How to Run A/B Tests with Strapi to Boost Conversions

Experimentation is one of the most effective ways to make better product decisions. Among the most widely used methods is A/B testing, a technique that helps teams validate ideas with real user data before rolling out changes broadly. If your website is powered by Strapi, combining it with a headless A/B testing platform enables you to launch experiments and optimize conversion rates without friction.

In this post, we’ll cover the essentials of AB testing, best practices, common pitfalls, and how you can set up experimentation with Strapi and Croct.

What is A/B testing?

At its core, A/B testing is the process of comparing two or more versions of a page, component, or strategy to determine which one performs better in achieving a predefined goal.

For example, you might test different headlines on your landing page to determine which drives more sign-ups. By randomly exposing a percentage of traffic to each version, you collect data that enables you to measure the impact of changes before making a full commitment.

When should you run A/B tests?

A/B testing is most valuable when you’re unsure about how changes in your interface or content could impact user behavior, revenue, or engagement. For instance:

• Deploying a website redesign
• Testing alternative CTAs
• Validating new navigation layouts
• Experimenting with pricing pages and product cards

Beyond these examples, here are some situations where running an A/B test can make a real difference:

• Early-stage features: When you’re introducing a new product feature and want to validate if users actually engage with it as expected.
• Content optimization: When you’re unsure which copy, design, or visual assets resonate best with your audience (here you can think about running segmented AB tests.
• Conversion funnels: When drop-offs happen in sign-up or checkout flows, and you want to identify which adjustments reduce friction.
• Retention strategies: When you’re experimenting with onboarding experiences, emails, or in-app prompts to improve long-term user engagement.
• Seasonal campaigns: When you want to compare variations of promotions or offers during high-traffic periods.

In short, you should consider running A/B tests whenever the outcome of a decision carries uncertainty and the potential impact on your business justifies the effort.

A/B testing isn’t about testing everything. It’s about testing what matters most.

Why most companies struggle with AB testing

While the concept of A/B testing sounds straightforward, many companies struggle to make it work.

Many products aren’t built with experimentation in mind, so every test feels like a bespoke engineering project, especially if the architecture is rigid. This usually means that CRO teams often depend heavily on scarce developer resources, which slows down the iteration process. If you work with product interfaces or e-commerce stores, consider that applications are usually dynamic, stateful, and connected to backend systems, making experiments more challenging than simple marketing page tests.

On the workflow and ownership side, we must account for the fact that product, growth, and engineering teams may not align on who drives experimentation. With unclear ownership, experimentation projects lead to stalled initiatives.

As if this is not enough, let's consider the technical aspect of AB testing: when improperly implemented, experiments can compromise reliability and performance, which introduces risk and analytics hurdles. If you don't have the right tooling at your side, you'll fall short.

In short, experimentation is challenging because of technical complexity, organizational misalignment, and a lack of purpose-built tools. Overcoming these barriers is key to unlocking continuous, meaningful conversion optimization.

A/B testing best practices

Running effective experiments requires more than just changing button colors. The A/B test itself is just one of many parts of a long workflow, and you should pay attention to each step with the same care as the test itself.

1. How to talk your boss into A/B testing

Sometimes, the biggest challenge isn’t technical, it’s organizational. Convincing leadership to invest in experimentation requires framing it as a lever for growth, highlighting the ROI of A/B testing, and positioning it as a competitive advantage. This guide provides practical guidance on framing the conversation, emphasizing risk reduction and continuous learning.

2. How to prioritize your AB testing hypotheses

Before even listing them, you should define what success means to you and which metrics you can use to validate it.

Listing testing hypotheses requires both data knowledge and creativity, and there's nothing better than brainstorming. You should invite all your team members to this assignment and have them consider what can improve the conversion rates. The more diverse this team is, the better.

Once you have a list of ideas to test, use a method like the ICE scoring to evaluate each option by assessing its impact, your confidence in its potential outcome, and the ease of implementation.

3. How to plan your AB testing

This should be the easiest step.

Start by defining your primary and complementary metrics. Gather current traffic and conversion rate information to estimate the experiment duration beforehand (this calculator can help you). Then, define which method you will use to analyze the results.

If you have a tight calendar, running multiple tests simultaneously may sound tempting. Don't do that. Overlapping experiments could interfere with one another, so our advice is to keep it simple and test one hypothesis at a time.

Finally, ensure that you include a step for documenting and sharing the learnings. Even failed experiments produce valuable insights. Maintain a knowledge base to prevent teams from repeating the same tests or mistakes.

How to analyze your experiments

Once the data comes in, it’s crucial to analyze the results correctly. Two common approaches are frequentist and Bayesian statistics, and we could write a whole article about each of them.

While frequentist methods have been the standard for decades, Bayesian methods are increasingly popular due to their flexibility, intuitive insights, and faster conclusions.

If you want to dive deeper into each of these topics, we suggest these two guides:

• Bayesian or frequentist: which approach is better for AB testing?
• Bayesian approach: a deep overview for AB testing

Which tools can you use?

The right tool can make or break your experimentation workflow. Choosing the right one depends on your team’s technical expertise and needs. It's not an easy task.

Before discussing names, consider the four types of tools and the level of development dependence each one brings.

1. No-code and low-code tools

Tools like VWO and Convert allow marketers and product managers to create experiments without any developer involvement.

These tools typically provide a visual editor for modifying elements on a webpage and tracking conversions, but offer limited to no flexibility for complex experiments, and usually impact page performance due to client-side execution.

2. Headless experimentation platforms

Platforms like Croct and Optimizely provide AB testing and personalization directly within the content management system.

These tools integrate seamlessly with dynamic websites, enabling teams to manage experiments without incurring engineering overhead, flickering, or performance issues. However, it requires a developer for the first integration and doesn't provide full flexibility for marketers in terms of design and layout.

3. JavaScript-based tools

JavaScript-based tools, such as AB Tasty, Amplitude Experiments, and Dynamic Yield, require a basic knowledge of JavaScript but offer more control over experiments. They typically involve embedding a script into the website to dynamically modify elements, which increases the dependence on developers for technical support at each test.

4. Feature flag

Feature flag tools like LaunchDarkly and Split.io allow teams to deploy new features to a subset of users without affecting everyone. Developers widely use them to roll out and test changes gradually, making them useful for backend-controlled experimentation.

5. Fully developer-driven

These solutions offer the highest level of flexibility and control, enabling engineers to implement and manage A/B testing directly within the codebase. They’re often combined with analytics platforms for result tracking.

How to run AB tests on Strapi-powered websites

There are many A/B testing tools on the market, but if you’re working with Strapi, Croct provides an out-of-the-box solution for experimentation and personalization. It stands out by offering:

• Component-level experimentation, ideal for modern frontend frameworks
• Real-time updates without redeploying code
• Advanced user segmentation and personalization capabilities
• Easy API-first integration with Strapi

This Strapi's guide walks you through adding experimentation on top of Strapi content, without complex development cycles.

Additionally, Croct provides Strapi CMS integration templates to help you get started quickly with minimal setup.

Conclusion

A/B testing is a crucial tool for developing products and websites with higher conversion rates. By combining Strapi with Croct, you can empower your team to experiment faster, personalize content at scale, and base decisions on data rather than guesswork.

If you’re ready to start running A/B tests and personalizing your Strapi-powered website, Croct is the easiest way to get there. Start planning your experiments today and see how it can take your website to the next level.