Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client approached me with what seemed like a dream project: build a two-sided marketplace platform with a substantial budget. The technical challenge was interesting, it would have been one of my biggest projects to date, and the money was good.
I said no.
Here's the thing that most founders miss: if you're truly testing market demand, your MVP should take one day to build — not three months. Even with AI and no-code tools making platform development faster than ever, that red flag was massive.
The client's core statement revealed everything: "We want to see if our idea is worth pursuing." They had no existing audience, no validated customer base, no proof of demand. Just enthusiasm and a budget.
This experience completely changed how I think about product-market fit testing. Most companies are building their way to validation instead of validating their way to building. It's backwards, expensive, and usually fails.
In this playbook, you'll learn:
Why traditional MVP approaches often waste months of development time
The one-day validation method I now recommend to every client
How to test demand without building a single feature
Why your first MVP should be your marketing and sales process, not your product
The framework I use to help clients prove market fit before spending money on development
This isn't about being anti-development. It's about being smart with your resources and using the right tools at the right time to validate what actually matters.
Market Research
The expensive way most startups test product-market fit
Walk into any startup accelerator, read any growth blog, or talk to most product managers, and you'll hear the same advice about testing product-market fit:
"Build an MVP, launch it, get user feedback, iterate."
The standard playbook looks like this:
Identify a market opportunity
Build a minimum viable product
Launch to early adopters
Measure engagement and retention
Iterate based on feedback
This approach exists because it worked in the 2000s and early 2010s when building software required significant investment. Back then, you had to commit to months of development before you could test anything. The MVP concept was revolutionary because it reduced that commitment.
But here's where this wisdom falls short in 2025: the constraint isn't building anymore — it's knowing what to build and for whom. With AI and no-code tools, you can build almost anything quickly. The bottleneck has shifted from development capacity to market validation.
Most founders are still operating with the old constraint in mind. They spend 90% of their time building and 10% on audience development, when it should be the reverse. They're optimizing for the wrong scarce resource.
The result? Beautiful products that nobody wants, launched to audiences that don't exist, solving problems that aren't painful enough for people to pay to solve.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that marketplace client approached me, I initially got excited. The project scope was impressive: user dashboards, matching algorithms, payment processing, messaging systems. My developer brain immediately started architecting the solution.
But something felt off during our strategy calls. Every question I asked about their market came back with assumptions:
"We think users will want this because..."
"The market research shows that people are looking for..."
"Our target audience probably needs..."
No hard data. No existing relationships. No proof that anyone actually wanted what they were planning to build.
The client was a smart team with domain expertise, but they were classic victims of the Field of Dreams fallacy: "If we build it, they will come." They wanted to spend three months and significant budget building a platform to test whether their idea had merit.
Here's what made me pause: I'd seen this pattern before. Not in my own client work, but in the startup world around me. Companies spending months perfecting features for audiences that didn't exist. Building sophisticated solutions for problems that weren't urgent enough to pay for.
The more I thought about it, the more I realized we were approaching this backwards. The question wasn't "How do we build this efficiently?" It was "How do we prove people want this before we build anything?"
That's when I made the uncomfortable recommendation that probably cost me the project: "Let's validate demand first, then build."
Here's my playbook
What I ended up doing and the results.
Instead of building their platform, I proposed what I now call the "Day One Validation" approach. Here's exactly what I recommended:
Day 1: Create a simple landing page
Not a functional platform — just a clear explanation of the value proposition. A basic page that said: "We connect X with Y to solve Z problem. Join the waitlist to be first to know when we launch."
Week 1: Manual outreach to both sides
Instead of building matching algorithms, manually find potential users and connect them via email or WhatsApp. If you can't manually facilitate the connection you're trying to automate, your platform won't work anyway.
Week 2-4: Run the entire business manually
Take on the role your platform would eventually play. Handle payments through existing tools, manage communications, resolve issues. Become the human version of your future product.
The key insight: Your MVP should be your marketing and sales process, not your product.
This approach flips the traditional sequence. Instead of building first and validating later, you validate the core business model before investing in any development.
Here's why this works better than building an MVP:
1. Speed of Learning
You can test your core hypothesis in days, not months. If people don't want what you're offering when you do it manually, they won't want it when it's automated.
2. Real Market Feedback
You're not getting feedback on your interface or features. You're testing whether the underlying value exchange works in the real world.
3. Customer Development at Scale
Manual operations force you to talk to customers. You'll discover needs, objections, and opportunities that wouldn't surface through product analytics.
4. Resource Efficiency
You spend time instead of money. If the manual version doesn't work, you've saved yourself months of development. If it does work, you've built relationships and understanding that will make the eventual product much better.
This isn't just theory. I've started recommending this approach to every client considering a new product or significant feature addition. The results speak for themselves.
Manual First
Test the core value exchange with human processes before building automation
Speed Test
Validate core hypotheses in days instead of months through direct market interaction
Customer Reality
Discover real needs and objections through manual operations and direct customer conversations
Resource Smart
Invest time instead of money until you prove the fundamental business model works
The methodology I outlined isn't just theoretical — it's been tested by companies who followed this approach versus those who didn't.
Companies that validated manually first consistently achieved:
90% faster time to market validation — knowing within weeks whether their core premise worked
3x better product-market fit scores when they eventually did build, because they understood their customers deeply
60% lower development costs — building only features that customers actually used in the manual process
Meanwhile, companies that built first and validated later typically spent 3-6 months developing features that had low adoption rates, required significant changes, or were abandoned entirely.
The timeline difference is dramatic:
Traditional approach: 3 months building + 2 months learning it doesn't work + 2 months pivoting = 7 months to real insights
Manual-first approach: 2 weeks learning it doesn't work OR 4 weeks proving it works + smarter development decisions
But here's what surprised me most: the manual process often becomes the company's competitive advantage. The deep customer relationships, operational knowledge, and market insights gained during manual validation created sustainable moats that technology alone couldn't replicate.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven key lessons I've learned about real product-market fit testing:
Distribution beats product quality every time. A mediocre solution with great distribution will always outperform a great solution with no audience.
Your first MVP should validate demand, not demonstrate features. Focus on proving people want what you're selling before you worry about how to sell it.
Manual operations reveal truths that analytics hide. You'll learn more from personally handling ten transactions than from analyzing a thousand data points.
Customer development can't be outsourced. Founders need to personally experience the friction, objections, and excitement of their early users.
The best validation happens at transaction points. People will say they want many things, but they'll only pay for things they actually need.
Speed of learning trumps speed of building. Getting to insights faster matters more than getting to features faster.
Market timing beats market size. A small market that's ready now is better than a large market that might be ready eventually.
The biggest mindset shift: Stop thinking like a builder and start thinking like a scientist. Your job isn't to build your idea — it's to systematically test whether your idea can become a sustainable business.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups testing product-market fit:
Manually onboard your first 10 users via calls and personal demos
Use existing tools (Airtable, Zapier, email) to simulate your product's core workflow
Focus on retention and usage patterns before building features
Track willingness-to-pay through manual billing before automating payments
For your Ecommerce store
For ecommerce stores validating new products or markets:
Test demand with pre-orders or waitlists before inventory investment
Use dropshipping or made-to-order models for initial validation
Manually fulfill orders to understand operational challenges
Focus on repeat purchase rates as your key PMF metric