A/B testing doesn’t need to be overly technical or intimidating. At it's simplest, it’s about making small changes and paying attention to how users respond.
If you’re thinking about running A/B tests in your community, here are a few best practices to keep in mind:
Start with a clear goal
Before you make any changes, decide what you’re trying to improve. For example:
- More visitor-to-member signups
- More clicks on a banner or CTA
- Increased engagement with a specific post or feature
Pick one primary metric per test. Keeping the focus narrow makes results easier to understand and act on.
Don’t forget to collect baseline data before starting your test. When applicable, make sure to account for factors like seasonality.
Create a simple hypothesis
Good tests start with a clear “why.” Ask yourself what you expect to happen and why. For example:
- If we update the homepage banner to highlight community value, more visitors will sign up.
- If we change the CTA text to be more action-oriented, we’ll see more clicks.
With a clear hypothesis, even a “losing’ test helps you learn something useful.
Change one thing per test
It’s tempting to tweak multiple elements at once. Introducing too many variables makes it hard to know what made the difference. Try to limit each test to one variable, such as:
- Banner messaging or imagery
- Button color or label text
- Headline wording
- CTA placement
Small, focused tests usually produce the clearest insights.
What’s worth testing?
Testing opportunities are nearly limitless, but here are some ideasfor getting started:
- Community banners: headlines, visuals, or value-focused messaging
- Messaging: benefit-driven vs. feature-driven language
- Buttons and CTAs: color, size, and copy (e.g., “Join Now” vs. “Get Started”)
The above are highly visible elements and often influence first impressions and actions.
Give the test time
Make sure you give your test enough time to collect meaningful data. Ending too early can lead to misleading conclusions. Look for steady trends rather than short-term spikes. Make use of your historical data.
Remember – A/B testing is about continuous improvement. Small tests, run consistently, can go a long way.
