Overview
A/B testing (opens in new tab) is a randomized experimental method comparing two versions of a product element to determine which performs better against defined metrics. In GLIDR, users define their test audience, establish success criteria beforehand, run the experiment, and analyze results to inform product updates.
Key Concepts
Purpose: Testing determines whether small product changes—such as button color or website layout—meaningfully improve specific metrics like conversion rates or bounce rates.
Core Process:
- Define your research question and goal
- Conduct background research using analytics tools
- Establish metrics and minimum meaningful difference thresholds
- Calculate required visitor volume and test duration
- Create variations (A = control, B = modification)
- Analyze data and draw conclusions
- Report findings to stakeholders
Important Considerations
Timing: Tests typically run from several days to two weeks, depending on traffic volume. Running one test at a time ensures accuracy.
Statistical Requirements: For online testing, a 1-2% difference justifies changes; offline testing may require 10-15% difference. Ensure sufficient sample sizes and representative audience distribution across key demographics.
Limitations: A/B testing (opens in new tab) works poorly for novelty effects, multiple simultaneous changes, or missing features.
Potential Biases: Insufficient test duration yields skewed results. External variables affecting outcomes should be documented and accounted for during analysis.