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More than just answering a one-off question or settling a disagreement, A/B testing can be used to continually improve a given experience or improve a single goal like conversion rate optimization (CRO) over time.


Collect data: Your analytics tool (for example Google Analytics) will often provide insight into where you can begin optimizing. It helps to begin with high traffic areas of your site or app to allow you to gather data faster. For conversion rate optimization, make sure to look for pages with high bounce or drop-off rates that can be improved. Also consult other sources like heatmaps, social media and surveys to find new areas for improvement.


Create different variations: Using your A/B testing software (like Optimizely Experiment), make the desired changes to an element of your website or mobile app. This might be changing the color of a button, swapping the order of elements on the page template, hiding navigation elements, or something entirely custom. Many leading A/B testing tools have a visual editor that will make these changes easy. Make sure to test run your experiment to make sure the different versions as expected.


Larger sites and apps often employ segmentation for their A/B tests. If your number of visitors is high enough, this is a valuable way to test changes for specific sets of visitors. A common segment used for A/B testing is splitting out new visitors versus return visitors. This allows you to test changes to elements that only apply for new visitors, like signup forms.


Keep in mind that even simple changes, like changing the image in your email or the words on your call-to-action button, can drive big improvements. In fact, these sorts of changes are usually easier to measure than the bigger ones.


If you're testing something that doesn't have a finite audience, like a web page, then how long you keep your test running will directly affect your sample size. You'll need to let your test run long enough to obtain a substantial number of views. Otherwise, it will be hard to tell whether there was a statistically significant difference between variations.