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Author Archives: Mikolaj Kowalski

  1. Imagine running an A/B test where 100,000 users see your new design … or do they?  

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    Are your A/B Tests telling the truth?

    Many A/B tests count impressions/views/sessions on page load regardless of whether the user scrolls to that change or not. 

    This can lead to:  

    • Misleading or over inflated data – Counting users who never actually saw the change. 
    • Slower decision-making – Making choices based on incomplete data. 
    • Longer time to reach statistical significance – Delaying test conclusions. 

    Recommended Approach

    Here at Fabric Analytics, we recommend view-based tracking which ensures that only the users who could’ve been influenced by the test are counted. 

    By doing this, you: 

    • Improve data accuracy – You’re measuring real engagement, not just visits. 
    • Make better decisions – More reliable data leads to more confident optimisations. 
    • Potentially get faster results – Less overinflated data may help reach statistical significance sooner. 

    We implement view-based tracking manually in to each test using the JavaScript Intersection Observer API, which will detect when an element enters the viewport and then fire the tracking. 

    Example code: 

    Better Tracking = Better Results

    If you are using A/B testing in your field of work or know colleagues that do, I would highly recommend implementing view-based tracking as an additional event. You’ll soon find out how much that extra noise has been impacting your results.