A/B Test

Optimizing digital marketing strategies and campaigns can help you achieve success. But how do you know what resonates best with your target audience? 

A/B testing, or split testing, is a fundamental technique marketers use to compare two versions of a webpage, email, ad, or other marketing asset to determine which one performs better at achieving the overall goal. 

What is A/B testing?

A/B testing is an optimization method for comparing two versions of any market asset to determine which one performs better. 

In an A/B test, the original version is compared against a variation that includes one or more changes. These changes involve elements like headlines, images, CTAs, colors, or layout updates. 

By randomly presenting these variations to users and collecting data on their interactions and responses, you can analyze the results to identify which version drives more conversions, click-throughs, or other desired results. 

How does A/B testing work?

Here's how A/B testing typically works: 

Forming a hypothesis

The process starts with forming a hypothesis or question about a specific element of a marketing asset that could be improved. 

For example, changing the color of a call-to-action button might increase click-through rates.

Creating variations

Two versions of the marketing asset are created: the original version (the control or A version) and a modified version (the variation or B version). 

In our example, the control version might have a green call-to-action button, while the variation version might have a blue button.

Randomized assignment

Visitors or users are randomly assigned to either the control group (exposed to the A version) or the variation group (exposed to the B version). 

This random assignment helps ensure that any differences in performance between the two versions are not due to external factors or biases.

Data collection

Metrics relevant to the test's objective are measured and tracked for each group. Common metrics include click-through rates, conversion rates, bounce rates, time on page, or revenue generated. 

In our example, you would track the number of users who click the call-to-action button for the control and variation versions.

Statistical analysis

Once sufficient data is collected, determine if the two versions have a statistically significant difference in performance. This analysis helps determine if any observed differences are likely due to the variations in the marketing assets rather than random chance.


Based on the results of the A/B test, the version that performs better is implemented as the new standard. This is the one you’ll use throughout your marketing strategy. You can also A/B test against this new standard to ensure continuous improvement. 

Continual testing and optimization

A/B testing is an iterative process, and continual testing and optimization are often necessary to improve performance.

Benefits of A/B tests

A/B testing offers many benefits for businesses seeking to optimize their marketing strategies.

Increased engagement

A/B testing enables businesses to tailor their marketing assets based on user preferences and behavior. 

By testing different variations, businesses can identify elements that resonate most with their audience, leading to increased engagement with their content, emails, ads, or website.

Better user experience

Businesses can enhance the overall user experience by systematically testing different design elements, layouts, messaging, and user flows. 

A/B testing helps identify the most user-friendly and intuitive designs, leading to higher satisfaction and retention rates among customers and visitors.

Improved conversion rates

A/B testing allows businesses to fine-tune their conversion funnel by experimenting with various calls-to-action, forms, pricing strategies, or product placements. 

By identifying the most effective conversion strategies, businesses can optimize their marketing efforts to drive more conversions, whether sales, sign-ups, or other desired actions.

Key elements to A/B test

You can test virtually any element of a website that can influence user behavior, such as: 

  • CTA buttons
  • Form length and layout
  • Personalization
  • Headlines
  • Social proof
  • Content types
  • Images/visuals
  • Color schemes

Make data-driven decisions with A/B testing

A/B testing optimizes digital marketing strategies and campaigns to help businesses identify what resonates best with their target audience, increasing engagement, providing a better user experience, and boosting conversion rates. 

Refine and optimize ad creatives and copy by A/B testing your Reddit Ads. Try different variations to see what works best and ensure your ads are as effective as possible.