A/B Test Tracker

A/B testing, or split testing, is a powerful method for making data-driven decisions. It involves comparing two versions of a product, webpage, or feature to see which one performs better against a specific goal. However, running multiple tests can quickly become disorganized. This A/B Test Tracker is a simple tool designed to help you log your experiments, track their performance, and see the results in one place. It calculates conversion rates and uplift for each test, providing a clear, at-a-glance view of your optimization efforts. By keeping a structured log of your tests—both winners and losers—you build an institutional memory of what works, leading to smarter, more effective product improvements over time.

1. Log a New Test: In the "Log a New A/B Test" card, enter a descriptive name for your test and state the hypothesis (what you expect to happen). Click "Add Test".
2. Enter Your Data: For each test, you will see two sections: Variation A (your control) and Variation B (your challenger). For each variation, enter the total number of visitors (or users) and the number of conversions (or goal completions).
3. Review the Results: The tool will automatically calculate the conversion rate for each variation in real-time.
4. Analyze the Uplift: The footer of each test card shows the "Uplift"—the percentage difference in performance between Variation B and Variation A. A positive number (green) means your challenger won, while a negative number (red) means the control performed better.
5. Manage Your Tests: You can add as many tests as you like. Use the trash can icon to remove a test from your log.

• Organized Tracking: Provides a central place to log all your A/B tests, preventing insights from getting lost in spreadsheets or documents.
• Clear Performance Metrics: Automatically calculates conversion rates and percentage uplift, making it easy to determine the winner of each test.
• Improved Decision Making: By tracking results over time, you can identify patterns and make more informed hypotheses for future experiments.
• Fosters a Testing Culture: A simple, accessible tool encourages the whole team to engage with experimentation and focus on data-driven outcomes.