The tech founders guide to selling before building

If you build it, they will do absolutely nothing. - Patrick McKenzie (@patio11)

Six months of your life. Countless late nights. Thousands of lines of code.

And then... silence. No users. No sales. Just the crushing realization that you might have built something nobody wants.

If you're a technical founder, this story might hit close to home. Or maybe it's the scenario you're desperately trying to avoid.

I know what you're thinking: "But I need to build an MVP to validate my idea."

Think of it like this.

Imagine two new restaurants:

• Restaurant A spends six months perfecting their menu behind closed doors, then opens to crickets.
• Restaurant B spends that time hosting dinner parties, testing recipes, and building buzz. When they open, they’re booked out.

Which would you rather be?

The truth is, you'll need to do customer research either way. The only question is whether you want to do it before or after spending months building.

Every day in communities like r/SaaS, another developer shares the same heartbreaking experience.

We do it because we're builders at heart. We see a potential solution, and our instinct is to dive straight into coding or the idea, convinced that if we just make it good enough, users will come.

Months later, they're ready to launch. That's when reality hits:

• They don't really know who it's for
• They can't articulate what problem it solves in crisp details
• There's no one waiting to open their wallets and give you their cash

Panic sets in. They frantically go through every tactic in the book to brute force sales:

• Cold outreach that goes nowhere
• Social media posts that get ignored
• Content marketing that takes ages to distribute
• Paid ads that burn through savings
• Starts to think that SEO might be the silver bullet.

But there's a better way.

If you've followed my Reddit analysis framework from part 1, part 2, and part 3 and collected 8-12 threads showing the same problem patterns, you already have something most founders would KILL for:

• Crystal-clear understanding of your audience's pain points (validated across multiple threads)
• Detailed context of when and why these problems occur (with real examples)
• Deep understanding of failed solutions and why they're not working (backed by real attempts and consistent patterns)
• Insight into your audience's worldviews and beliefs that drive their decision making

Now it's time to turn that research into something you can test. Before writing a single line of code.

What you'll learn

In this fourth and final part of our Reddit research series, you'll discover:

• How to map customer context and pain points from Reddit research
• A framework for understanding their failed solution attempts and customer worldviews
• How to craft targeted offers for different customer segments
• Step-by-step instructions for creating effective waitlist landing pages
• Practical strategies for testing your offer using both paid and organic traffic
• Clear metrics to measure response quality and guide your focus

You'll see this framework applied to a real example: solving fake checkout spam for Shopify stores.

Buckle in. Let’s go!

Converting Reddit insights into testable offers

Let's see this framework in action by analyzing a real problem I discovered during Reddit research.

I'll demonstrate this framework using 5 analyzed threads about fake checkout spam affecting Shopify stores (though remember, you should aim for 8-12 threads for your own research as mentioned earlier). This example will show you exactly how to turn Reddit insights into testable offers.

While analyzing these threads, I noticed something interesting. There are two distinct segments experiencing the same problem differently:

• Course creators using free tutorials as lead magnets
• eCommerce stores running paid ads with guest checkout

This presents an important lesson in customer research: sometimes what looks like a single problem actually affects different segments in unique ways.

Let's break down what we learned about each segment's specific situation:

1. Mapping their context

My analysis revealed several key contexts where this problem occurs.

Business Model Constraints

"Our store allows customers to check out without registering, as we're running ads everywhere"

"I use [free tutorials] to attract customers to paid tutorials"

"Want to maximize ad ROI"

How segments differ:

• Ad buyers need guest checkout for conversion optimization
• Course creators need free products for student acquisition

Technical Environment Constraints

"Using Shopify checkout so not able to customize anything"

"[They] haven't actually completed an order, I don't have an IP address"

"Am using Shopify checkout so not able to customize anything there"

Both segments face the same platform limitations

Operational Requirements

"Need to keep an eye on abandoned carts... for shipping muckups"

"We're running ads everywhere"

"Use them to attract customers to paid tutorials"

How segments differ:

• Ad buyers need clean data for ROAS calculations
• Course creators need to track lead magnet performance

Support Situation

"Shopify support was notified but they are not doing anything"

"They told me to just rely on their fraud detection/filtering"

Both segments feel unsupported by the platform

These threads reveal our ideal customer profile and more specifically what's the situation they’re in:

• Shopify merchants whose business model depends on offering something valuable (free courses or guest checkout) without barriers.
• They're being pushed to look for a solution when their analytics become unreliable due to fake checkouts, threatening their ability to make data-driven decisions.
• What makes this moment particularly painful is the combination of wasted resources and the inability to distinguish real interest from fake engagement by spammers.

