What it takes to set up meaningful tests – and what you should watch out for when analyzing the results.
Why This Topic Matters Right Now
Personalization, performance, and user experience are the cornerstones of any successful digital strategy. But all of it relies on one solid foundation: data.
To make informed, data-driven decisions, you need structured testing – and a sound understanding of how to interpret the results. In this blog post, we’ll show you how to properly build, run, and evaluate A/A, A/B, and multivariate (MV) tests – and how trbo can support you in the process.
1. Solid Structure: No Valid Test Without KPIs
Before setting up any test, ask yourself the most important question:
What do you want to learn from this test?
A clearly defined hypothesis is your starting point. And every hypothesis needs the right Key Performance Indicators (KPIs) to make it measurable.
Common KPIs include:
- Conversion rate
- Click-through rate (CTR)
- User value
- Time on site
- Scroll depth
- Interactions with specific elements
Keep in mind: Not every initiative has a direct impact on conversions. Many tests focus on higher-level goals, such as increasing the visibility of product recommendations, building brand trust, or boosting site engagement.
Pro tip: Define your KPIs before the test begins – and make sure they directly align with your hypothesis. That’s the only way to ensure a clean setup and valid analysis.
2. A/A Tests: The Underrated Check Before Real Testing
What is an A/A test, anyway?
An A/A test compares two identical versions – “A” vs. “A”. The goal isn’t to spot a difference (there shouldn’t be one), but to confirm that your tracking and test setup are working properly.
A properly run A/A test should:
- Show no statistically significant differences between groups
- Help you identify potential tracking errors
- Lay the foundation for future A/B or MV tests
Why does this matter?
A broken test setup can lead to incorrect results – and bad decisions. Think of an A/A test as a technical dress rehearsal. It’s highly recommended before launching anything major.
3. A/B vs. MV Tests: When to Use Which
A/B Tests
One variation is tested against the original.
Example: You test a new CTA button color vs. the current one. Simple, quick, and effective for isolated changes.
MV Tests (Multivariant Testing)
You test multiple variants at the same time – for example, three different placements for a recommendation box (top, bottom, sidebar). A control group can also be included as one of the variants.
At trbo, we often use MV tests to also measure clicks and impressions in the control group. That way, we get a full picture of all variants – not just those with visible changes.
4. How to Analyze Test Results Correctly
The key: a technically sound setup.
All variants must track the same KPIs on the same elements and events – that’s the only way to generate reliable results.
What to keep in mind:
- Ensure comparability: Don’t test “recommendation at top” vs. “recommendation at bottom” in one go. Instead, compare “top vs. none” and then “bottom vs. none” in separate tests.
- Break tests into smaller pieces: Many small, focused tests often yield faster and clearer insights than one large, complex one.
- Watch significance levels: The more variants you test, the more data and time you’ll need to reach statistically meaningful results.
Quick Checklist for Valid Testing:
- Hypothesis defined?
- KPIs set?
- A/A test completed successfully?
- Technically consistent setup across all groups?
- Variants designed to be comparable?
- Test duration planned adequately?
5. How trbo Supports Your Testing
With trbo, you can create tests quickly, flexibly, and with full data reliability. Especially for multivariate testing, our features make it easy to compare variations – backed by clean tracking and clear KPI reporting.
With trbo, you’ll also benefit from:
- Advanced audience targeting
- Accurate event-based tracking
- Real-time reporting in the dashboard
- A/B, MV, and A/A testing options
In short: You test efficiently – and base your decisions on solid data, not gut feelings.
Conclusion: Small Steps, Big Results
Testing isn’t rocket science – but it does require care, planning, and a solid setup. Those who define KPIs early, maintain clean tracking, and run smaller, focused experiments will reach better outcomes, faster.
And that saves not just time – it leads to better business decisions.
Want to learn more?
👉 Download our whitepaper: “Using A/B and Multivariant Testing for Onsite Optimization”
👉 Dive deeper: Check out our case studies on successful tests with trbo customers!
About this article
This blog post was contributed by one of our Customer Success Managers. With direct insights from daily client work, our CSM team shares hands-on experience, practical learnings, and proven approaches from real-life optimization projects. Thanks to Minea for this valuable contribution.