What is A/B testing, and how do you implement it?

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A/B testing is a method used to compare two versions of a webpage, app feature, or marketing element to determine which performs better. It helps make data-driven decisions by showing version A (the control) to one group and version B (the variation) to another, then analyzing the results.

Purpose:

To optimize user experience, increase conversions, or improve engagement by testing changes like button text, layout, pricing, or images.

How to Implement A/B Testing:

  1. Define Your Goal:
    Identify what you want to improve (e.g., click-through rate, signups, purchases).

  2. Create Variations:
    Build two (or more) versions—A (original) and B (modified).

  3. Split Your Audience:
    Randomly divide users into groups. Each group sees only one version.

  4. Run the Test:
    Use a consistent time frame to gather reliable data.

  5. Measure Performance:
    Track key metrics (conversions, time on site, etc.) for both versions.

  6. Analyze Results:
    Use statistical methods to determine if the difference is significant.

  7. Implement the Winner:
    If one version clearly outperforms the other, make it the default.

Tools Commonly Used:

  • Google Optimize (deprecated but previously popular)

  • Optimizely

  • VWO

  • Adobe Target

  • In-house custom scripts (using JavaScript and analytics)

Example:
Test two headlines on a landing page. If version B leads to 20% more signups than version A, and the difference is statistically significant, B is likely the better choice.

A/B testing is key to continuous improvement, reducing guesswork and boosting outcomes through real user data.

Read More

What role does feedback play in iterative UX design?

What are common methods of UX research?

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