2. What's the pain?

Functional Pain:

Data Integrity:

"It's starting to really mess up our backend order statistics, making it harder to analyze real data"

"For some reason the shopify dashboard continues to show these orders in the total sales although it is auto cancelled"

How segments differ:

• Ad buyers can't trust ROAS calculations
• Course creators can't track lead magnet effectiveness

Platform Limitations:

"I am unable to block the order or ban the customer account"

"Can't get an IP addy for them"

"Not able to customise anything there [in Shopify checkout]"

Both segments face the same technical constraints

Scale of Attack:

"10–15 abandoned checkouts per hour"

"Hundreds of times since this started - anywhere from 3 to dozens of times a DAY!"

How segments differ:

• Ad buyers see spikes during campaign runs
• Course creators face constant, ongoing abuse

Emotional Pain:

Helplessness:

"For MONTHS! I've tried..."

"Shopify support was notified but they are not doing anything"

Frustration:

"Getting old... hundreds of abandoned checkouts"

"It seems like these scammers are set on using my website"

Anxiety:

"Need to keep an eye on abandoned carts... for shipping muckups"

Both segments share these emotional responses, but triggers differ:

• Ad buyers anxious about wasting ad spend
• Course creators frustrated about lead magnet abuse

Social Pain:

Looking Unprofessional:

"Okay - hopefully this will make sense..." [showing uncertainty]

Appearing Unsuccessful:

"We're running ads everywhere" [but can't show real conversion data to others]

"Making it harder to analyze real data" [can't report accurate metrics]

How segments differ:

• Ad buyers worry about reporting to stakeholders
• Course creators concerned about appearing legitimate to potential students

While both segments first mention the functional pain of messy data, it's the emotional drain of constant attacks and social pain of appearing vulnerable that drives them to act and seek solutions.

3. Learn from failed attempts

Users have tried everything available with varying success.

Platform Solutions Failed:

"Shopify support was notified but they are not doing anything"

"They told me to just rely on their fraud detection/filtering"

Both segments get the same unhelpful platform response

Existing Apps Don't Work:

"Fraud filter... doesn't stop him from adding to cart"

"Blockify... can't get an IP addy for them"

How segments differ:

• Ad buyers try fraud detection apps
• Course creators attempt blocking solutions

Manual Efforts Useless:

"Deleting the customer - all that did was remove his info from previous abandoned carts"

Both segments find manual cleanup ineffective.

Desperate Measures:

"Disabling the free tutorials - that stopped him, but not fair to me [needs it for lead generation]"

How segments differ:

• Course creators consider removing free content
• Ad buyers contemplate requiring registration

The key insight is that both segments have tried solutions that either work too late (after checkout) or create too much friction (registration/removal of free content), showing why a “pre-checkout” solution might be needed.

4. Address worldviews

The threads revealed deeply held beliefs:

About Customer Experience:

"Want to maximize ad ROI... don't want to force account registration"

"Our store allows customers to check out without registering, as we're running ads everywhere"

How segments differ:

• Ad buyers believe any friction kills conversion rates
• Course creators believe free content builds trust that leads to sales

About Data-Driven Growth:

"Need to keep an eye on abandoned carts... for shipping muckups"

"Making it harder to analyze real data" [can't make growth decisions]

"For some reason the Shopify dashboard continues to show these orders in the total sales"

How segments differ:

• Ad buyers need clean data for ROAS optimization
• Course creators need reliable lead magnet metrics

About Platform Support:

"Shopify support was notified but they are not doing anything"

"They told me to just rely on their fraud detection/filtering"

Both segments share deep skepticism about platform solutions

These worldviews explain why existing solutions fail: They either compromise the customer experience (forced registration) or mess with their metrics (post-purchase cancellation).

Any viable solution must work within these deeply held beliefs rather than challenge them (in this scenario).

In some cases, where unhelpful beliefs might be blockers to progress. It’s OK to challenge them and change their mind with evidence of the contrary.

5. Match their desired outcomes

What does success look like for these people?

Clean Data

"Need to keep an eye on abandoned carts... for shipping muckups"

"Making it harder to analyze real data"

How segments differ:

• Ad buyers want accurate ROAS metrics
• Course creators need clear lead magnet conversion data

Prevention vs. Cleanup:

"Stop him from even adding anything to the cart"

"Can't even stop basic cart spam"

Both segments want pre-emptive protection

Business Model Protection:

"Use them to attract customers to paid tutorials"

"Want to maximize ad ROI"

How segments differ:

• Course creators need to protect lead generation system
• Ad buyers need to maintain guest checkout conversion rates

Getting their Time Back:

"For MONTHS! I've tried..."

"Getting old... hundreds of abandoned checkouts"

Both segments want to stop wasting time on manual cleanup

While both segments want the same high-level outcome (clean data), their specific metrics of success differ based on their model.

