending money on the wrong problem. Plug the activation hole first, then protect what's actually working.
What's your activation benchmark before you even start thinking about retention? Genuinely curious what others are using.
What does AI driven activation actually mean in practice, because I genuinely want to know?
We spent a quarter trying to make our onboarding smarter. Read everything, including the Skene code driven model. Good ideas in there. Real ones. But here's what nobody writes about: the model assumes you have clean user event data, a dev team with bandwidth, and a product that doesn't change every three weeks. We have none of those things.
The AI suggestions were fine. The maintenance burden was not. Two months later we were back to a static checklist that a junior PM built in a weekend. It converts better than anything we shipped that quarter.
Maybe the lesson isn't that AI can fix activation. Maybe it's that activation breaks for boring, fixable reasons that don't need a model at all.
What's the most overhyped solution in SaaS right now?
Mine is AI powered onboarding, We've been chasing the right tool for two years. The activation problem was never technical.
What's a framework everyone in SaaS is treating as gospel that you think is half wrong?
Skene's model assumes your activation bottleneck is visibility. But most teams I know already have the data. They just don't act on it, AI layered on top of a broken process is still a broken process, just faster.
I redesigned our pricing three times before I understood what “value metric” actually meant in practice.
We launched with seat‑based pricing because that’s what everyone else in our category did. Logical enough. Except our free users were mostly solo operators who would never add a seat. They hit the paywall, saw “add team members,” assumed the product wasn’t for them, and churned quietly. No rage, no feedback, just gone.
Switched to usage‑based. Conversion rate doubled in a quarter.
The hard lesson: your pricing structure tells users who the product is for. If the upgrade logic doesn’t match how free users are already getting value, you’re not solving a conversion problem you’re solving a product‑market fit problem disguised as a pricing problem. Those are very different things to fix, and confusing them will waste you a year of experiments that go nowhere.
What’s the pricing mistake you’d go back and fix first if you could?
We're a team of 2 and honestly our CRM is a mix of Notion, a Stripe dashboard and a shared Google Sheet that one of us updates when we remember.
It works until it doesn't. Last month we nearly missed a renewal because the data lived in three different places and nobody caught it in time.
Curious what other small teams are actually using day to day. Not what you'd recommend in theory, what you're actually running right now.
Are you using a proper CRM or just duct-taping tools together and hoping for the best? and if you made the switch to something more structured, was it actually worth the setup time?
We had a growth dashboard with 14 shiny metrics. Product was proud of it. Activation rate looked great, engagement scores climbing, PQLs ticking up. Every standup felt like a win. Then someone finally pulled 90‑day retention by cohort and the room went dead quiet. Users were ‘activating’ and disappearing inside three weeks.
The mistake was obvious in hindsight: we optimized the onboarding flow for the metric, not for the user. Get someone to click five features in session one? Activation. Whether those features made sense for their workflow? Not our concern. We celebrated the funnel, not the long‑term value.
PLG metrics are only honest if you’re willing to follow the user past the moment they stop being interesting to your dashboard. Most teams don’t. They celebrate activation and call it growth.
The one metric we measured wrong was activation. It looked healthy, but it was just noise. Retention told the real story.
So tell me, what’s the one metric you’ve seen teams obsess over that turned out to be completely misleading?
I have a startup idea that I genuinely believe has strong potential. The full concept, structure, and vision are clear in my mind, but I’m struggling with where to begin.
I don’t have prior experience in launching a business, building a team, or managing operations. On top of that, I currently don’t have the financial resources to fund the project.
I’m looking for guidance on the first practical steps I should take to move from idea to execution. How do I validate the idea, start building, and eventually scale? And how can I approach hiring or collaborating with others when I’m starting from zero?
Any advice, frameworks, or personal experiences would be really appreciated.
We spent weeks adding just one more request. Integrations, dashboards, edge cases. The backlog grew, the product sagged.
Retention slipped while we chased noise. Turns out, nobody wanted the bloated version. They wanted the focused one.
