The scientific method is the basis to improve a website’s conversion rate. The scientific method used in the context of a website is observing a website, measuring aspects of the website, experimenting based on measurements, formulating improvements based on measurements, testing, and then implementing improvements.
One of the simplest but also effective ways to improve a website’s conversion rate is A/B testing. The way A/B testing works is that you show a website’s visitors two different versions of a website: website version A and website version B. In A/B testing you choose part of the website that you want to change (copy, navigation bar, etc.), which is called the variable. You then pick two values of the variable that you want to test. The website with the first possible value is A (the control), and the website with the second possible value is B. If website version B leads to more conversions then website version A, you can change the final website to website version B.
If you want to run A/B testing or some of the more advanced testing methods talked about later in this article, you will need to decide some website metrics to measure and analyze. Website metrics could be what you view as positive actions taken on your website such as downloads or leads generated. They could also be positive website interaction such as time on site. The website metric could directly be the business performance such as revenue generated. Determining what metrics that you want to use is crucial to creating useful testing.
Multivariate testing is a more complicated version of testing than A/B testing. Multivariate testing uses many different variables when determining the highest value page. Multivariate testing compares multiple variables together and determines the highest value set of variables. Multivariate testing not only gives you the best set of variables to use but also gives you information on how much each variables influences the success of each variation.
There are some negative influences that can cause your testing to give you incorrect results. These incorrect results can occur because of sampling distortion, selection, history, and instrumentation. Sampling distortion occurs when you did not obtain enough visitors to make an accurate test. Compared to A/B testing, with multivariate testing, you will need more visitors to make an accurate conclusion. Selection problems occur when changes in variables extraneous to the test cause the test to give an invalid conclusion. History errors occur, when over the course of time, the conversion ratios change because the environment where the test is occurring has changed. Instrumentation errors occur when the technology or methodology of your testing is not properly set up, and the results of your test are invalid.
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