While discussing analytical reports of a website’s performance, scientific method, testing and statistics are few very common words that you get to hear. However, before one starts with a testing or optimization strategy, it is important to determine an ideal method. Generally, the two most widely used techniques are A/B Split Testing, and Multivariate Testing.

Split testing

Split testing lets you test a variant of your running ad, text, background color, headline and etc against existing one and if the new one brings better results than the already running one then it is advised to repeat the process with new variants. However, irrespective of being simple and easy, split testing is no doubt a slow process fore many and this is why multivariate testing is preferred because it can change multiple elements on the page in one run and produce a winning combination in the end by combing the best elements.

It is believed that when multivariate optimization gets coupled with A/B testing, a wide open, elegant and powerful analytics and optimization platform gets formed. With their own set of pros and cons, both multivariate optimization and A/B testing can do bring efficient and productive results. Testing is undoubtedly, one of the most important, exciting and profitable activity need to analyze the effectiveness, performance and influential ability of a web page, and the thrill to get the surprising results from your changes is unparallel. Therefore, both split and multivariate testing should be used as an efficient testing tool as they both have their own times.

One needs to conduct split tests and multivariate tests to basically, draw a comparison between a set of page “recipes” or creative designs. In common language, this set is widely known as the “test battery.”

The Two Techniques: 

Many records would suggest the use of word ‘testing’ rather than “optimization”, but actually, they are quite different. However, if viewed deeply, the idea of testing is more appropriate for the concept of split testing, whereas optimization on the other hand, is associated with “multivariate optimization.”

Hence, the difference present between the two may not be extremely visible but it is actually significant. Testing is somehow limited to manual interaction, a hypothesis, experiment, and analysis of the results and split testing draw the comparison between two or multiple options of the same variable. Analysis is very simple in this case, it will either be fruitful or not.

On the other hand, optimization is a process that is conducted to find out the most appropriate collection of variables to get the most out of on some output. In case of carrying out a multivariate optimization, it is important to consider to variables with many options as the objective of the process is determining the best array of them. Here, analysis does not give clear results immediately. Instead, the output of the entire experiment will be an “optimized” page design, which is calculated mechanically with the help of mathematical algorithm.

Experts believe that in order to avail best results, one must engage both these techniques of Split Testing and Multivariate optimization as part of their thorough testing campaign. Both tests have their own set of benefits and situations when one is preferred over the other.

Split Testing: Pros & Cons:

Split Testing

A/B spilt testing is best to understand visitors’ behavior and priorities, while they visit your website. As a diagnostic tool, it can also help you locate the most specific problems within your website. Also, it lets you considerably test your assumptions about the “the most ideal” way to design or write a page. The whole process of split testing is considered useful for quick tests, engaging minor changes. They instantaneous and can be launched anytime, as they are totally free from the requirement of substantial creative development to start the test.

As far as the disadvantages are concerned, you may feel little tied up with the pace of the process as it considers only one change at a time. Also, it does not tell you anything about why a particular way is better than the other, irrespective of showing accurate results. Inefficient data collection is another thing that you may avail with A/B split testing process. Having multiple tests incessantly may bring a great deal of information which probably would of no use with other changed variables that you would want to test in future.

Multivariate Optimization: Pros & Cons

Multivariate Optimization

The entire process of multivariate optimization is a standalone way that potentially offers you the best result for your testing moolah. The skill to test several thousand or at times a million combination in one go would need extensive duration.

Multivariate algorithms provides sufficient amount of quality information which helps you determine all those positive as well as the negative aspects of your page. It clearly demonstrates the things on your page, bringing the attribute of efficiency and influential information for testing in future. It is believed that the most persuasive benefit that multivariate testing process exhibit is that the algorithm automatically decides the ideal page design for you.

This needs planning and time. Setting up a good multivariate test is a big job that needs time and efforts. As you plan to test a number of variables having multiple options for each, a lot more creative work is needed to be done to initiate the process. Moreover, special considerations should be laid on the design of the test and development of creative options. And finally, although you receive the best possible result of the experiment in the form of a “theoretical” best page design, it may not be ideally perfect and may require a little more tweaking further.


With the technique of split testing, you can test a variant of your running ad, text, background color, headline and etc against existing one, whereas with multivariate, the option of changing multiple elements on the page is possible. Both techniques have their own set of pros and cons and hence, should be campaign-specific.


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