Conversion Rate Optimisation
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CONVERSION RATE OPTIMISATION

Understand it, log it, optimise it
FROM “CLICK HERE” BY ALEX SCHULTZ

Conversion rate optimisation is the discipline of turning more of your existing traffic into customers: understand what a conversion is, log it properly, then optimise the flow. I rate it the highest-leverage work in marketing, because every improvement compounds across all of your acquisition spend.

🎯
Define Your Conversions

Primary Conversion

The main action that drives your business:

  • E-commerce: Complete purchase
  • SaaS: Start paid subscription
  • Media: Subscribe to newsletter
  • App: Complete signup & first key action

Secondary Conversions (Micro-conversions)

Smaller actions that lead to primary:

  • Add to cart
  • Start free trial
  • Download resource
  • Watch demo
  • Create account

🔍
Not to Be Confused With

🎯 Primary vs micro-conversions

The primary conversion is the action that drives the business; micro-conversions are the steps along the way. Optimise the primary, diagnose with the micro.

📝 Logging vs attribution

Logging records what happened; attribution argues about who gets credit. Get logging right first: I have seen teams argue for quarters over attribution models built on event data nobody had validated. Attribution built on bad logging is fiction.

🧪 Testing vs tweaking

A test has a hypothesis, a control, and a pre-registered read window. Changing button colours on a hunch while watching the dashboard is not testing.

📋
The Three Essentials
1

LOG IT

Record every conversion event:

  • User ID
  • Timestamp
  • Source / channel
  • Device type
  • Value

And define the conversion before you log it. At eBay we tracked confirmed registered users, then realised that was the wrong conversion: we switched to activated confirmed registered users: registered, confirmed, and having bid on, bought or listed an item. Defining the conversion is the logging problem.

2

ATTRIBUTE IT

Understand which efforts drove the conversion. But beware: last-click attribution is broken.

Use incrementality testing where possible.

3

MEASURE IT

Key metrics:

CR = (Conversions ÷ Visitors) × 100
CPA = Total Spend ÷ Conversions
ROAS = Revenue ÷ Ad Spend

🗺️
Map Your Conversion Path
Ad Click
1,000
Landing Page

→
▼ 40%
600
Product Page

→
▼ 67%
200
Cart

→
▼ 40%
120
Checkout

→
▼ 33%
80
Thank You

Illustrative example: your numbers will differ, but the shape is always a funnel.

“Your biggest drop-off is your biggest opportunity. Fix the leakiest part of the bucket first.”

⚡
Optimisation Quick Wins
✅ Reduce form fields: every field removed increases completion (my experience)
✅ Clear CTA copy: “Create an Ad” beat “Advertise” by ~40% (from the book)
✅ Personalise by name: double-digit uplifts throughout my career (my experience)
✅ Add social proof: friends’ actions > reviews > nothing (my experience)
✅ Move CTAs to prominent positions: bottom to top gave a 30% lift (from the book)
✅ Show context: showing email provider logos gave a double-digit uplift (my experience)
✅ Explain why: “Because you bought this…” increases acceptance (my experience)
✅ Chain actions: promote the next step right after someone completes the current one (my experience)

🧪
Testing Principles

Isolate each test

I run multivariate tests, but I never cross-contaminate: each experiment gets a clear control and variant.

Pre-register test length

Don’t peek and kill early. Require sustained significance over a pre-registered period.

Business significance matters

“If you need a data scientist and a microscope to determine if you had impact, then you didn’t.”

Conversion is where your marketing investment pays off. If your conversion flow is broken, no amount of top-of-funnel spend will save it. Every additional step loses users. Minimise friction relentlessly.

🧭
Know Its Limits

My position: optimisation has a ceiling, and the ceiling is set by your offer and your audience. You can test your way from 2% to 4%, but you can’t optimise your way out of a product people don’t want or traffic that was never qualified. When my tests stop producing winners, the problem is usually upstream: in the targeting or the offer itself.

Beware the local maximum, too. Relentless optimisation of the current flow can blind you to the fact that a completely different flow would convert twice as well. When I hit that wall, I stop testing the flow and redesign it.

And keep the business-significance rule: if you need a microscope to see the impact, you didn’t move the business.

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