A/B testing

A/B Testing Definition

A/B testing, also known as split testing, is a controlled experiment where two versions (A and B) of a digital asset—such as a landing page, ad copy, or email—are shown to different audience segments to determine which performs better. It is a foundational practice in performance marketing and conversion rate optimization (CRO), used to make data-driven decisions about design, content, or targeting variations.

How A/B Testing Works

• Identify a single variable to test (e.g., headline, call-to-action, image).
• Create two versions: the original (A) and a variation (B).
• Split traffic randomly between the two versions.
• Collect performance data based on a key metric (e.g., click-through rate, form submission).
• Analyze the results to determine which version achieved better outcomes.
• Implement the winning variant and optionally test further refinements.

Most A/B tests are run using tools like Google Optimize, VWO, or Blockchain-Ads, and can be set up manually or automatically depending on the platform.

Example of A/B Testing

An e-commerce brand tests two versions of a product page:

• Version A: A red “Buy Now” button
• Version B: A green “Buy Now” button
  After sending equal traffic to both, Version B sees a 15% higher conversion rate. The brand then uses green buttons across its site to improve overall sales.

Bad usage: Testing multiple changes at once (e.g., button color, headline, and layout) without isolating a single variable confuses the result.

Why A/B Testing Matters in Advertising

• Helps marketers optimize campaigns based on real user behavior
• Reduces guesswork and relies on data for decision-making
• Improves conversion rates and ROI across channels
• Enhances user experience by identifying more effective messaging or designs
  Ultimately, A/B testing allows advertisers to continuously improve campaign performance with low risk and high strategic impact.