Website conversion 101 for startups

Stop redesigning your site.

That's probably our best advice when it comes to conversion for startup sites.

Because at the end of the day, the startups winning at conversion aren't the ones with the prettiest homepages. They're the ones running experiments every week, learning from failures, and compounding small wins.

Here's everything we’ve learnt about website conversion for startups after running hundreds of tests over the years ⬇️

But first…

Are you ready to test? (a checklist ✅)

Before you A/B test anything, answer these three questions

• Do you have 1,000+ visitors per week? Below that, your tests won't reach statistical significance. You'll be making decisions on noise, not signal.‍
• Can you track what matters? If you can't measure signups, clicks, and drop-offs accurately, you're flying blind. Set up event tracking in GA4 or Mixpanel first.
• Do you know your most important metric? Current conversion rate. Average time on page. Bounce rate by source. It all starts with finding the right thing to measure.

If you answered "no" to any of these, you might need to fix some fundamentals first. Clear your value proposition. Speed up your site. Remove obvious friction.

The 5 tests every startup should run

Once you've got the basics sorted, start here. These five experiments tend to deliver the biggest impact for early-stage startups. ⬇️

Test 1: Value proposition clarity
Generic positioning ("Powerful platform for teams") loses to specific outcomes ("Turn support tickets into insights in 3 clicks for SaaS teams").

Test 2: CTA copy
"Get Started" vs. outcome-focused ("See your dashboard"). People want to know what happens when they click.

Test 3: Social proof placement
Put trust signals directly under your CTA, not buried at the bottom. Reduces perceived risk at the moment of decision.

Test 4: Signup form length
Email only vs. 5-8 fields. This is consistently the highest-impact test. Ask for everything else after they've experienced value.

Test 5: Product visibility
Show your actual UI above the fold. Product-led buyers want to see what they're signing up for.

Not all visitors are equal (and your tests shouldn't treat them that way)

The thing about A/B testing is that aggregate results lie.

👀 A homepage test shows +8% conversion. Looks like a win. Ship it.

But check the segmented data:

• Organic search: +42% conversion
• Paid ads: -12% conversion
• Direct traffic: +3% conversion

⚠️ Optimise for the average, and you optimise for nobody.

Always segment your results by ⬇️

• Traffic source (organic vs. paid vs. direct behave completely differently)
• Device type (mobile vs. desktop—long copy can win +30% on desktop while losing -5% on mobile)
• Geography/Industry (what works in US can flop in EMEA)

When to run separate tests:
If one segment represents >30% of your traffic and behaves differently, test it separately. Don't optimise for averages, optimise for your best customers.

The conversion testing toolkit

Stage 1: Just launched (<1,000 visitors/week)
Microsoft Clarity (free session recordings) + Loom (quick feedback). Find the obvious problems.

Stage 2: Ready to test (1,000-10,000 visitors/week)
Add GA4 for conversion tracking. Run 1-2 tests per month. Build a testing discipline.

Stage 3: Scaling (10,000+ visitors/week)
Add paid tools for advanced segmentation (Hotjar, VWO). Test multiple segments simultaneously.

When to redesign vs. when to test

Here's a typical scenario we’ve seen unfold many times:

Website converts at 2%. Team invests 3 months and $30K in a redesign. New site launches. Conversion: 2.3%.

The new site looks better, but the ROI is disappointing.

Why redesigns often underdeliver

❌ Missing the real problem
Teams assume the issue is visual when it's often about clarity. A beautiful redesign can make an unclear message look more professional, without making it clearer.

❌ Changing too much at once
When you ship 47 changes simultaneously, you can't identify which ones helped. Next time you want to improve, you're guessing again.

❌ Designing by preference instead of data
"This looks better" is subjective. "This converted 30% better" is measurable.

Why systematic testing works differently

✅ You learn from every experiment
Test 10 variations and you'll understand your users 10x better, regardless of whether each test wins or loses.

✅ Improvements stack
Small wins compound. +15% here, +12% there, +20% on the third test. Total: +55% improvement.

✅ You control the risk
Test a hypothesis on 10% of traffic for 2 weeks. If it fails, you've risked 10% of conversions for a short period. Compare that to launching a redesign that underperforms – you're risking 100% until you fix it.

Test for 6-12 months, learn what matters, then do a strategic redesign that locks in those learnings.