What is A/B Testing? (Definition)

A/B testing (also known as split testing) is a method for comparing two versions of the same digital asset (e.g., web page, email, advertisement, button) with one small difference to determine which version achieves better results against a predefined goal.

Simply put, this is a scientific experiment where:

• Version A is the original (control group).
• Version B is the modified version (variant).
• They are shown to randomly divided audiences simultaneously, and their effectiveness is measured.

Why are A/B Tests Important?

In digital marketing, decisions are made based on data, not assumptions. A/B tests help marketers to:

• Increase Return on Investment (ROI): Even small changes can lead to significant improvement in conversion.
• Reduce Risk: Any major change to a website or campaign can be tested first with a small portion of the audience.
• Understand Users: Tests reveal what resonates with your audience – what language, design, and user experience work.
• Resolve Disputes: Instead of arguing with colleagues or bosses based on personal opinion, you can test and make decisions based on data.

How Does A/B Testing Work?

• Problem Identification: You notice that you have a low conversion rate on a specific page or low CTR (click-through rate) in an email campaign.
• Hypothesis Formulation: You create an assumption about what change might improve the result. E.g.: "If I change the color of the call-to-action (CTA) button from blue to red, I will increase clicks because it will stand out more."
• Creating the Variant (Version B): You make the desired change – only one! (e.g., only the button color, not both color and text simultaneously).
• Starting the Test: Testing software (like Google Optimize, Optimizely, VWO) shows Version A to 50% of visitors and Version B to the other 50% for a specific period.
• Data Collection and Analysis: After collecting enough data (usually when the test reaches "statistical significance"), you analyze the results.
• Decision Making: If Version B achieves significantly better results, you apply it to the entire audience. If not, you return to Version A and test a new hypothesis.

What Elements Can Be Tested?

Almost every element of your digital asset can be A/B tested:

• Headlines and Text: Length, tone, wording.
• Call-to-Action (CTA) Buttons: Color, size, text, placement.
• Images and Videos: Type, placement, size.
• Ad Copy: Google Ads, Facebook Ads.
• Email Subject Lines: Subject line order, sender name.
• Pricing and Offers: Promotion text, free shipping.
• Layout and Navigation: Element placement, menus.
• Forms: Number of fields, placeholder text.

Real-Life Example

Problem: The "Sign up now" button on a website has a low click-through rate.

Hypothesis: Changing the button text from "Sign up now" to "Start your free trial" will increase clicks because it emphasizes the benefit (free) and reduces the feeling of commitment.

Test:

Version A: Button with text "Sign up now" (blue color)

Version B: Button with text "Start your free trial" (blue color - same color to isolate the change)

Result: After two weeks, Version B shows a 17% increase in clicks. The decision is to apply it to all visitors.

Key Terms

• Statistical Significance: Shows whether the test results are reliable and not caused by chance. Usually aims for 95% or 99% confidence level.
• Conversion Rate: The percentage of users who perform the desired action.
• Control Group: The original version (A).
• Variant: The new, tested version (B).

Important: Always test only one change at a time so you know exactly what caused the difference in results. If you change multiple things simultaneously (e.g., button color and text), you won't be able to determine which one is responsible.

A/B tests are a powerful tool for continuous and gradual optimization of all aspects of digital marketing, leading to better results and better understanding of customers.