CRO
Conversion Rate Optimisation (CRO) is the systematic process of increasing the percentage of website visitors who complete a desired action — making a purchase, signing up, downloading, or requesting a demo. It's one of the highest-ROI marketing activities because it improves returns from existing traffic without increasing ad spend.
What is CRO?
CRO combines qualitative methods (user interviews, session recordings, heatmaps, user testing) with quantitative methods (A/B testing, funnel analysis, cohort analysis) to form hypotheses about why users aren't converting, then tests changes to validate improvements. Key tools: Google Optimize (deprecated), VWO, Optimizely, AB Tasty, Hotjar, FullStory, and GA4 funnel analysis. CRO spans landing pages, pricing pages, checkout flows, and onboarding sequences.
Why CRO matters for your career
A 1% improvement in conversion rate across a business generating $1M/month in revenue adds $120K/year — without increasing ad spend. CRO practitioners who run rigorous experiments create compounding improvements that are invisible to competitors and deeply defensible. It's one of the most commercially direct roles in digital businesses.
Career paths using CRO
CRO Specialist, Conversion Optimisation Manager, Growth Analyst, and E-Commerce Optimisation Manager are core roles. Growth PMs and Performance Marketers often have heavy CRO responsibilities.
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Frequently asked questions
How long do A/B tests need to run?▼
Until you reach statistical significance (typically 95% confidence) with sufficient sample size to detect the effect size you care about. A general rule: run tests for at least two full business cycles (e.g., two weeks to capture weekday vs. weekend patterns), even if significance is reached earlier. Stopping early on promising results is a major source of false positives.
What's the difference between A/B testing and multivariate testing?▼
A/B testing compares two versions of a page. Multivariate testing tests multiple element variations simultaneously to find the winning combination. A/B testing requires less traffic and is easier to interpret. Multivariate testing finds interaction effects between changes but requires significantly more traffic to reach significance.
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