Sales & Conversion
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
I once watched a client spend two weeks obsessing over whether every heading on their landing page should start with a verb. Two weeks. While competitors were launching new features and capturing market share, this team was stuck in grammatical paralysis.
This wasn't an isolated incident. Throughout my career building landing pages for SaaS and ecommerce businesses, I've seen this pattern repeatedly: teams focusing on the wrong priorities while their conversion rates stagnate.
Most A/B testing advice you'll find focuses on button colors, headline tweaks, and micro-optimizations that barely move the needle. But here's what I learned after running hundreds of tests across different industries: the biggest wins come from testing assumptions everyone takes for granted.
In this playbook, you'll discover:
Why most A/B testing frameworks fail to deliver meaningful results
The counter-intuitive approach that doubled our conversion rates
How to identify which elements actually impact user behavior
A systematic framework for running tests that matter
When to ignore statistical significance and trust your gut
If you're tired of running tests that change nothing, this is your roadmap to conversion rate optimization that actually works.
Industry Wisdom
What every marketer has already heard
Open any A/B testing guide and you'll find the same recycled advice. Test your button colors. Try different headlines. Optimize your call-to-action copy. The industry has created this methodical, "scientific" approach to testing that sounds impressive but rarely delivers transformational results.
Here's what the conventional wisdom tells you to test:
Headlines and subheadlines - Small variations in wording to see what resonates
Button colors and text - The famous "red vs green" button debate
Form fields - Reducing the number of fields to decrease friction
Images and visual elements - Testing different hero images or graphics
Page layout - Moving elements around to find the "optimal" arrangement
The industry loves this approach because it feels scientific and controlled. You can run statistical significance tests, measure confidence intervals, and present clean data to stakeholders. It's methodical, predictable, and completely misses the point.
This conventional wisdom exists because it's safe. Testing button colors won't break your site or confuse your users. It's low-risk, low-controversy testing that everyone can agree on. Plus, it's easy to measure and explain to management.
But here's where it falls short: you're optimizing tactics while ignoring strategy. You're rearranging deck chairs on the Titanic while the ship is still heading toward the iceberg. Most landing page problems aren't about button colors - they're about fundamental misalignment between your message and your market.
The conventional approach treats symptoms while ignoring the disease. And that's exactly why most A/B tests produce marginal improvements that don't compound into meaningful business growth.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
A few years ago, I was working with a SaaS client whose conversion rates were stuck at 2.1%. They'd been running A/B tests for months - button colors, headlines, form layouts - all the textbook stuff. Nothing was moving the needle.
Their landing page looked professional. Clean design, clear value proposition, social proof in all the right places. On paper, it should have been converting. But the numbers told a different story.
The CEO was frustrated. "We've tried everything," he said during our initial call. "We've tested every element on the page multiple times. Maybe our product just doesn't convert well online."
I dug into their analytics and found something interesting. The page had decent traffic and low bounce rates, but users weren't taking action. They were reading, scrolling, even spending time on the page - but not converting. This told me the issue wasn't attention or interest. It was something deeper.
Here's what I discovered: they were solving the wrong problem entirely. Their landing page was perfectly optimized for selling their product's features, but their market didn't care about features. They cared about outcomes.
The client had built their entire testing strategy around the assumption that better product messaging would improve conversions. But their real problem was that they were talking to the wrong audience segment with the wrong promise.
This realization changed everything about how I approach A/B testing. Instead of testing variations of the same broken approach, I started testing completely different approaches. Not "should this button be blue or green" but "should we even have this button here at all."
The breakthrough came when we realized their highest-converting traffic wasn't coming from their primary target audience. It was coming from a completely different user type that they'd barely considered. Once we started designing tests around this insight, everything changed.
Here's my playbook
What I ended up doing and the results.
Instead of following the conventional A/B testing playbook, I developed what I call the "Assumption Audit" approach. Rather than testing variations of what exists, you systematically test the fundamental assumptions underlying your current approach.
Here's the framework I used with this client:
Step 1: Identify Core Assumptions
First, I listed every assumption built into their current landing page:
Users want to understand all product features before buying
Social proof should be testimonials from happy customers
The signup form should be above the fold
People need to see pricing to make a decision
The value proposition should focus on what the product does
Step 2: Research Alternative Approaches
I looked at completely different industries for inspiration. Instead of studying other SaaS landing pages, I analyzed pages from consumer brands, services, and even offline sales processes. The goal was to find radically different approaches to the same conversion challenge.
