Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Most founders spend 90% of their time building the product and 10% on marketing and audience building. It should be reversed.
After watching countless startups fail not because their product was bad, but because they built it for the wrong people, I've become convinced that traditional product-market fit approaches are fundamentally broken in the AI era.
The old playbook says: build, measure, learn. Survey your users. Run experiments. But here's the uncomfortable truth: your customers can't tell you what they want when AI is changing their workflow every three months.
Last year, I worked with a B2B SaaS client who had spent six months perfecting their user onboarding based on traditional feedback. Users said they loved it. Conversion rates were decent. But something was missing – real engagement, real stickiness, real PMF.
That's when we pivoted to an AI-driven approach that changed everything. Not AI for the sake of AI, but AI as a systematic way to understand what people actually do versus what they say they want.
Here's what you'll learn:
Why traditional PMF validation is failing in fast-changing markets
My systematic approach to using AI for customer behavior analysis
The 3-layer framework that uncovered hidden user patterns
How we increased engagement by 300% by listening to what users did rather than what they said
When this approach works (and when it doesn't)
This isn't about replacing human insight with algorithms. It's about using AI to see patterns we're blind to and validate demand in ways traditional surveys never could. Read more SaaS strategies here.
The Traditional Way
What every startup founder has been told about PMF
If you've raised any funding or talked to any accelerator, you've heard the standard product-market fit gospel:
The Traditional PMF Playbook:
Build an MVP based on assumptions
Run customer interviews and surveys
Measure engagement and retention metrics
Iterate based on feedback
Look for that magical moment when growth becomes effortless
This advice exists because it worked beautifully in slower-moving markets. When customer needs were stable for years, when competitors took months to copy features, when workflow changes happened gradually.
But here's the problem: We're not in that world anymore.
AI is changing how people work every quarter. The tools your customers used six months ago might be obsolete. The problems they're solving today didn't exist last year. Traditional PMF assumes static user needs in a dynamic world.
The conventional approach also suffers from the "say-do gap". People are notoriously bad at predicting their own behavior. They'll tell you they want feature X in a survey, then completely ignore it when you build it.
Even worse, traditional PMF validation is retrospective. By the time you've surveyed users, analyzed feedback, and built features, the market has moved. You're always playing catch-up instead of anticipating where things are going.
This creates a dangerous cycle: founders build what users say they want, users don't engage as expected, founders survey more users, build more features nobody uses. Sound familiar?
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came during a project with a B2B SaaS client who was convinced they had PMF. All the traditional metrics looked good: decent trial-to-paid conversion, positive NPS scores, users saying they "loved" the product in interviews.
But something felt off. Users weren't becoming power users. They weren't expanding their usage. They weren't referring others organically. They had a "nice-to-have" product masquerading as a "must-have."
The client had spent months optimizing their onboarding flow based on user feedback. Users complained the setup was too complex, so they simplified it. Users wanted more features, so they built them. Users asked for integrations, so they added those too.
Yet engagement remained stubbornly flat. We were solving the wrong problems because we were asking the wrong questions.
That's when I realized: we were treating PMF like a customer service problem when it's actually a pattern recognition problem. We needed to understand what successful users actually did differently from unsuccessful ones, not what they said they needed.
Traditional surveys and interviews couldn't capture the nuanced behavioral patterns that separated power users from casual users. We needed a way to analyze user behavior at scale, identify hidden patterns, and predict which early signals actually led to long-term success.
The breakthrough moment came when we stopped asking "what do you want?" and started asking "what do successful users do that unsuccessful users don't?" But to answer that question systematically, we needed AI.
Here's my playbook
What I ended up doing and the results.
Here's the systematic approach I developed for using AI to achieve true product-market fit:
Step 1: Behavioral Data Collection
Instead of surveys, I implemented comprehensive behavioral tracking. Every click, every feature interaction, every workflow pattern. Not for creepy surveillance, but for pattern recognition.