The best products scale because they stay simple. Listening doesn’t mean saying yes to everything. It means protecting the core.
The annoying gap for me is not the first response. It is the stuff that happens after someone fills out a form, replies to an email, or books time.
A task gets created somewhere, a note lands in the CRM, someone says they handled it, and a week later you realize the important follow-up never actually happened. It is worse when the team is small because everyone assumes the owner saw it.
I have been trying to make the handoff less dependent on memory, but most setups either turn into noisy checklists or they only show that something ran, not whether it changed anything useful.
For people running lean teams, what do you actually trust here? One owner per lead, a daily manual review, stricter CRM stages, Slack alerts, something else?
What does AI driven activation actually mean in practice, because I genuinely want to know?
We spent a quarter trying to make our onboarding smarter. Read everything, including the Skene code-driven model. Good ideas in there. Real ones. But here's what nobody writes about: the model assumes you have clean user event data, a dev team with bandwidth, and a product that doesn't change every three weeks. We have none of those things.
The AI suggestions were fine. The maintenance burden was not. Two months later we were back to a static checklist that a junior PM built in a weekend. It converts better than anything we shipped that quarter.
Maybe the lesson isn't that AI can fix activation. Maybe it's that activation breaks for boring, fixable reasons that don't need a model at all.
I spent three months trying to build a custom community hub for my platform, and honestly, it was a waste of resources. We struggled with real-time latency and, even worse, the moderation was a nightmare. Every time we gained new users, the "trolls" would just ruin the experience.
I finally gave up and switched to a ready-made engagement SDK. It allowed us to embed community chats and even live streaming directly into our existing app. The AI-driven moderation took care of 95% of the toxic content automatically, which was a huge relief. Now my team can finally focus on the core product instead of fixing chat bugs. It really changed how our users interact with each other; the vibe is much more positive now.
I’m curious, do you guys prefer building these "social layers" in-house to have full control, or is it better to use third-party tools and focus on the main business logic? What's your take?
I’m a founder and a mother, running my second SaaS right now.
My first startup didn’t exactly fail, but it never really scaled the way I wanted. We had a decent product, some early traction, but things always felt harder than they should have been. Our grrowth was inconsistent, execution was messy, and I kept thinking the problem was something external.
This time, things moved very differently. We crossed around $3M ARR in roughly 18 months. Same kind of ambition, slightly different market (not a competitor to our first SaaS), but the way we operated internally changed a lot. And honestly, that made all the difference.
Let me walk you through what actually worked.
1. I didn’t start from scratch this time
In my first startup, I made the mistake most founders make. I built everything from zero, including the team. New hires, new culture, new alignment issues, all at the same time.
The second time, I didn’t do that. I took one PM from my previous company, someone who already understood how I think, how I make decisions, and the kind of speed I expect. That single decision removed a lot of friction early on.
You don’t realize how much time gets wasted in alignment until you don’t have to do it.
2. We stayed lean, but paid well
We’re a team of 12. That’s it.
There’s no middle layer, no unnecessary roles, and no one is just “there.” Everyone owns something meaningful, and decisions move quickly because there aren’t too many people involved.
But there’s one thing I changed from my first startup.
I stopped underpaying.
In my first company, I believed people would stay for the vision. Some do, but most don’t. People care about stability and compensation, especially right now when layoffs are everywhere. So we paid well, even when it felt slightly uncomfortable financially. That decision paid off in execution speed and accountability fr.
3. Hiring wasn’t reactive this time
Earlier, we used to hire when things broke. This time, we were much more structured.
We relied on three main channels. LinkedIn helped with visibility and inbound, referrals worked extremely well and we paid anywhere between $5K to $10K per hire, and for engineering, we used Uplers.
Uplers worked particularly well for AI and ML roles. We hired two core engineers remotely from India through them, and both were pretty strong from day one. They were already vetted, experienced, and we didn’t have to spend weeks filtering through irrelevant profiles.
When you’re a small team, saving that time matters more than saving money.
4. We stopped chasing perfection
This was one of the hardest lessons to accept.
