Optimisation is mostly measurement. These cover how to run a test that produces a trustworthy answer, and how to read the result without fooling yourself.
13 articles
A/B testing is the most reliable way to improve conversion rates. But most tests fail because of poor methodology, not poor ideas. This guide shows you how to run tests that produce trustworthy results.
The biggest waste in A/B testing is testing the wrong things. This guide shows you how to use analytics, user feedback, and competitive analysis to identify tests that move the needle.
Most email sign-up forms fail because they are invisible, ask for too much, or offer too little. These three fixes can double your opt-in rate without any additional traffic.
A landing page conversion is the percentage of visitors who complete the page target action. The formula is simple, but most teams measure it wrong because session-based analytics fragments the data. Here is what the metric actually means, how to calculate it, and what good looks like in 2026.
Calling a test winner too early is the most common A/B testing mistake. This guide explains statistical significance in plain language and shows you exactly when it is safe to make a decision.
E-commerce converts at 2-3%, SaaS free trials at 2-5%, trial-to-paid at 15-25%, B2B landing pages at 2-5%. Here is the full set of conversion rate benchmarks by industry and funnel stage, and how to use them without drawing the wrong conclusion.
Your main funnel shows the big picture. Micro-funnels reveal the hidden friction points within each stage. This technique consistently uncovers the small fixes that produce outsized improvements.
Every form field you add reduces completions by 3-5%. But removing fields means less data. This guide shows you how to balance data collection with conversion using progressive profiling and smart defaults.
Button color debates miss the point. The data shows that copy, placement, and context matter far more than whether your CTA is green or orange. Here is what actually moves conversion rates.
Exit intent overlays can recover 10-15% of abandoning visitors when done right. But most are done wrong: too aggressive, poorly targeted, and never measured. Here is the analytics-driven approach.
The formula is trivial. Choosing the denominator, defining the conversion, locating the stage it belongs to and knowing how much of its movement is noise are not, and a rate missing those four is a different quantity that happens to share the name.
Run 20 tests and at least one will show a false positive at 95% confidence. This guide explains the multiple comparisons trap and gives you practical methods to avoid it.
Most A/B tests are called too early with too little data. This guide shows you how to calculate the right sample size before you start and why peeking at results destroys statistical validity.
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