Step 3: Design Contrarian Tests
Rather than testing minor variations, I created versions that challenged core assumptions:
Test 1: Problem-First vs Product-First
Original page led with product features. Test version led with the customer's problem and emotional pain points. No product mention until halfway down the page.
Test 2: Story vs Specifications
Instead of bullet points listing features, I told a story about a customer's journey from problem to solution. Made it feel more like a case study than a product page.
Test 3: Single Focus vs Everything
Stripped away everything except one core message and one action. No secondary CTAs, no feature lists, no "learn more" options. Just one clear path forward.
Step 4: Test Completely Different Value Propositions
This was the big one. Instead of optimizing their existing value prop, I tested three completely different positioning approaches targeting different user types and pain points. Each version spoke to a different segment as if they were the primary audience.
The problem-first approach increased conversions by 40%. The story-based version did even better at 65% improvement. But the real breakthrough came from the repositioning tests - one version targeting a different user segment converted at 127% higher than the original.
This wasn't about better copywriting or design. It was about testing whether we were solving the right problem for the right people in the first place.
Assumption Audit
Question every foundational belief about your current approach before testing surface-level variations.
Contrarian Testing
Look outside your industry for radically different approaches to the same conversion challenge.
Problem-First Framework
Lead with emotional pain points rather than product features to create deeper user engagement.
Segment Repositioning
Test completely different value propositions targeting alternative user segments within your market.
The results were transformational, not incremental. After implementing the winning variation, we saw:
Immediate Impact:
Conversion rate jumped from 2.1% to 4.8% within the first week of implementing the winning variation. This wasn't a gradual improvement - it was an immediate step-function change in performance.
Sustained Performance:
Unlike typical A/B test winners that often regress to the mean, this improvement held steady over six months. The new approach had found genuine product-market fit at the messaging level.
Downstream Effects:
The improved conversion rate meant higher quality leads were entering their funnel. Sales qualified lead rate increased by 23% because the new messaging attracted users with stronger intent and better fit.
Team Mindset Shift:
Most importantly, the success changed how the entire team thought about optimization. Instead of asking "how can we improve this page," they started asking "should we be building this page at all."
But here's what really surprised us: the winning variation wasn't the most polished or "best designed." It was the one that most accurately reflected how their actual customers talked about and thought about the problem. Sometimes the best optimization is just being more honest about what you're actually selling.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After running this type of testing across dozens of clients, here are the key lessons that apply universally:
Test Assumptions, Not Variations - Most A/B tests optimize the wrong things. Before testing different headlines, test whether you need that headline at all.
Look Outside Your Industry - The best insights come from studying completely different approaches to similar problems. Don't just benchmark against competitors.
Start With Your Worst Assumption - Identify the biggest assumption underlying your current approach and test the opposite first. This is where you'll find the biggest wins.
Ignore Statistical Significance Sometimes - If you see a 3x improvement after 100 visitors, you don't need to wait for 95% confidence. Some signals are strong enough to trust immediately.
Test Different Problems, Not Different Solutions - Instead of testing different ways to solve the same problem, test whether you're solving the right problem entirely.
Measure Leading Indicators - Don't just track conversions. Track engagement depth, time on page, and scroll behavior to understand why tests win or lose.
Document Everything - Keep detailed notes about not just what you tested, but why you tested it and what assumptions you were challenging. These insights compound over time.
The biggest lesson? Most conversion problems aren't conversion problems - they're product-market fit problems. Your landing page can't convince people to want something they don't actually want. But it can help you discover what they do want and position accordingly.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS teams implementing this approach:
Focus on testing different user segments and use cases rather than features
Test freemium vs trial vs demo as fundamentally different value propositions
Challenge assumptions about technical vs business benefits in your messaging
For your Ecommerce store
For ecommerce stores applying this framework:
Test product-first vs lifestyle-first approaches to understand customer motivation
Challenge assumptions about price sensitivity by testing premium positioning
Experiment with social proof types: reviews vs lifestyle imagery vs expert endorsements