Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Here's what nobody tells you about product-market fit: you'll know it's happening long before your metrics dashboard shows it.
Three years ago, I was working with a B2B SaaS client who was obsessing over their trial-to-paid conversion rate. Every week, we'd pour over analytics, A/B test signup flows, and debate whether 14 days or 30 days was the "perfect" trial length. Classic startup theater - lots of activity, minimal progress.
Then something interesting happened. The founder started getting weird requests. Customers asking if they could pay annually upfront. Sales calls where prospects said "I don't need a demo, I just want to get started." Support tickets that were actually feature requests from engaged users, not frustrated complaints.
That's when I realized we were looking at the wrong signals. Product-market fit isn't something you measure - it's something you feel. And it shows up in customer behavior long before it shows up in your carefully crafted KPI dashboards.
After working with dozens of SaaS startups at different stages, I've learned that the most reliable product-market fit signals aren't the ones everyone talks about. They're the subtle behavioral changes that happen when people stop evaluating your product and start depending on it.
In this playbook, you'll discover:
The 7 early signals that predict product-market fit before your metrics catch up
Why churn rate and NPS scores can be misleading indicators
How to spot the difference between "nice to have" and "must have" user behavior
My framework for tracking qualitative signals alongside quantitative metrics
What to do when you're seeing mixed signals (and how to interpret them)
This isn't another "metrics you should track" guide. This is about recognizing when you've built something people actually need, not just something they'll politely try.
Industry Wisdom
What every startup accelerator teaches about PMF
Walk into any startup accelerator or read any SaaS growth blog, and you'll hear the same advice about measuring product-market fit:
The Standard Metrics Everyone Tracks:
Net Promoter Score (NPS): "Would you recommend this to a friend?"
Trial-to-Paid Conversion: Aim for 15-20% or you don't have PMF
Monthly Churn Rate: Keep it under 5% for healthy SaaS
Customer Acquisition Cost (CAC) to Lifetime Value (LTV): 3:1 ratio is the golden standard
Sean Ellis Test: 40% of users saying they'd be "very disappointed" without your product
This advice isn't wrong - these metrics matter. But there's a fundamental problem with relying on them as your primary PMF indicators: they're lagging indicators. By the time your NPS hits 50 or your churn drops below 5%, you've either already achieved product-market fit or you've been struggling without it for months.
The bigger issue is that these metrics can be gamed or misinterpreted. I've seen startups with "great" conversion rates because they had friction-heavy signup flows that filtered out casual users. I've watched companies celebrate low churn rates when their real problem was that nobody was signing up in the first place.
Most dangerously, focusing on these standard metrics can create a false sense of progress. You start optimizing for better numbers instead of listening to what customers actually need. You become data-driven instead of customer-driven.
The reality is that product-market fit is more about customer behavior than customer satisfaction. It's about how people use your product when they think you're not watching, not how they respond to your surveys.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breakthrough came when I stopped focusing on the metrics everyone said mattered and started paying attention to the weird stuff happening around the edges.
With that B2B SaaS client, the "aha" moment wasn't in our dashboard - it was in the founder's inbox. He showed me an email from a customer asking if they could prepay for two years because the tool had become "mission-critical" to their workflow. Another wanted to add three team members immediately, no trial needed.
These weren't responses to a survey or funnel optimization. These were unprompted behaviors from people who had crossed the line from "trying" the product to "needing" it.
That's when I developed what I call the "Signal Stack" - a way to track the behavioral patterns that predict product-market fit before traditional metrics catch up. It's based on one core insight: when people truly need your product, they start acting differently around it.
The most telling example was a customer who hadn't just upgraded their plan - they'd started training other departments in their company to use the tool, without being asked. When someone becomes your unpaid evangelist internally, that's not satisfaction. That's dependency.
I started tracking these "edge behaviors" across multiple clients and found patterns. Companies that later achieved strong PMF always showed specific early signals that had nothing to do with conversion rates or churn percentages.
The problem is that most founders are so focused on their KPI dashboards that they miss the human stories happening in their support tickets, sales calls, and user feedback. They optimize for metrics instead of listening for signals.
This is especially critical in B2B SaaS because business customers don't behave like consumers. They don't give you high NPS scores just because your product is "nice." They only recommend tools that solve real business problems. When they do recommend you, it means something different than a consumer product recommendation.
Here's my playbook
What I ended up doing and the results.
Here's my 7-signal framework for detecting product-market fit before your metrics dashboard catches up:
Signal 1: Unprompted Expansion Requests
When customers start asking to add team members, upgrade plans, or pay annually without any sales prompt from you, that's not just growth - it's dependency. Track how many expansion conversations you didn't initiate.
