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Thebest AI website builderscan help you generate a fully-functioning website faster than ever before.
But blindly implementing AI suggestions without a thorough quality assurance analysis and A/B test can hurt your business.
A/B testing also ensures your AI-powered site meets crucial compliance standards.
To effectively A/B test your AI-powered website, consider implementing these best practices:
1.
Define clear objectives
Start by identifying what you want to achieve with your A/B testing.
Having a clear objective helps guide the design and implementation of the A/B test.
This provides a clear target to work towards and measure success against.
Communicate your objectives clearly to all stakeholders involved in the A/B testing process.
Use simple language and visuals to ensure everyone understands the purpose and expected outcomes of the test.
Getting buy-in and alignment upfront will make the entire process smoother.
- grab the right metrics
Choose key performance indicators (KPIs) to measure the success of your test.
As the saying goes, “measure what matters”.
check that the metrics you choose are relevant to your specific test and will provide actionable insights.
Avoid tracking too many metrics which can muddy the results.
Identify a primary metric that will be the key indicator of your test’s performance.
Verify the data is being captured accurately before launching the test.
Segment your audience
Develop a hypothesis for each element you plan to test.
A hypothesis is a prediction you create prior to running an experiment.
It states clearly what is being changed, what you believe the outcome will be, and why.
A good hypothesis is specific and testable.
Use a format like: If [variable], then [result], because [rationale].
Grounding hypotheses in research and data will make them stronger.
Do your homework first by analyzing existing site data, collecting user feedback, and reviewing competitor sites.
Insights from these activities can inform your hypotheses.
Prioritize and limit the number of hypotheses.
Focus on those you believe will have the greatest impact.
Spreading yourself too thin by testing too many things at once will limit your ability to find meaningful results.
Aim for quality over quantity.
Test one variable at a time
For conclusive results, only test one element at a time.
You’d have to run follow-up tests to isolate the effect of each change.
This allows you to identify exactly what works and what doesn’t.
Focus on your primary metric, but look at the data as a whole.
Segment your results by user properties like traffic source, machine, or location to uncover deeper insights.
Determine whether your results are statistically significant using an A/B significance test.
Doing so will tell you the probability that your results are real and not due to chance.
Most A/B testing tools have this feature built in, or there are plenty of free options online.
If a variation is statistically significant and aligns with your hypothesis, congratulations!
you’ve got the option to confidently implement the change knowing it’s an improvement.
If not, don’t fret.
A negative result is still valuable learning.
It’s just as important to know what doesn’t work as what does.
