TL;DR
A/B testing compares two versions of a marketing variable (like a landing page, ad creative, or subject line) to determine which performs better. It’s a foundational tool for data-driven optimization, especially in digital channels.

What is A/B Testing?

A/B testing, also known as split testing, is a controlled experiment where two variants (A and B) are shown to different segments of users to evaluate which one yields better results against a defined metric. This can apply to email campaigns, website elements, product pricing, ad copy, or UX changes.

Typically:

  • Version A is the control (current or original version).
  • Version B is the treatment (new or modified version).

Users are randomly split between the two, and their behavior is tracked to assess performance.

Common metrics tested include:

  • Click-through rate (CTR)
  • Conversion rate
  • Engagement rate
  • Revenue per visitor (RPV)

Popular tools for running A/B tests include platforms like VWO (Visual Website Optimizer) and Optimizely. Note that Google Optimize was discontinued as of September 30, 2023.

Why is it important?

Data-Driven Decision-Making

A/B testing eliminates guesswork by validating changes with real user behavior, reducing the risk of deploying ineffective updates.

Incremental Optimization

It enables teams to make small, evidence-backed improvements that compound over time – critical for performance marketing, CRO (conversion rate optimization), and product-led growth.

Attribution Accuracy

Unlike multivariate or multi-touch approaches, A/B testing provides a clean, single-variable comparison, which makes it easier to attribute performance changes to specific factors.

Key Considerations

Statistical Significance and Sample Size

Running a test without enough data can lead to misleading results. Ensure you calculate the minimum detectable effect and use tools to estimate required sample size based on your traffic and baseline conversion rate.

Test Duration

Avoid stopping tests too early. Time your tests to account for variation in user behavior across days and weeks (for example, weekday vs. weekend behavior).

Randomization and Segmentation

Users must be randomly and evenly assigned. For tests involving personalization or geolocation, segment-aware randomization is crucial to prevent skewed results.

Limitations

  • Only one variable can be reliably tested at a time.
  • Not suitable for low-traffic environments where results may take too long.
  • False positives or inconclusive results can occur without rigorous controls