How to Run a Successful A/B Test – Best Practices & Common Mistakes
Every campaign, landing page, and creative asset is built on assumptions about what will persuade your audience. But assumptions alone are risky. The only way to truly know what works is to test, measure, and optimise based on real user behaviour. That’s why A/B testing is fundamental to digital marketing success. For e-commerce brands, A/B testing can reveal which product images increase conversion rates or which promotional offers drive higher cart values. For lead generation businesses, it can uncover which landing page layouts capture more qualified leads or which messaging frameworks improve form completion rates. A/B testing takes the guesswork out of your campaigns. It allows you to validate ideas with hard evidence and identify what truly moves the needle. Even the boldest ideas and the most trusted best practices require validation through structured testing.
What is A/B Testing?
A/B testing is a controlled experiment where two variations of a single element—such as an ad image, video, landing page, email, headlines, copy or call-to-action—are shown to different segments of your audience to determine which performs better against a specific objective.
In an A/B test:
- Version A is your original or control version.
- Version B is the challenger, where you modify one key element you want to test (e.g., a headline, a button colour, an image, an offer).
Steps to Run a Successful A/B Test
A well-executed A/B test follows a structured process. Here’s how to approach A/B testing systematically:
1. Establish a Well-Defined Objective
Every A/B test requires a clear and quantifiable goal. Without a specific goal, you risk collecting data that looks interesting but leads to no actionable decisions.
Your objective should be directly linked to a critical business result, for instance:
- Improving the efficiency of paid media by reducing Cost per Acquisition (CPA) while maintaining lead quality
- Increase in Return on Ad Spend to grow revenue without increasing the media budget.
Common Mistake: Setting vague goals like “get more clicks” without connecting them to revenue or pipeline impact. Always ask: “If this test wins, how will it drive business growth?”
2. Formulate a Clear Hypothesis
A strong hypothesis predicts the outcome of your test and gives it purpose.
Good hypothesis example:
“Reducing the number of fields in the lead capture form will increase the submission rate by at least 20%, without sacrificing lead quality.”
Or
Using product-focused lifestyle video ads instead of product static images will increase Click-Through Rate (CTR) and Return on Ad Spend (ROAS) by at least 5%
The hypothesis should follow the format:
- If [change is made], then [specific result is expected]
3. Select One Variable to Test
Focus on changing only one element at a time. Testing multiple changes simultaneously will make it impossible to determine which factor influenced the result.
4. Split the Audience Randomly
Ensure that your test groups (A and B) are randomly and evenly split from the same audience pool. This guarantees that performance differences are due to the variation, not differences in audience behaviour. Most advertising platforms (Meta, Google Ads etc) allow you to split traffic automatically.
5. Choose the Right Success Metric
Define the primary metric that will determine success before you launch the test. For example, Cost Per Acquisition, Return on Ad Spend, or CTR.
6. Run the Test for an Appropriate Duration
Allow the test to run long enough to gather meaningful data. Avoid making decisions based on early trends—short-term fluctuations are common.
- Run the test for at least 1–2 weeks to account for daily variations and gather enough data. More data increases the accuracy of the test.
- Each of the variants should have served over 100K impressions. If possible – aim for at least 100 conversions per variant (or more if traffic volume allows).
- Achieve 95% statistical confidence before declaring a winner.
7. Test no more than 4 variations at a time
Our recommended number of variations is up to 4 at a time depending if there is sufficient budget to support the variants. If either the audience size or the budget is small, stick to 2 variations at a time.
8. Analyse your tests and take action
Running an A/B test is only half the job. How you analyse the results ultimately determines whether you gain a true insight—or draw the wrong conclusion. The goal of analysing test results is not simply to pick a “winner” but to understand:
- What changed user behaviour
- Why the winning variant performed better (or didn’t)
- What broader learnings can be applied to future campaigns
Good analysis looks beyond surface-level metrics. It connects the performance back to the original hypothesis and business objective, while critically examining all relevant data segments.
9. Document all your test results
A/B testing is not just about single improvements—it’s about building a permanent foundation of learnings across your marketing efforts. Each A/B test, whether it wins, loses, or shows no significant difference, provides critical insights that should inform future campaigns, creative strategies, landing page optimisations, and even broader marketing decisions. Hence every test should be carefully documented and shared with a wider team. Here is a template to document all your test results with an example.
In Summary
A/B testing is a powerful tool for marketers looking to optimise their campaigns. By testing elements like subject lines, CTAs, and designs, you can make data-driven decisions that drive higher engagement and conversion rates. With the right tools and strategy, A/B testing removes guesswork, improves performance, and ensures every decision is backed by concrete data. Ready to optimise your marketing strategy? Get in touch with an ADMATICian today.