This explains why a one-size-fits-all solution focused just on fraud prevention isn't enough. It needs to preserve the specific metrics each segment cares about most.

Creating your minimum testable offer(s)

Now that we understand our segments, let's create targeted offers for each. Imagine we're a Shopify developer who's noticed these patterns and wants to validate a solution.

Instead of picking a segment arbitrarily, let's test both to see which resonates more:

Course creator version

1. Lead with data-driven pain: "Stop spammers from poisoning your course analytics. Protect your lead magnets without adding friction"
2. Follow with worldview-aligned messaging:
   • "Know exactly which free tutorials convert to paid courses"
   • "Zero impact on legitimate student signups"
   • "Keep your lead magnets accessible to real students"
   • "Works silently in the background. No registration walls"
3. Build trust through understanding (the cure for not having testimonials):
   • "Built by a developer who saw course creators losing real student data to fake signups"
   • "Designed specifically for Shopify course platforms"
   • "Preserves everything you need: clean analytics, easy access, and reliable tracking"
   • "Finally see the real impact of your lead magnets"
4. Address platform skepticism:
   • "Works independently of Shopify's fraud detection"
   • "No checkout modifications needed"
   • "Automatic protection—no manual review required"
5. Offer low-risk next steps:
   • "14-day trial with guaranteed clean analytics"
   • "Priority access for course creators"
   • "Full refund if it affects student signups"

Paid traffic store version

1. Lead with data-driven pain: "Stop fake checkouts from destroying your ROAS data (without touching your checkout flow)"
2. Follow with worldview-aligned messaging:
   • "Keep your ad metrics clean and reliable"
   • "Zero impact on guest checkout conversion"
   • "Know which abandoned carts are worth retargeting"
   • "Maintain frictionless checkout for ad traffic"
3. Build trust through understanding:
   • "Built by a developer who watched ad spend get wasted on fake abandoned carts"
   • "Designed specifically for Shopify stores running paid traffic"
   • "Preserves everything you need: guest checkout, clean data, and accurate ROAS"
   • "Finally retarget real potential customers, not bots"
4. Address platform skepticism:
   • "Works with any ad platform"
   • "No checkout modifications required"
   • "Set and forget—no rules to configure"
5. Offer low-risk next steps:
   • "14-day trial with clean ROAS tracking"
   • "Priority access for stores with active ad campaigns"
   • "Full refund if it affects conversion rates"

Testing your offers with landing pages

With our targeted offers crafted for each segment, we need to validate which resonates most strongly. While our Reddit research revealed these distinct needs, real-world testing will show us where to focus first.

For this validation phase, create two focused landing pages:

• Course creator version: Focus on lead magnet protection, using the exact pain points and language we found in their discussions
• Paid traffic store version: Emphasize ROAS accuracy, speaking directly to the worldviews and concerns we uncovered

You'll use these landing pages for both organic and paid traffic approaches, which we'll cover next.

Elements of an Effective Waitlist Landing Page

1. Problem (Hero):

• Headline using their exact painful language
• Sub-headline showing you know their struggle
• Simple email capture for waitlist

2. Agitation (Problem Section):

• "You're not alone" (show Reddit evidence)
• Paint the painful scenario:
  • Wasted ad spend/lead magnets
  • Messy analytics
  • Time lost on cleanup
• Highlight what happens if this continues:
  • Wrong business decisions
  • More wasted resources
  • Growing frustration

3. Solution (Preview):

• Show the opposite scenario
• Focus on their desired outcomes
• Early access benefit (if any)

4. Waitlist-Specific Elements:

• Single field email form
• "X people waiting" social proof (optional)
• No pricing discussions yet, you can figure this out afterwards

5. Trust Building:

• Use the same language from your research
• Show you've tried existing solutions
• Reference specific tools they know and why they suck

6. What to Avoid:

• Multiple calls to action
• Complex signup forms
• Feature promises
• Launch dates (unless certain)
• Pricing details
• Over-selling the solution or focusing on WE/ME too much

Driving traffic to your waitlist

Now you have two targeted landing pages. But a landing page without traffic is like a store in the middle of nowhere. It doesn't matter how perfect your message is if no one sees it.

You have two main paths to get your first validation: paid and organic traffic.

Each has its tradeoffs.

The Fast Path: Paid Traffic

If you have some budget (even $100-200), paid traffic can give you quick validation. Think of it as paying to compress months of organic growth into a few days.

This is how Charle’s Burdett validated Workshop Tactics.

