Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I had one of those moments that defines your entire approach to business. A potential client approached me with a substantial budget to build a two-sided marketplace platform. The numbers were impressive, the technical challenge was exciting, and honestly, it would have been one of my biggest projects to date.
I said no.
Not because I couldn't do it, but because they had it completely backwards. They wanted to spend months building before they knew if anyone actually wanted what they were creating. This is the trap I see everywhere - founders rushing to scale before they've validated anything.
Here's the uncomfortable truth: if you're truly testing market demand, your MVP should take one day to build, not three months. Every successful business I've worked with started with brutally non-scaleable tactics that taught them what actually mattered.
In this playbook, you'll discover:
Why the most successful SaaS companies I've worked with started manually
The exact framework I use to validate ideas before building anything
How non-scaleable tactics revealed billion-dollar insights for my clients
When to transition from manual processes to automated systems
The counterintuitive reason why manual work creates better products
Real Talk
What every startup accelerator tells you
Every startup guru preaches the same gospel: build for scale from day one. The advice sounds logical - why waste time on things that won't work when you have millions of users?
Here's what they typically recommend:
Build your MVP with scaleable architecture - Use cloud infrastructure, microservices, and robust databases from the start
Automate everything immediately - Why do manually what software can do automatically?
Focus on user acquisition metrics - Get to 10,000 users as fast as possible to validate product-market fit
Raise money to hire talent - Scale your team to match your ambitious timeline
Launch with full feature sets - Give users everything they might want from day one
This advice exists because VCs and accelerators are playing a numbers game. They need companies that can scale to billion-dollar valuations quickly. The pressure to show exponential growth from day one creates this obsession with scaleable solutions.
But here's the problem: scaleable solutions optimize for efficiency, not learning. When everything is automated and optimized, you lose the messy, human insights that actually matter. You end up building something perfectly scaleable that nobody wants.
The real challenge isn't scaling - it's figuring out what's worth scaling in the first place.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The marketplace client I mentioned came to me after reading about the success of no-code tools and AI platforms. They'd heard you could build anything quickly and cheaply now. Technically, they weren't wrong - we absolutely could have built their two-sided platform.
But when they explained their core motivation - "We want to see if our idea is worth pursuing" - that's when I knew we had to pump the brakes.
They had zero existing audience, no validated customer base, no proof of demand. Just enthusiasm and a business plan. Sound familiar?
I've been in this exact situation multiple times. Early in my freelance career, I would have taken the project and built exactly what they asked for. Beautiful marketplace, perfect user flows, scaleable infrastructure. And then... crickets. No users, no traction, no revenue.
The pattern became obvious after my third "successful" project launch that failed to gain users. I was optimizing for the wrong thing. I was building scaleable solutions before anyone knew what problem we were actually solving.
The breaking point came with an e-commerce client who wanted a complex recommendation engine. We spent two months building sophisticated algorithms to suggest products. The system worked perfectly. But when we analyzed user behavior, we discovered something embarrassing: customers were ignoring the recommendations entirely. They were using the search bar and browsing categories manually.
All that "scaleable" technology was irrelevant because we hadn't validated the basic assumption that customers wanted AI-powered recommendations in the first place.
That's when I started asking the uncomfortable question: What if we tested this manually first?
Here's my playbook
What I ended up doing and the results.
Instead of building the marketplace platform, I walked my client through what I now call the "One-Day MVP Framework." Here's exactly what we did:
Day 1: The Landing Page Test
We created a simple landing page explaining their value proposition. No complex platform, no user accounts, just a clear description of what they wanted to build. We added a "Join Waitlist" button and drove traffic through their existing networks.
Result: 847 email signups in the first week. But more importantly, the comments revealed that people wanted something completely different than what they'd planned to build.
Week 1: Manual Matchmaking
Instead of building automated matching algorithms, we had them manually connect supply and demand via email and WhatsApp. Every transaction was handled personally. This sounds insane from a scalability perspective, but it was pure gold for learning.
We discovered:
The pricing model was wrong - customers wanted project-based fees, not subscription
The main value wasn't matching - it was trust and verification
Users needed way more hand-holding than we anticipated
Month 1: The Concierge Service
We positioned the manual process as a premium "concierge" service. Customers paid for the personal attention. This immediately solved two problems: revenue validation and deep customer research.
Every interaction taught us something about what the actual platform needed to do. We kept detailed notes on every pain point, every requested feature, every moment of friction.
Month 2: Selective Automation
Only after proving demand manually did we start building automation. But now we knew exactly what to automate and why. We weren't guessing - we were solving proven problems.
The beauty of this approach? Our first automated features had 90%+ adoption rates because they solved real, validated problems.
Validation Method
Start with landing pages and waitlists before building anything. Let demand prove itself through real sign-ups and pre-orders.
Manual Operations
Handle core processes manually first. Use email, spreadsheets, and personal communication to understand what automation actually needs to do.
Customer Discovery
Every manual interaction is a research opportunity. Document pain points, feature requests, and unexpected use cases.
Revenue First
Charge for manual services to validate willingness to pay while funding your learning process.
The results were dramatically different from traditional "build first" approaches:
Speed to Validation: 1 week vs 6 months
Traditional MVP development would have taken 6 months to get real user feedback. Our manual approach provided clear market signals in 7 days.
Cost to Validate: $200 vs $50,000
The manual validation cost the price of a landing page and some email tools. Building the full platform first would have cost tens of thousands.
Learning Quality: Deep vs Surface
Manual processes forced direct customer interaction at every step. We learned not just what features people used, but why they used them and how they actually worked.
Pivot Speed: Days vs Months
When manual testing revealed new insights, we could pivot immediately. No complex code to rewrite, no architectural decisions to undo.
Most importantly: the client ended up building a completely different product than originally planned. The manual validation revealed that their initial idea was solving the wrong problem. Without non-scaleable tactics, they would have spent months building something nobody wanted.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from focusing on non-scaleable tactics first:
Manual processes reveal hidden assumptions - Every automation hides decision points that you need to understand first
Customer intimacy beats efficiency - Close customer contact teaches you things surveys and analytics never will
Revenue validation trumps usage metrics - People signing up for free is different from people paying money
Constraints force creativity - Limited resources make you focus on what actually matters
Manual scaling teaches automation priorities - You only automate what's proven to matter
Speed to learning beats speed to scale - Fast validation prevents expensive mistakes
Non-scaleable can be profitable - Manual services often have better margins than automated ones
The biggest mistake I see founders make is treating non-scaleable tactics as temporary embarrassments rather than strategic advantages. Manual processes aren't bugs in your business model - they're features.
When you eventually do automate, you'll build exactly what customers need, not what you think they need.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups specifically:
Start with manual onboarding calls before building automated flows
Use personal demos instead of self-service trials initially
Handle customer support personally to understand real pain points
Manually curate content before building recommendation engines
For your Ecommerce store
For e-commerce stores:
Start with manual inventory and fulfillment to understand logistics
Use personal customer service before implementing chatbots
Manually curate product recommendations before automation
Test pricing and positioning through direct customer conversations