GLOSSARY

A/B testing

A/B testing (or split testing), is a controlled experiment where users are divided into two groups: one group interacts with webpage version A (the control version), and the other interacts with webpage version B (containing one element variation).

Metrics like click-through rate (CTR), conversion rate, or engagement can be measured to determine which version of that element delivers better results.

Why is A/B testing important?

A/B testing enables precise optimization of individual elements within a digital experience. By isolating and testing a single variable—like the color of a button, headline copy, or the placement of a CTA—you can attribute performance changes directly to that specific element. This specificity eliminates guesswork, ensuring your decisions are based on measurable user behavior rather than subjective assumptions.

If you test multiple changes simultaneously (e.g., a new button color and a different headline), you risk introducing confounding variables that muddy the data. In this scenario, you wouldn’t know which change influenced the outcome. 

A/B testing also allows marketers to validate hypotheses before rolling out changes to a broader audience. For example, if you hypothesize that shortening your form from five fields to three will increase conversions, A/B testing provides the framework to prove—or disprove—that idea with statistical confidence. This level of precision not only informs design and copy decisions but also reduces risk, helping teams prioritize changes with the highest potential ROI.

Additionally, A/B testing fosters a culture of experimentation. Teams are encouraged to hypothesize, test, and iterate, which leads to continuous improvement. Whether you’re optimizing website landing pages, testing app interfaces, or email campaigns, A/B testing helps maximize ROI while offering actionable insights into user preferences.

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How to do A/B testing?

  1. Define your goal: Identify the metric you want to improve, such as clicks, form submissions, or sales conversions.
  2. Form a hypothesis: Decide which element you’ll test and why. For example, “We believe changing the call-to-action (CTA) color will increase button clicks.”
  3. Calculate the required sample size: Use a sample size calculator or an A/B testing tool to determine how many users you’ll need in each group to achieve statistically significant results. This step ensures that your test results will be reliable and not due to random chance.
  4. Create variations: Develop your control (version A) and variation (version B). The only change between each version should be the element elected on step 2.
  5. Split your audience: Use an A/B testing tool to divide your audience randomly and evenly between the two versions.
  6. Run the test: Ensure the test runs long enough to gather statistically significant data. Avoid stopping early to prevent skewed results.
  7. Analyze results and iterate: Review the data to determine which version performed better and implement changes accordingly.

Faster and easier A/B testing with Pathmonk

Pathmonk enables businesses to run A/B tests on its AI-powered micro-experiences, which are dynamic interactions triggered by visitor behavior. Here’s how it works:

  1. Set up the experiment: Create two variations of a Pathmonk micro-experience. For example, you could test different headlines, CTAs, or designs. These micro-experiences are tailored to engage visitors based on their behavior, such as time on page, intent signals, or exit intent.

  2. Traffic split: Pathmonk automatically splits traffic between the two variations, ensuring that some visitors see version A and others see version B. This process is seamless and doesn’t require manual intervention.

  3. Behavioral triggers: Each variation is triggered by specific user actions or intent, meaning the experiences are shown in real time to the right audience. For instance, visitors with high purchase intent might see a different message than those at the top of the funnel.

  4. Real-time analytics: Pathmonk provides detailed performance metrics for each variation. The dashboard highlights which experience drives better engagement, conversions, or other key goals, so you can make data-driven decisions.

  5. Iterate and optimize: Once the winning variation is identified, you can continue testing new elements or roll out the successful experience across your website.

Key benefits of A/B testing with Pathmonk

  • AI-driven personalization: Unlike traditional A/B testing tools, Pathmonk ensures that each experience adapts to the visitor’s intent and journey stage.
  • No traffic limitations: Pathmonk works effectively for websites with low traffic by leveraging AI to accelerate insights.
  • Ease of use: Setting up and running tests doesn’t require technical expertise or developer support.
  • Integrated insights: Results from A/B tests can inform broader marketing strategies, from ads to landing pages.

Pathmonk’s approach simplifies A/B testing by focusing on behavior-based, personalized interactions that make testing faster and more effective.

Pathmonk makes A/B testing accessible for teams of all sizes, offering actionable insights that help align user experience with customer preferences, all while boosting conversions effortlessly.

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FAQs on A/B testing

What tools can I use for A/B testing?

Popular A/B testing tools include Google Optimize, Optimizely, and VWO. However, if you want easier and faster testing, focused on increasing website conversions, you should consider Pathmonk.

How long should I run an A/B test?

The duration of an A/B test depends on several factors, including your website traffic, conversion rates, and the level of confidence you want in the results. As a general guideline:

  1. Reach statistical significance: You should run the test until you’ve collected enough data to determine a clear winner. This typically means reaching a confidence level of at least 95% to ensure the results are not due to random chance.

  2. Traffic and conversions: High-traffic websites may only need a few days to gather sufficient data, while lower-traffic sites may require weeks. The more visitors and conversions you have, the faster you’ll reach meaningful results.

  3. Avoid testing too long or too short: Ending a test too early may lead to unreliable results, while running it too long can waste time or introduce external variables (like seasonality). A good rule of thumb is to test for at least one full business cycle (e.g., a week) to account for day-to-day variations.

Can A/B testing hurt my business?

If not planned properly, A/B testing can represent misleading results, especially if the sample size is too small or the testing duration is too short. Careful planning and clear goals minimize risks.

What is the difference between A/B testing and multivariate testing?

While A/B testing compares two versions (A and B), multivariate testing analyzes multiple variations of several elements simultaneously to determine which combination performs best. A/B testing is simpler and best for smaller changes, while multivariate testing is suited for more complex experiments.

Can I test more than two versions at once?

Yes, this is called A/B/n testing, where “n” represents additional variations. However, testing more versions requires a larger audience to ensure statistically significant results for each variation.

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