Why consider paid traffic

Pros:

• Fast feedback on messaging (24-48 hours)
• Easy to test different headlines and angles
• Clear data on what resonates (CTR, conversion rates)
• Can quickly pivot if one segment responds better
• Control over targeting and exposure

Cons:

• Requires upfront budget
• May need to educate cold traffic about the problem
• Higher customer acquisition cost initially (treat it as the cost of learning)
• Risk of attracting price shoppers vs. problem-aware customers

How to do it:

1. Run small, targeted ads to your landing pages:
   • Course Creators:
     • Facebook groups for course creation
     • Reddit ads in education/course subreddits
     • LinkedIn targeting course creators
   • Paid Traffic Stores:
     • Facebook groups for e-commerce advertising
     • Reddit ads in PPC/ecommerce subreddits
     • LinkedIn targeting e-commerce marketers
2. Test with minimal budget:
   • Start with a comfortable budget per day for each segment (depending on your scenario)
   • Test different pain point headlines using the research you already have
   • Track which messages get highest CTR

The Slow (But Sustainable) Path: Organic Traffic

If you're bootstrapping or prefer building genuine relationships, organic traffic might be your path. It takes longer, but often leads to deeper insights and more committed early users.

Important: When I say "organic traffic" here, I don't mean SEO or waiting for Google. I mean actively participating in communities where your potential customers already discuss these problems.

Why consider organic traffic

Pros:

• Attracts problem-aware customers
• Builds authority through helping
• Creates valuable content you can reuse
• Lower customer acquisition cost
• More authentic relationships

Cons:

• Takes longer to get meaningful data (2-4 weeks minimum)
• Harder to test multiple messages quickly
• Requires consistent time investment
• Can't control when your audience sees your message
• You need to balance helping vs. promoting

How to do it:

1. Find where your segments already hang out:
   • Course creators: Reddit (r/shopify, r/coursemakers), Facebook groups for course creation
   • Paid traffic stores: Reddit (r/PPC, r/ecommerce), Facebook ads groups
2. Build authority through helping:
   • Share insights from your research
   • Answer questions about fake order problems
   • Post detailed breakdowns of common solutions' limitations
3. Test messaging naturally:
   • Drop your landing page or direct message when relevant (don’t be spammy)
   • Note which pain points get clicks
   • Track which segment engages more

Personally, if you have the money and time I’d use a mix of both.

Tracking response metrics

Measure response quality:

• Which landing page gets more time on page?
• Who fills out the email sign-up form?
• Which group shows urgency to solve the problem?
• Where are signups coming from?

Let data guide your focus:

• Landing page conversion rates
• Quality of conversations with sign ups
• Cost to reach each segment

Even though our research suggests both segments have strong pain points, let their response to our landing pages tell us which one to focus on first. Research gives us direction, but real-world validation confirms where to invest our time.

When to start building your MVP?

The right time to start building is when you have:

1. Clear validation from your landing page tests (aim for 10-20 highly engaged signups)
2. Consistent patterns in the feedback and pain points described
3. Potential customers actively asking when they can try your solution
4. Enough understanding of the problem to scope a focused MVP

While building, don't go silent. Keep nurturing your waitlist with progress updates, additional insights, and opportunities for feedback. This maintains momentum and ensures you'll have engaged users ready for launch.

Don't wait for hundreds of signups. What matters more is the quality of engagement and consistency of the problem description across your early supporters.

Remember: Your goal isn't to build the perfect solution right away. It's to create something focused enough to solve the core pain point for your most engaged early adopters.

Your Sell First checklist

1. Map your customer research:
   1. Use part 3 for this.
2. Create targeted landing pages:
   • Write headlines using their exact painful language
   • Include "you're not alone" evidence from research
   • Show deep understanding of their situation
   • Showcase the solution
   • Add simple email capture for waitlist
3. Drive targeted traffic:
   1. Paid approach:
      • Set up small, targeted ads ($100-200 budget)
      • Test different pain point headlines
      • Track CTR and conversion rates
   2. Organic approach:
      • Participate in relevant communities
      • Share helpful insights from your research
      • Answer related questions
      • Drop your landing page when relevant
4. Track response quality:
   • Aim for 10-20 highly engaged signups
   • Measure time on page
   • Monitor conversion rates
   • Evaluate quality of conversations
   • Document specific feedback patterns
5. Analyze and iterate:
   • Identify which segment responds better
   • Refine messaging based on feedback
   • Adjust traffic strategy as needed
   • Document specific problem patterns
   • Prepare for MVP scoping

Next steps

Don't wait for MVP perfection. Here's why:

• Every day you spend building without validation is a day you could be learning from real customers
• The longer you wait, the more attached you'll become to your current vision
• The market isn't waiting - customer problems need solving now

Your next step is simple: Take one hour today to find and analyse 8-12 Reddit threads in your target market. Then use the template above to create a basic landing page tomorrow.

The worst case? You spend a few days learning your idea needs adjustment. The best case? You'll have paying customers waiting before you write a single line of code.

Which outcome would you rather have three months from now?