A/B testing is one of the most powerful tools in a product team’s arsenal. When done right, it removes guesswork and lets data drive your decisions. But there’s a right way and a wrong way to approach experimentation.
The Foundation: Statistical Significance
Before you run any test, understand that you need enough data to draw meaningful conclusions. A test that runs for a day with 50 users won’t tell you anything reliable.
Key Metrics to Consider
- Sample size: How many users do you need?
- Duration: How long should the test run?
- Confidence level: Typically 95% is the industry standard
Common A/B Testing Mistakes
1. Ending Tests Too Early
It’s tempting to call a test as soon as you see a winner, but early results can be misleading. Let your tests run to completion.
2. Testing Too Many Variables
If you change five things at once, you won’t know which change drove the result. Test one variable at a time for clear insights.
3. Ignoring Segment Analysis
Your overall results might hide important patterns. Always break down results by user segments.
What to Test
The best tests focus on high-impact areas:
- Call-to-action buttons: Text, color, placement
- Headlines: Different value propositions
- Pricing displays: Monthly vs. annual, with/without discounts
- Onboarding flows: Steps, content, timing
- Feature placement: Navigation, visibility
Rigora’s A/B Testing Features
With Rigora, setting up an A/B test takes minutes:
- Define your variants in our visual editor
- Set your targeting rules
- Choose your success metrics
- Launch and let the data flow
Our platform automatically calculates statistical significance and alerts you when a winner emerges.
Start experimenting today with Rigora’s powerful A/B testing suite.