In my first startup, we delayed launches because things didn’t feel ready. This time, we shipped faster. Not sloppy, but not overpolished either.
Users don’t reward perfection. They reward speed and usefulness.
Once we understood that, things started compounding faster.
FYI: My engineers took Claude code to a different level, it feels unbelievable ti me seeing how these AI ttools have changed things.
5. Our stack was simple, but it actually supported how we worked
We didn’t go tool-hopping this time. We picked things we could stick with and built our processes around them.
Let me explain how each one actually helped.
Salesforce Starter
We had already used Salesforce in my first startup, so the team didn’t need to relearn anything. That familiarity itself saved time.
The biggest thing it solved was chaos. Earlier, leads were scattered across spreadsheets, messages, and random notes. This time, everything sat in one place. Pipeline visibility became clear, and no deal just disappeared because someone forgot to follow up.
Now coming to the AI side of it.
Salesforce has been pushing Agentforce, which is basically their AI layer that sits on top of your CRM and actually works with your data. It’s not just reporting anymore. It can look at your pipeline, understand context, and suggest actions or automate routine workflows.
We started using it for things like nudging reps on follow-ups and helping prioritize deals. It’s not some magic system that fixes bad sales, but once your data is clean, it genuinely reduces manual work and helps your team focus on actual conversations.
That said, setup can feel heavy in the beginning. If your team isn’t disciplined with CRM usage, it feels like extra work before it starts helping.
Chatway
This came directly from a pain we were facing.
In the early days, support was messy. Same questions kept coming in, responses were delayed, and sometimes we just missed conversations altogether. And in SaaS, a delayed reply is basically a lost customer.
We implemented Chatway and fed it all our FAQs, onboarding docs, and support content.
Now their AI agent handles a large chunk of repetitive queries. AI chat systems like these are designed to understand user queries and respond instantly using trained knowledge, which is why they reduce wait times and improve efficiency.
The biggest difference we saw was in response time. Users started getting answers instantly, even when the team was offline. That alone improved conversions and reduced pressure on the support team.
But it’s not perfect.
I still wish it had slightly deeper integrations like Intercom, especially around tracking user behavior and events. Also, context memory across longer conversations could be better. When you scale, you start noticing these gaps.
Still, for a lean team, it removed the need to hire multiple support people early on.
Gather
This one is a bit underrated.
We’re fully remote, and Slack plus Zoom all day started feeling exhausting. Everything had to be scheduled, and conversations felt forced.
Gather changed that dynamic a bit. It gave us a more natural way to interact. People could just jump into conversations, collaborate without planning every call, and it felt closer to an actual workspace.
Not everyone uses it the same way, which is a limitation. Some people still prefer async communication. But overall, it helped make remote work feel less isolated.
Slack
We used Slack for day-to-day communication. Nothing fancy here, it just works and integrates with everything else.
6. Support became part of growth
This was a mindset shift for me.
Earlier, support was just something you had to manage. Now it directly impacts revenue. When someone asks a question, timing matters more than the answer itself.
If you respond quickly, you win. If you don’t, someone else does.
We made sure that gap never exists.
7. Biggest lesson from my first startup
I used to think growth was about getting more leads.
Now I think it’s about not losing the ones you already have.
Most companies already have enough traffic. They just lose people in slow responses, bad onboarding, or poor follow-up.
Fixing that alone can change everything.
8. My Last thing to Say
Tbh, running a startup as a mother is not balanced.
Some days work takes over. Some days family does. You don’t get it perfect, you just keep going.
If I had to summarize everything, my first startup gave me lessons. This one worked because I actually followed them.
Happy to answer anything.
Activation rates went up. Referrals went up. Marketing team headcount went down.
Nobody tells you that 'product as marketing channel' is sometimes just a slow way to justify cuts. The product does more, the marketing budget does less, and leadership calls it maturity. Maybe I'm wrong. But I've sat in enough roadmap reviews where 'virality loop' was the answer to 'why are we reducing campaign spend' to be a little skeptical.
Anyone else seen this play out?