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A/B testing may very well be one of the easiest, quickest, and simplest ways to evaluate ideas how to optimize conversion rate on your website or application. To revise decision-making into a data-driven process that avoids subjective opinions and points to the right direction, you need to be aware of a few best practices to help you validate your hypothesis.
Keep in mind, though, that A/B testing is not a cure-all remedy. It is part of a wider ecosystem for optimizing your website and increasing conversion rates, which includes analytics, personalization, business strategy, marketing campaigns, and so forth. A/B testing is the simplest controlled experiment you can run to understand and prove which changes are worth it and will increase conversions.
The following high-level guidelines will help you avoid A/B testing pitfalls and make sure your experiments are gathering enough data that you can trust.
NOTE: If you encounter connection issues, see Troubleshoot connection issues.
You need a hypothesis to ensure your tests have a well-defined focus and goal. It is often the case that the hardest part is to decide what to test, why, and to what end.
Align the goal with the business strategy and analytics of your site.
In the example above, the focus is on the following:
Once you start the A/B test, you need to decide on the sample size and timeframe that will result in trustworthy data. Since the purpose of an A/B test is to have a winning variation, you need high-quality results, so that you can confidently choose the variation that optimizes the conversion rate of your goal.
In the example above, the significance and trustworthiness of data is based on the following:
An experiment is an attempt to understand what works or not. Therefore, your A/B test may turn out to be inconclusive, without a clear winner, or, simply, fail. Which is also good – you run an A/B test to learn from it, change your hypothesis or key metric, and try again.
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