Skip to main content

Quantitative research: Multivariate test

A multivariate test is a logical continuation of an A\B test topic. Instead of comparing only two variants (A\B test) you can compare more than two - multivariate test (MVT).

Image credit.


Multivariate testing vs A\B (split) testing ?

The main difference of multivariate tests is that you can simultaneously test several elements of a page instead of testing one thing at the time (A\B test).

Let's imagine you have a headline and a button in two versions. You want to find out which combination of a headline and a button will lead to a higher conversion. With MVT you can compare each version of a button and each version of a headline. This means you will have four variants to test.

What's the difference between multivariate test and pairs of A\B tests ? 

Thing is, you need to run A\B test in isolation. To get clear, representable results you need to run one A\B test at a time on a single page. Which brings in certain limitations.
Multivariate tests normally allow you to test more variants at the same time. That will allow you to get results quicker.

Disadvantages ? 

The more options you'll test the more fragmented your traffic distribution is going to be. In a case of an A\B test your traffic split is 50\50. While if you want to test four options in a multivariate test your traffic split will be 25\25\25\25. To get reliable results - traffic groups should contain enough participants (sample size). In a case of an MVT, this requirement could be fulfilled only by a large amount of traffic.

When do A\B test and when do MVT ?

For major changes and drastically different options - make an A\B test.
For marginal improvements and conversion optimization - make an MVT. 

Have a lot of traffic and a lot of ideas ? - make an MVT. 
Have one metric to improve and best guess how ? - make an A\B test. 

Start with an A\B test, because it's simple. Continue with MVT to reach top conversion. 

An hour long webinar on A\B and MVT: 

Read more about multivariate tests:

Popular posts from this blog

PM's AI toolkit - OpenAI Codex and Google Jules

  If I'd try Jules first - I'd have a totally different impression of the state of coding agents today. A much worse impression. Here's why.

Product management and operations tools - Aha.io review

  Aha.io is the latest tool I had a chance to review and one of the most sophisticated product discovery and management software available on the market today.

OpenAI Codex Tips: A Practical Guide for Product Managers and Non-Engineers

  I have been using Codex more and more recently. Not in a “look, AI wrote me a todo app” kind of way. More like: I need to ship my actual product, fix real bugs, improve real tests, and not spend my evenings arguing with a robot about why it deleted half of my codebase.