Data analysis

In UX, data analysis transforms raw data obtained through research into information and insights. It's a fundamental step that allows designers to make informed design decisions.

You can perform data analysis based on qualitative and quantitative data. However, unlike common belief, the analysis is not conducted when the research phase has been completed.

Instead, data analysis must start as early as possible. It will help you better define research goals, select the perfect sample size, and collect useful data.

Types of analysis

There are various ways of conducting data analysis as a UX designer, each with unique characteristics. Here's an overview of what type of data analysis to do at what point.

Quantitative analysis

Quantitative data is all about big numbers. It provides insights into how well users utilize and engage with your product. Common metrics you'll find here are success rate, time on task, and bounce rate.

As a UX designer, you can perform data analysis on quantitative data by looking for patterns. For example, many users might be stuck on a specific step in the user flow or only when using a mobile device.

Qualitative analysis

Qualitative data, on the other hand, focuses on individual user behavior. As a result, you'll work with users' opinions, needs, frustrations, and experiences.

You'll likely conduct user interviews or a design thinking workshop to gather the data. An example of data analysis for this type of data includes creating design challenges based on the interviews.

Useful resources

A Guide To UX Data Analysis For Actionable Insights - Invesp

UX Research Data Analysis: A Step-By-Step - Aela

Data-informed design: Getting started with UX analytics - Mixpanel