Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I watched a potential client approach me with what they called their "successful MVP." They had 10,000 signups, impressive engagement metrics, and a beautiful dashboard full of green numbers. The problem? Zero revenue and users who disappeared after day one.
This isn't uncommon. Most founders get lost in vanity metrics because that's what every MVP guide tells you to track. User acquisition, session duration, feature adoption rates—all impressive numbers that make you feel good about your progress but tell you nothing about whether you're building something people actually want to pay for.
After working with dozens of startups and building my own MVPs, I've learned that most traditional MVP metrics are actually counterproductive. They create a false sense of progress while hiding the real indicators of product-market fit.
Here's what you'll learn from my framework:
Why engagement metrics can be completely misleading for MVP validation
The 3 metrics that actually predict MVP success (and they're not what you think)
My "lovability test" that reveals if users truly care about your product
How to measure product-market fit before building advanced features
The counterintuitive approach that helped me avoid the "feature trap"
If you're tired of vanity metrics that don't translate to real business success, this playbook will change how you think about MVP validation. Building your MVP is just the first step—measuring the right things is what separates successful products from expensive experiments.
Industry Reality
What every startup founder has been told about MVP metrics
Walk into any startup accelerator or read any product management blog, and you'll hear the same advice about MVP metrics. Track everything, measure engagement, optimize for retention, and watch your daily active users climb. The conventional wisdom sounds logical:
User Acquisition: More signups mean more potential customers
Engagement Metrics: Time spent in app shows product value
Feature Adoption: Which features users interact with most
Retention Curves: Day 1, 7, 30 retention rates
Session Duration: Longer sessions indicate higher engagement
This framework exists because it's borrowed from established tech companies with millions of users. When Facebook tracks engagement, it makes sense—they have a proven business model and need to optimize existing revenue streams. When Google measures session duration, they're fine-tuning an advertising machine that already works.
But here's the problem: your MVP isn't Facebook or Google. You're not optimizing an existing business model—you're trying to discover if one exists at all. Traditional metrics assume you already know what success looks like, but with an MVP, that's exactly what you're trying to figure out.
The conventional approach creates what I call "engagement theater." You build features that increase session time, improve retention curves, and boost user activity. But none of this tells you if people would actually pay for your product or recommend it to others. Worse, it often leads you away from building something truly valuable because you're optimizing for the wrong signals.
Most MVP failures aren't due to poor execution or bad marketing—they're due to measuring the wrong things and optimizing for metrics that don't correlate with business success.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I learned this lesson the hard way when working with a client who had built what looked like a successful AI-powered project management tool. Their metrics were impressive: 2,000 beta users, 40% daily active user rate, and average session times of 15 minutes. Every traditional MVP guide would have told them they had a winner.
But something felt off. During our strategy sessions, I noticed they kept talking about engagement numbers but never mentioned user feedback or willingness to pay. When I dug deeper, the reality was stark: users were trying the tool, spending time figuring it out, but then abandoning it completely.
The high engagement wasn't a sign of love—it was confusion. People were spending 15 minutes trying to understand how the tool worked, then giving up. The 40% DAU rate was actually people coming back to give it "one more try" before churning permanently.
We ran a simple experiment: asked 50 active users if they'd pay $29/month for the tool. Only 3 said yes. Asked if they'd recommend it to a colleague? 8 out of 50. Asked what they'd do if the tool disappeared tomorrow? 47 out of 50 said "find something else" or "probably nothing."
This disconnect between engagement metrics and actual user sentiment taught me that traditional MVP metrics are often lagging indicators at best, and completely misleading at worst. High engagement can indicate confusion just as easily as satisfaction. Retention can mean habit rather than value.
That's when I realized we needed a completely different approach to measuring MVP success—one focused on leading indicators of genuine product-market fit rather than vanity metrics that look good in investor presentations.
Here's my playbook
What I ended up doing and the results.
After that wake-up call, I developed what I call the "Lovability Framework" for MVP metrics. Instead of measuring what users do, I focus on measuring how they feel and what they're willing to sacrifice for your product. Here's the system that actually predicts MVP success:
The Three Core Metrics That Matter:
1. Willingness to Pay (WTP) Score
This isn't about actual payments—it's about intent. I survey users with: "If this cost $X/month, would you pay for it?" The magic number is 40% saying yes at a reasonable price point. Below 40%, you're building a nice-to-have. Above 40%, you've found something people truly value. I test this within the first 100 users, not after thousands of signups.
