Multivariate analysis methods are used in the evaluation and collection of statistical data to clarify and explain relationships between different variables that may be associated with this data.
Multivariate tests are always used when more than three variables are involved and the context of their content is unclear. The goal is to detect a structure on the one hand, and to check the data for structures on the other hand.
In the context of the usability of a website, multivariate analysis methods can be used to systematically increase usability. While A/B tests always isolate only one web page, multivariate methods show the relationships and interactions of several elements within a web page. The expressiveness depends on which and how many elements of the website are used. All elements of the website that enable the user to interact with the website via the user interface are generally considered variables. This includes in particular those that have an impact on the conversion rate.
Originally, multivariate test and analysis methods were used in statistics to uncover causal relationships. Since manual calculations are very complex, the methods only became practicable in other fields of application with the development of corresponding hardware and software. Multivariate analysis methods are used today in very different areas:
Today, multivariate analyses are usually carried out using software in order to deal with the huge amounts of data and to monitor the changed variables in practical applications such as usability tests. However, multivariate tests can also make a significant contribution to improved user-friendliness on a smaller scale.
Multivariate methods can be subdivided according to different aspects. First of all, they are differentiated according to whether a structure is to be discovered or checked with them. The structure-determining methods include the:
Structural review procedures include, among others, the:
A multivariate test of a web page can be presented in the following simplified way. Elements such as headlines, teasers, images, but also buttons, icons or background colors have different effects on user behavior. Different variants of elements are tested. The test would initially identify these elements and show different users differently designed elements. The aim would be to obtain data on the effects of the changes in terms of conversion rate or other factors such as retention time, bounce rate or scrolling behavior compared to other sets of elements.
As a quantitative method, multivariate analysis is one of the most effective methods of testing usability. At the same time, it is very complex and sometimes cost-intensive. Software can be used to help, but the tests as such are considerably more complex than A/B tests in terms of study design. The decisive advantage lies in the number of variables that can be considered and their weighting as a measure of the significance of certain variables.
Even four different versions of an article's headline can result in completely different click rates. The same applies to the design of buttons or the background color of the order form. In individual cases, it is therefore worth considering from a multivariate perspective also financially, especially for commercially oriented websites, such as online shops or websites, which are to be amortized through advertising.