Analyze and Rectify Mistakes on A Failed Split Test
A split test might be helpful to decide on which variant proved to be the best in compelling your target market to act and ensure a higher rate of conversions. However, what do you do when your A/B or A/B/n or split test fails in evoking the desired conversions?
One point to bear in mind is that you should not shy away from split testing even though they might occasionally fail. A split test can fail due to several reasons. If the different choices that you have offered that might number 2 or even more, fail to convince your potential customers to act since they are all un-exciting by design or quality, then that could evoke a failed response.
On occasion, you or your webmaster might be too confident of a particular design and might be shocked at the poor results of your split test. On the other hand, the displayed designs might simply be too similar to provide a conclusive result. You might not have indulged in any kind of analytical research while conducting the split test and would thus fail to notice any improvements in conversions based merely on visual differences.
In the above cases, your split testing could fail and you need to thoroughly analyze the actual reasons for that failure instead of losing hope on the testing aspect itself. You can use Google Analytics or CoreMetrics or Omniture or any other analytics tool to analyze the exact behavior of visitors on reaching that specific landing page. Utilizing the user behavior analysis tool will provide you with a clear picture on how visitors reacted upon visiting the particular web page.
Observing the results based on the above tools might help you to further fine-tune your landing page and engage in another split test. The best part is that indulging in split tests might enable you to realize that your existing web page does have the potential to boost conversion rates.
A failed split test might be demoralizing, but it should not put you off continuing with testing on a regular basis. Use the failed split test as a positive excuse to analyze the exact causes and to optimize your website for better conversions in the future.
