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All Tutorials /Mixpanel

How To Do A/B Testing With Mixpanel

Updated on:
May 11, 2026
By:
Madhav Bhandari
Use this interactive demo to learn how to run and analyze A/B experiments in Mixpanel.

Quick summary

Mixpanel's Experiments feature lets you set up and analyze A/B tests directly within your analytics workspace, comparing control and variant groups across key board metrics. Using Mixpanel segmentation, you can drill into cohort-level breakdowns to determine which experiment variant drives better outcomes.


Steps

  1. Go to the Applications tab on the right side of the top navigation menu.
  2. Select Experiments from the Applications menu.
  3. Review the experiment analysis page, where you can set parameters for your tests and analyze results.
  4. Choose the experiment you wish to analyze — select from Tracked Experiments or create a Custom experiment to proceed.
  5. Select the specific board on which you want to measure the impact of your experiment.
  6. When prompted, select your control and variant groups for the test.
  7. For this example, define two user groups — one under and one above the age of 18.
  8. Configure your first group by setting its parameters.
  9. Then configure your second group to complete the experiment setup.
  10. Apply Mixpanel segmentation to break down statistics by a specific group or type, such as a Cohort.
  11. Expand your results to view a full breakdown and comparison of your A and B groups impacting your board metrics for a Cohort.

📌 Why this matters

A/B testing is essential for data-driven product teams that need to validate decisions with real user behavior rather than assumptions. Mixpanel's built-in Experiments feature eliminates the need for separate testing tools by letting teams set up, track, and analyze experiment results — including control vs. variant comparisons — directly inside their existing analytics workflow. The ability to segment experiment results by cohort gives product managers and growth teams granular insight into how different user groups respond to changes, accelerating iteration cycles and improving the reliability of product decisions. For teams already using Mixpanel for event tracking, running A/B tests natively means faster setup, unified data, and cleaner experiment reporting without context switching.
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