Signal 2: Feature Requests from Engaged Users
The quality of feature requests changes dramatically when you hit PMF. Instead of asking for basic functionality or things that already exist elsewhere, engaged users request specific enhancements that make your tool more integral to their workflow.
Signal 3: Internal Evangelism
This is the strongest signal: customers training their colleagues or other departments on your tool without you asking. When someone becomes your unpaid internal champion, they've crossed the line from user to advocate.
Signal 4: Process Integration Depth
Pay attention to how deeply customers integrate your tool into their workflows. Are they just using it for one task, or has it become part of multiple processes? When customers start building their procedures around your product, that's true product-market fit.
Signal 5: Competitor Comparison Conversations
Early in the customer journey, prospects ask "How are you different from [competitor]?" Once you have PMF, conversations shift to "Can you integrate with our existing tool?" They're not comparing - they're planning to stay.
Signal 6: Support Ticket Quality Shift
In the beginning, support tickets are mostly "How do I...?" or "This doesn't work." With PMF, tickets become "Can I do this advanced thing?" or "Is there a way to..." The nature of questions changes from basic usage to optimization.
Signal 7: Organic Word-of-Mouth Evidence
Track where new signups heard about you. When "colleague recommendation" or "saw it mentioned in [industry forum]" starts showing up regularly in your signup surveys, that's organic distribution happening.
The Signal Tracking System:
I create a simple weekly ritual: every Friday, review the "weird" customer behaviors from the week. Not the metrics - the stories. What unusual requests came in? What surprising usage patterns appeared? Which customers acted in ways you didn't expect?
Document these in a "Signal Log" alongside your traditional metrics. Over time, you'll start to see patterns that predict business outcomes better than conversion rates alone.
Behavioral Changes
Track how customers act differently when they need vs. want your product
Early Warning System
Identify PMF signals weeks before they show in your metrics dashboard
Qualitative Tracking
Document customer stories and edge behaviors alongside quantitative data
Signal Interpretation
Learn to read between the lines of support tickets and sales calls
The results of tracking these behavioral signals have been consistently revealing across multiple SaaS clients:
Early Detection Advantage: Behavioral signals typically appear 4-6 weeks before traditional PMF metrics start showing improvement. This early warning system allows for faster pivoting or doubling down on what's working.
False Positive Prevention: Several clients had "good" traditional metrics but weak behavioral signals, which predicted later struggles. The Signal Stack helped identify when apparent success was actually just delayed churn.
Resource Allocation Insights: By tracking which customer behaviors predict expansion revenue, teams could focus sales efforts on accounts showing specific signal patterns rather than just high usage metrics.
Product Direction Clarity: The quality of feature requests from signal-positive customers provided clearer product roadmap direction than traditional user feedback surveys.
Most importantly, this approach changes the conversation from "Are we there yet?" to "What are customers telling us through their actions?" It's a more nuanced, human-centered way to understand product-market fit.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the top lessons learned from tracking behavioral PMF signals across multiple SaaS companies:
1. Context Matters More Than Numbers
A customer asking to add one team member might be routine growth, but asking to add an entire department signals something different. Always dig into the context behind the behavior.
2. Mixed Signals Are Normal
Don't expect all seven signals to light up simultaneously. Different customer segments may show different signal patterns. The key is identifying which signals matter most for your specific market.
3. B2B vs B2C Signal Differences
B2B customers show PMF through process integration and internal advocacy. B2C shows through usage frequency and organic sharing. Tailor your signal tracking to your market type.
4. Early Signals Can Be Misleading
Some customers are naturally enthusiastic adopters. The real test is whether signal behaviors persist and spread to other customers, not just individual power users.
5. Documentation Discipline Is Essential
Behavioral signals are easy to forget or dismiss as anecdotal unless you systematically track them. The weekly Signal Log practice is crucial for pattern recognition.
6. Combine with Traditional Metrics
Behavioral signals predict PMF, but traditional metrics confirm and measure it. Use both systems together, not as replacements for each other.
7. Team-Wide Signal Awareness
Sales, support, and product teams all interact with customers differently and see different signals. Create a system for sharing signal observations across the entire team.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this behavioral signal tracking approach:
Set up weekly "signal review" meetings to discuss unusual customer behaviors
Create a shared document for logging customer stories that don't fit normal patterns
Train your sales and support teams to recognize and document signal behaviors
Track expansion requests by source - which come from your outreach vs. customer initiative
For your Ecommerce store
For E-commerce businesses adapting this framework:
Monitor repeat purchase patterns and order frequency changes
Track customer service inquiries that indicate deeper product integration
Watch for organic social mentions and user-generated content around your products
Document when customers start recommending your products in reviews or forums