Table of contents
What Is A/B Testing?
A/B testing (also called split testing) means comparing two versions of a webpage, email, advert, or other marketing asset - version A (the original) and version B (the variation) - by showing each to a similar audience at the same time and measuring which one performs better.
In other words, A/B testing replaces guesswork with data-driven decisions. As Google’s Digital Marketing Evangelist Avinash Kaushik says:
A/B testing is not only about the testing, but also about the learning.
How Does A/B Testing Work?
A/B testing typically follows these steps:
- Set a clear goal: Define exactly what you want to improve, such as click-through rates, conversions, or sign-ups.
- Create two versions: Version A (the control) and version B (the variation). Change only one element at a time, such as the headline, call-to-action, image, or layout.
- Split your audience: Randomly divide your audience into two statistically similar groups.
- Run the test: Show each group one version simultaneously and track performance metrics.
- Analyse results: After gathering enough data, compare the results to identify the winning version.
- Implement and iterate: Use the winning version and continue testing new ideas to further optimise your marketing.
For a visual overview, see this helpful A/B testing process diagram.
Advertising legend David Ogilvy famously advised:
Never stop testing, and your advertising will never stop improving.
Why Should You Use A/B Testing?
If you’re serious about growth marketing, A/B testing is essential. It helps you:
- Understand your audience better by learning what resonates with them.
- Optimise your marketing assets to maximise conversions and ROI.
- Reduce risk by validating ideas before committing significant resources.
However, effective A/B testing requires clear hypotheses, structured processes, and robust analytics. If your team struggles with these elements, Growth Method can help.
A/B Testing vs Other Experimentation Methods
A/B testing is one of several ways to test a marketing change. Here’s how it compares to two other common methods, and when a lighter-weight minimum viable test beats a full A/B test:
| Method | What it compares | Best for | Traffic needed |
|---|---|---|---|
| A/B testing | Two full versions of one page or asset (A vs B) | Validating one clear change with statistical confidence | Medium-high |
| Multivariate testing | Multiple combinations of several elements at once | Finding which specific elements interact and matter most | High |
| Before/after testing | The same page over two time periods, no control group | Quick, low-traffic pages where a proper split isn’t practical | Low |
About Growth Method
Running one A/B test is easy. Building the discipline to run test after test - each with a clear hypothesis, a recorded baseline, and a documented result - is what actually compounds into growth. That’s what Growth Method is built for: it’s the agentic marketing platform for B2B teams, built for people and agents to plan strategy, ship campaigns, and learn what works, all in one place.
Instead of an A/B test living as a one-off spreadsheet row, Growth Method turns each test into a structured plan-launch-analyse workflow: ideas are automatically checked against your team’s hypothesis best practices, tests move through clear stages that increase your experiment velocity, and before/after metrics are captured and reported without manual work.
Get started to run your next A/B test inside Growth Method.
Further Resources on A/B Testing
To learn more about A/B testing and conversion optimisation, explore these resources:
- Our Guide to Conversion Rate Optimisation
- Marketing Experimentation: How to Run Effective Tests
- Building a Culture of Experimentation
- Understanding Sample Ratio Mismatch in A/B Testing
- Frequentist vs Bayesian Statistics in A/B Testing
- CXL’s Comprehensive A/B Testing Guide
- Optimizely’s A/B Testing Guide
- Smashing Magazine’s Ultimate Guide to A/B Testing by Paras Chopra
- VWO’s A/B Testing Guide
- Trustworthy Online Controlled Experiments by Ron Kohavi
Ultimately, A/B testing is about making smarter marketing decisions based on data rather than guesswork. As statistician W. Edwards Deming famously said:
In God we trust. All others must bring data.
Frequently asked questions
What is A/B testing in marketing?
In marketing, A/B testing means showing two versions of a page, email, or ad to similar audiences at the same time and measuring which one performs better against a clear goal, such as clicks, sign-ups, or sales. It replaces opinion-based decisions with a real, measured result.
What is the difference between A/B testing and multivariate testing?
A/B testing compares two full versions of one page or asset against each other. Multivariate testing compares multiple combinations of several elements at once (for example, headline, image, and button colour together), which needs significantly more traffic to reach a reliable result.
How long should an A/B test run?
Run an A/B test for at least one to two full business cycles (commonly two to four weeks), and predetermine your sample size in advance rather than stopping as soon as you see a promising result - early trends often regress to the mean.
Is A/B testing the same as split testing?
Yes. A/B testing and split testing are the same thing: two names for comparing version A against version B to see which one performs better.