2. Recommendation Intensity
Standard NPS asks "Would you recommend this?" I ask "Who specifically would you recommend this to, and when would you tell them?" If users can't name specific people and situations, the product isn't solving a real problem. Genuine love creates specific, immediate recommendation scenarios.
3. Alternative Behavior Test
I ask: "What were you doing to solve this problem before our tool?" and "What would you do if our tool disappeared tomorrow?" If the answer is "nothing" or "I'd be fine," you're not solving a critical problem. The best MVPs make people say "I'd have to find something else immediately" or "I'd go back to that terrible manual process."
The Supporting Metrics Framework:
Organic Usage Patterns: Do people use your product without prompts, reminders, or incentives? I track "unprompted returns"—sessions that happen without any notification or email trigger. If most usage requires prompting, you don't have a habit-forming product.
Feature Depth vs. Breadth: Instead of tracking which features get used most, I measure how deeply users engage with core functionality. Shallow usage of many features indicates confusion; deep usage of few features indicates value discovery.
Time to Value (TTV) Reality Check: I measure time from signup to first "aha moment"—but I define that moment through user feedback, not behavioral proxies. If users can't articulate what value they received and when, you haven't delivered value regardless of what your metrics say.
The key insight: lovable MVPs create emotional responses, not just behavioral patterns. A user who spends 2 minutes accomplishing something important is infinitely more valuable than one who spends 20 minutes exploring features they'll never use again.
Emotional Signals
Track feelings and willingness to sacrifice, not just actions. High engagement can mean confusion—focus on user sentiment instead.
Critical Questions
Ask specific recommendation scenarios and alternative behaviors. Vague positivity isn't enough—you need concrete evidence of value.
Value Discovery
Measure depth of core feature usage, not breadth. Users who go deep on key features show true product-market fit signs.
Reality Checks
Survey early and often about payment willingness. Don't wait for thousands of users—test lovability with your first 100.
The framework revealed brutal but valuable truths. Of the 12 MVPs I've evaluated using this system, only 3 showed genuine lovability signals early on. Those 3 went on to raise funding and achieve sustainable growth. The other 9 pivoted or shut down within 6 months.
The most dramatic example was a productivity app that looked successful by traditional metrics—5,000 users, 35% weekly retention, 8-minute average sessions. But our lovability tests revealed only 12% willingness to pay and zero specific recommendation scenarios. The founder pivoted to a B2B model and found product-market fit within 4 months by focusing on business value rather than consumer engagement.
Another startup had concerning traditional metrics—only 800 users, 15% daily active usage, 3-minute sessions. But 67% said they'd pay $50/month, and users gave specific scenarios about recommending it to teammates. They focused on converting those truly engaged users and built a sustainable business with a small but passionate user base.
The timeline matters too. Traditional metrics often show positive trends for months before revealing their true nature. Lovability metrics give you clear signals within 2-4 weeks of user exposure. This speed advantage prevents months of building in the wrong direction.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson: fall in love with the problem, not your solution. When you measure the right things, you discover whether you're solving a real problem or just building an elegant solution to something nobody cares about. Most founders are optimizing for metrics that make them feel good rather than indicators that predict success.
Measure willingness to sacrifice before measuring willingness to engage—time, money, and effort are better indicators than clicks and sessions
Early lovability signals are more predictive than late engagement trends—test with 100 users, not 10,000
Emotional responses matter more than behavioral patterns—frustrated users often show high engagement while searching for alternatives
Specific beats general in user feedback—"I love it" means nothing; "I'd recommend this to my manager for our Q4 planning" means everything
Build measurement into your MVP from day one—don't retrofit metrics after you've already formed assumptions about success
Question every "positive" metric—high retention could mean confusion, long sessions could indicate poor UX, and lots of features tried could signal lack of core value
Focus on leading indicators of business model viability—engagement without willingness to pay is just expensive entertainment
The hardest part isn't building the measurements—it's being honest about what they reveal and pivoting when they suggest your assumptions are wrong.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building MVPs:
Test payment willingness within your first 100 signups
Focus on B2B use cases where value is easier to quantify
Measure "unprompted returns" rather than total engagement
Ask specific recommendation scenarios during user interviews
For your Ecommerce store
For ecommerce businesses testing new products:
Track repeat purchase intent, not just initial conversion
Measure specific gift/sharing scenarios as proxy for love
Focus on customer lifetime value predictors early
Test premium pricing willingness before scaling production