The key insight: PMF isn't about what users say – it's about what they repeatedly do without being asked.
Step 2: AI-Powered Cohort Analysis
I fed all this behavioral data into AI models to identify user segments we couldn't see manually. The AI found patterns like:
Users who performed action X within their first week had 5x higher retention
Users who engaged with feature Y became power users 3x more often
Certain workflow sequences predicted churn with 85% accuracy
Step 3: Predictive PMF Scoring
We built an AI model that could predict which new users were likely to become power users based on their first-week behavior. This became our "PMF compass" – showing us which features and workflows actually mattered.
Step 4: Dynamic Feature Validation
Instead of building features based on requests, we tested small behavioral changes and let the AI tell us which ones improved our PMF scores. We discovered that some highly-requested features actually hurt engagement, while some "boring" UX tweaks dramatically improved retention.
The Three-Layer Framework:
1. Pattern Recognition Layer: AI identifies behavioral patterns that humans miss
2. Predictive Layer: Models predict which users will succeed before they churn
3. Validation Layer: Every product decision gets tested against predicted PMF impact
This wasn't about replacing human intuition with algorithms. It was about using AI to see what our biases prevented us from seeing, then making human decisions with better data.
The result? We finally understood what "good" looked like and could optimize the entire funnel toward behaviors that actually predicted success.
Pattern Recognition
AI revealed user behavior patterns invisible to traditional analysis, showing which actions predicted long-term success vs. churn.
Predictive Scoring
Real-time PMF scoring system that evaluated new users within their first week, enabling proactive intervention before churn.
Dynamic Validation
Feature decisions validated against AI-predicted PMF impact rather than user requests, preventing feature bloat and focusing development.
Behavioral Truth
Systematic tracking of what users actually do versus what they say, revealing the gap between stated and revealed preferences.
The transformation was dramatic and measurable:
Engagement Transformation:
User engagement increased 300% within 90 days
Power user conversion rate jumped from 12% to 34%
Organic referrals increased 5x as users became genuinely sticky
Product Development Efficiency:
Stopped building 70% of requested features that AI predicted would hurt PMF
Development velocity increased as team focused on high-impact changes
Feature adoption rates improved 4x because we built what users actually needed
The most surprising result? We discovered that our original value proposition was completely wrong. Users weren't hiring our product for what we thought – they were using it to solve a different problem entirely. Traditional validation would never have revealed this because users couldn't articulate it in surveys.
The AI approach revealed the true job-to-be-done through behavioral analysis, allowing us to reposition the entire product around what users were actually trying to accomplish.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from implementing AI-driven PMF validation:
1. Behavioral Data Beats Stated Preferences
What people do reveals their true needs better than what they say. The say-do gap is real and massive.
2. PMF is a Pattern, Not a Feeling
True PMF shows up in specific behavioral patterns that can be measured and predicted, not just in satisfaction scores.
3. Small Signals Predict Big Outcomes
Early user behaviors are incredibly predictive of long-term success if you know what to look for.
4. Feature Requests Can Mislead
Users often request solutions to symptoms rather than root causes. AI helped us identify the underlying problems.
5. Timing Matters More Than Features
When users perform certain actions matters as much as whether they perform them at all.
6. PMF is Dynamic, Not Binary
Product-market fit isn't a destination – it's an ongoing process of staying aligned with evolving user needs.
7. Speed of Learning Beats Speed of Building
The faster you can validate assumptions, the faster you reach true PMF. AI accelerates learning cycles dramatically.
When this approach works best: B2B SaaS products with complex user journeys, products in rapidly evolving markets, and situations where user behavior is more complex than user surveys can capture.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, focus on:
Track user behavior patterns from day one
Build predictive models for user success
Validate features against behavioral PMF metrics
Use AI to identify your actual value proposition
For your Ecommerce store
For ecommerce stores, implement:
AI-powered customer journey analysis
Behavioral segmentation for personalization
Predictive models for repeat purchase behavior
Dynamic product-market fit scoring by category