A/B Test with Qt Insight!

Our new analytics solution, Qt Insight, is growing and evolving with feedback from our customers and partners. Our newest release on the product provides a tool to assist you with A/B testing different software versions to identify components and UI elements that may work better than others, as well as ones that don't necessarily have the impact you'd have hoped for. 

When creating your Comparisons, it's important to keep some things in mind, so for ease of use we have compiled them here for you. 

• Metrics: Use different metrics across the whole application to compare two different segments
• Screens: Compare how screens perform against different segments, or different screens
  
  

A practical checklist & best practices for A/B testing

1. Define your test variables and hypothesis
   Before jumping into Insight, you should have established clear test variables or have a clear hypothesis about what it is that you want to test. This could be something like "changing the color of a call-to-action button will increase conversions." Make sure you have a control group (Group A) and a test group (Group B) to compare. Distribute the different versions to each group in a way that makes it easy to differentiate them in Insight. The simplest way is to use different software versions, but creating differently named applications works as well.
   
   
2. Identify the metrics you want to compare
   Once you have your test variables or hypothesis, identify the metrics that will help you measure the impact of your test. For example, if you’re testing a menu structure change, you might track click events for entering the menu, users clicking on individual menu items, as well as the average usage time.
   
   
3. Use Insight to compare metrics
   Insight can help you compare the metrics you've identified between Group A and Group B. Easiest way to do this in Insight is to use the comparison tool to see the differences between the two groups. Make sure to eliminate other variables as best you can! Collect data for both groups during the same time period to avoid any seasonal or other fluctuations that could impact your results.
   
   
4. Analyze the results
   Once you have collected the data, analyze the results to determine if they support your initial hypothesis. Look for any statistically significant differences between Group A and Group B for the metrics you identified. If the results are inconclusive or if there is no statistically significant difference, you may need to go back to step 1 and refine your hypothesis further.
   
   
5. Implement the winning variation
    If the results show that your hypothesis was correct, implement the winning variation (Group B) across your entire user base. Monitor the metrics over time to ensure that the change continues to have a positive impact on your business.

Remember to follow best practices for A/B testing, including testing one variable at a time, testing for a long enough period to collect sufficient data, and avoiding bias in your testing.

Happy testing!

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