A/B Testing (split testing) compares two versions of a webpage, email, or process to determine which performs better. It helps optimise conversion rates, user experience, and operational efficiency by measuring which version achieves better results.

It's the scientific method applied to business decisions.

Why It Matters

Everyone's got opinions about what works. Blue buttons convert better. Customers prefer detailed descriptions. Free shipping increases sales. One-page checkout beats multi-step. These opinions are cheap, confidently stated, and frequently wrong.

A/B testing replaces guesswork with data. Instead of arguing, you test both and let customers decide with their behaviour. Amazon attributes much of its success to relentless testing. Booking.com runs hundreds of tests simultaneously. They're not smarter, they have better data.

Even small improvements compound. A 5% conversion increase across thousands of monthly visitors means significant revenue growth without spending more on marketing.

The Process

Identify what to test: one specific element. Create your variation. Split traffic 50/50. Collect data tracking behaviour and conversions. Analyse results for statistical significance. Implement the winner. Test something else.

Statistical significance matters. You need hundreds or thousands of visitors, 95% confidence level, and one to four weeks minimum test duration. Declaring winners too early causes false conclusions. Most tools calculate this automatically.

What to Test

Product pages: Images (lifestyle vs white background), descriptions (long vs short, bullets vs paragraphs), pricing display, button copy and colour, review positioning, trust signals.

One fashion retailer tested model photos against flat-lays. Models increased conversion 18% because customers visualised wearing products better.

Checkout process: Form fields, progress indicators, guest checkout options, payment displays, security signals, shipping presentation.

An eCommerce store tested single-page versus multi-step checkout. Single-page increased completion 21% because users saw the entire process upfront.

Category pages: Grid vs list view, products per page, filter types and positions, sorting options, promotional banners using faceted navigation.

Email: Subject lines (length, personalisation, urgency), from names, send times, content length, CTAs.

A subscription box company tested "Your box ships tomorrow" vs "Last chance to customise". Urgency increased opens 23%.

Testing Operations

Pick path optimisation tests different warehouse routes. Sequential location picking versus clustered picking. Measure time, accuracy, fatigue. One 3PL found clustered approach reduced pick time 15%.

Packing processes compare standard boxes with void fill versus right-sized boxes. Measure time, costs, damage, feedback. A retailer found eco-friendly packaging cost 8% more but increased satisfaction 25% and repeat purchases 15%.

Quality control might test 100% inspection versus 10% sampling. Returns processing could compare detailed inspection versus rapid processing with sampling.

Common Mistakes

Testing multiple variables makes results meaningless. Change one element at a time or you won't know what caused the difference.

Insufficient sample size creates false confidence. Need at least 100 conversions per variation, one to two weeks minimum, 95% confidence.

Ignoring external factors like seasonality, promotions, or press coverage. Testing checkout during Black Friday produces results that don't apply normally.

Not segmenting results hides truth. Overall result shows no difference, but mobile users love it whilst desktop users hate it. Segment by device, traffic source, new vs returning, location, customer type.

Testing irrelevant things wastes time. Button colour makes marginal difference. Broken checkout needs fixing, not testing.

Implementation errors like tracking not working, variations displaying incorrectly, or traffic not actually split 50/50.

The Testing Mindset

A/B testing is cultural shift from opinion-based to data-based decisions. Requires humility (accepting you might be wrong), curiosity (constantly asking "what if?"), patience (waiting for significance), and discipline (testing systematically).

Make testing routine, not special project. Celebrate learning from losses—they teach too. Share results across organisation. Create testing roadmap of prioritised experiments.

Getting Started

Choose your tool: Google Optimize (free), Optimizely (enterprise), VWO (mid-market), or email platforms with built-in testing. Identify where visitors drop off based on bounce rate and cart abandonment. Form hypothesis: "Changing X will improve Y because Z." Create variation. Define success metric. Calculate sample size needed. Launch test. Wait for significance. Implement winner. Document learning. Test something else.

The businesses winning online aren't smarter. They test, learn, and optimise continuously. You'll be wrong often. That's fine; you learned something without damaging your business permanently.