---
title: "Multivariate testing"
source: 18F Guides
source_url: https://github.com/18F/guides
licence: CC0-1.0
domain: engineering
subdomain: 18f-guides
date_added: 2026-04-25
---

## How to do it{#how-multivariate}

1. Identify the call to action, content section, or feature that needs to be improved to increase conversion rates or user engagement.
1. Develop a list of possible issues that may be hurting conversion rates or engagement. Specify in advance what you are optimizing for (possibly through [design hypothesis]({{ "/methods/decide/design-hypothesis/" | url }})).
1. Design several solutions that aim to address the issues listed. Each solution should attempt to address every issue by using a unique combination of variants so each solution can be compared fairly.
1. Use a web analytics tool that supports multivariate testing, such as Google Website Optimizer or Visual Website Optimizer, to set up the testing environment. Conduct the test for long enough to produce statistically significant results.
1. Analyze the testing results to determine which solution produced the best conversion or engagement rates. Review the other solutions, as well, to see if there is information worth examining in with future studies.

<section class="method--section method--section--government-considerations" markdown="1" >

## Government considerations{#con-multivariate}

No PRA implications. The users are not being asked questions, so the PRA does not apply. See the methods for
[Recruiting]({{ "/methods/fundamentals/recruiting/" | url }}) and [Privacy]({{ "/methods/fundamentals/privacy/" | url }}) for more tips on taking input from the public.
</section>
