By Inga Gérard, MSc, Director Marketing, CEO & Founder, IG Scientific Marketing

A/B testing has been around for nearly a century. Originally developed by statistician Ronald Fisher in the 1920s for agricultural experiments, it later became essential in medical trials and marketing campaigns. Today, it is a cornerstone of digital optimization, used everywhere from e-commerce to SaaS platforms to advertising.

But despite its simplicity, A/B testing is often misunderstood. Let’s dive into what it really is, how it works, and common mistakes to avoid.

What is A/B Testing?

A/B testing is a method of comparing two versions of something, whether it’s a webpage, an email subject line, or a call-to-action button, to see which one performs better with a target audience.

It is basically a simplified version of a randomized experiment. It helps ensure that any changes you make actually drive results, rather than just being random noise. By testing one variation against another under controlled conditions, you get real, data-backed insights instead of relying on gut instinct.

For example, say you want to increase sign-ups on your website. You might test two variations of a “Subscribe” button:
🔹 Version A: Square, green button
🔹 Version B: Round, blue button

Visitors are randomly assigned to one version, and you track the conversion rate (the percentage of users who click). If Version B outperforms Version A, you’ve found a winner.

The Power of A/B Testing: Improving a Landing Page

Here’s another example. A company wanted to see if adding a human touch to its landing page would increase conversions. The original page was very standard, with generic stock photos and lacking personalization.

They tested:
✅ Replacing stock photos with real images of team members.
✅ Adding video testimonials from actual customers.
✅ Displaying real job postings to reinforce trust and transparency

An A/B test was conducted to compare the updated version with the original.

The result? A significant increase in conversions, proving that authenticity builds trust.

Why A/B Testing Works (and How to Do It Right)

As mentioned earlier, A/B testing is a powerful decision-making tool. But A/B testing isn’t just about running experiments; it’s about running them correctly.

If you follow these steps, you’ll get better outcomes with more reliable results:

1. Define Clear Objectives: What metric are you trying to improve? Click-through rates? Purchases? Form submissions? Set measurable goals (SMART!).
2. Choose a Single Variable to Test: If you test too many things at once without structure, you won’t know what caused the change.
3. Randomize Your Sample: Avoid biased results by ensuring that your test panel is evenly split between your two variations.
4. Run the Test for Long Enough: Ending an A/B test too early is one of the biggest mistakes. Let statistical significance guide you.
5. Analyze & Implement: If one version wins, deploy it.
6. Test Again!: Just because something worked once doesn’t mean it will work forever. Consumer behavior evolves, so test again, especially when performance drops.

Beyond A/B: The Rise of Multivariate Testing

Multivariate testing takes A/B testing to the next level. While A/B testing focuses on comparing one variation to another, multivariate testing lets you evaluate multiple changes at once. Instead of testing a button’s color, size, and text separately, you can test all these factors simultaneously to understand how they work together.

For instance, instead of running three separate tests for color, size, and font, multivariate testing would show you combinations like:

🔹 Large red button with Arial
🔹 Small blue button with Times New Roman
🔹 Large blue button with Arial

This approach allows you to see not just how each individual element performs, but how they interact to drive results. It’s like upgrading from testing one option at a time to testing a whole set of possibilities in parallel.

While this method provides deeper insights, it also requires more statistical support to ensure your results are accurate. For marketers looking to use multivariate testing effectively, platforms like Google Optimize, Optimizely, or VWO offer the tools you need to get actionable data without being overwhelmed by complexity.

If you’re still sticking to simple A/B tests, it’s time to consider the power of multivariate testing to fine-tune your digital strategy.

A/B Testing: Used Every Day

Major tech companies use A/B testing at scale. Every time you browse an e-commerce site, click an ad, or open a marketing email, you’re likely part of an experiment, without even knowing it.

📩 Email marketing – Testing subject lines to improve open rates
🛒 E-commerce – Optimizing product page layouts to increase sales
📱 App design – Tweaking UI elements to improve engagement

Even small businesses can benefit. The beauty of A/B testing is that it’s low-cost, quick to implement, and highly effective.

Conclusion

After reading this article, you hopefully agree that A/B testing isn’t just for the data experts; it’s essential for anyone involved in decision-making across marketing, sales, product development, and more. The secret to success lies in how you test: ensuring that your experiments are thoughtfully designed and the conclusions you draw are rooted in solid, actionable data.

Have you ever run an A/B test? What were your results? Let’s discuss in the comments!

Sources

• A Refresher on A/B Testing – Harvard Business Review
• A/B Testing: A Beginner’s Guide – HubSpot
• What Is A/B Testing? – Optimizely
• Best Practices for A/B Testing – VWO
• Insights from Kaiser Fung – Data science expert & author of Number Sense