- A/B Testing Tutorial
- A/B Testing - Home
- A/B Testing - Overview
- A/B Testing - How it Works
- A/B Testing - Why to Use?
- A/B Testing - What to Test?
- A/B Testing - Process
- A/B Testing - Collect Data
- A/B Testing - Identify Goals
- A/B Testing - Create Variations
- A/B Testing - Run Experiment
- A/B Testing - Analyze Results
- A/B Testing - Tools
- A/B Testing - Multivariate
- A/B Testing - SEO
- A/B Testing - Interview Questions
- A/B Testing Useful Resources
- A/B Testing - Quick Guide
- A/B Testing - Useful Resources
- A/B Testing - Discussion
A/B Testing - Analyze Results
Once the experiment is completed, next step is to analyze the results. A/B Testing tool will present the data from the experiment and will tell you the difference between how the different variations on a web page performs, and also if there is a significant difference between variations, using the help of mathematical methods and statistics.
Example
If the images on a web page has reduced the bounce rate, you can decide whether it has a good conversion or not, once you upload more images on a web page. If you see no change in the bounce rate because of this, go back to the previous step and create a new hypothesis/variation to perform a new test.
Tools like VWO and Optimizely are used to run tests, but Google Analytics is best suited to run post-test analysis. This analysis is used to decide the way going forward. A/B Testing tools tell about the outcome of a test result, but there is a need to perform post analysis as well. To do post analysis you need to integrate each test with Google Analytics.
Both VWO and Optimizely provides built-in Google Analytics integration capability. The data for each test from both these tools should be sent to Google Analytics. By doing this, it enhances your analysis capabilities and ensures testing data. There is a possibility that your testing tool might be recording the data incorrectly, and if you have no other source for your test data, you can never be sure whether to trust it or not.