A/B testing
Data-driven decisions
What is A/B testing?
A/B testing, also known as split testing, is a method of comparing two versions of a webpage, app, email, or advertisement to determine which performs better. By showing version A to one audience group and version B to another, businesses can collect real user data to make evidence-based decisions that improve engagement, conversions, or revenue.
Why is A/B testing important?
- Improves conversion rates: Test headlines, call-to-actions, or layouts to increase sign-ups, purchases, or downloads.
- Reduces guesswork: Decisions are based on real user behaviour rather than assumptions.
- Optimises user experience: Identifies what resonates best with your audience.
- Supports growth marketing: A core practice in demand generation, SEO, and product development.
How does A/B testing work?
- Identify the goal: e.g., increase newsletter sign-ups.
- Create two versions: Version A (original) and Version B (variant).
- Split traffic: Randomly divide visitors between both versions.
- Measure performance: Collect data on user behaviour (clicks, conversions, bounce rates).
- Declare a winner: Choose the version with statistically significant better results.
A/B testing examples
- Web development: Testing different button colours or placement to increase form submissions.
- SEO: Trying alternative meta titles and descriptions to improve click-through rate on SERPs.
- Email marketing: Sending different subject lines to see which drives more opens.
- Paid ads: Running variations of ad copy to lower cost per click (CPC).
Best practices for A/B testing
- Test one element at a time for clear insights.
- Ensure a large enough sample size for statistical accuracy.
- Run tests long enough to account for traffic variations.
- Use tools like Google Optimize, Optimizely, or VWO for tracking and reporting.
- Document results to build a library of learnings.
FAQs
No. A/B testing compares two versions, while multivariate testing examines multiple changes across several elements simultaneously.
It depends on your traffic. Generally, tests should run for at least one to two weeks to ensure enough data.
Popular tools include Google Optimize, Optimizely, VWO, and Adobe Target.
If implemented correctly (using canonical tags and avoiding cloaking), A/B testing does not negatively impact SEO.