Most sites, especially small ones with a small audience, do not need A / B testing. You already know what best practices are accepted in your industry, and A / B testing reveals the tweaks you can make along these lines. Large and small sites If you have a large site with more than 10,000 visitors per month and hundreds of pages, you can run an A / B test to find out what works best in your user test sample. Useful for. If you have a larger site, A / B testing is a must. Establishing a general baseline If your site has been around for quite some time, you need to establish a general baseline for your site's traffic.
Once you have this baseline, you're ready to ghost mannequin effect evaluate the elements of your website and what needs to be changed for effective testing. advertisement Continue reading below Determining the elements to test Indeed, this is a step that requires trial and error in some tests to be correct. If red seems to be better than blue for the industry, you can change the button color during A / B testing to see which one works best. If the phone's CTA is green instead of red and you don't think it conveys the appropriate action in color, the test isn't an assumption about what the user will respond to, but what the user actually responds to. Is shown. This is an important difference.
You don't know what your customers are reacting to unless you talk to them or do a more detailed analysis. And all you are doing is just a guess. A / B testing turns this guess into a precision instrument. If the test is successful, you will know what the user is reacting to. goal setting You also need to set goals for the modified results. advertisement Continue reading below If you don't have at least some thought of what you want to do with these results, you're flying blindly. Again, don't make any assumptions. Making assumptions is not a good idea at any part of the process. The overall reason behind A / B testing is to make sure you have the test results to back up your changes before you do the testing. Consolidate assumptions into concrete data points. And that data point is compelling.