Quantitative data is numerical information that can be counted or measured, and is used to deliver answers to questions like "what" and "how many."

Collecting and analyzing quantitative data forms the foundation of a user-centric product design process.

What is Quantitative Data? (A Definition)

Quantitative data is numerical information that is gathered and analyzed to help inform product decisions. This type of data is used to measure and analyze specific aspects of a product in order to improve its usability and efficacy.

There are two main types of quantitative data: discrete and continuous.

  • Discrete data is data that can be counted, such as the number of users who click on a button.
  • Continuous data is data that can be measured, such as how long it takes a user to complete a task.

Who Uses Quantitative Data?

While there are careers that wholly revolve around collecting and analyzing quantitative data (think: data analysts and data scientists), it also plays a huge role in the world of product design. 

UX researchers and designers use quantitative data to measure a product’s usability, as well as its overall success and ROI. 

Because of this, anyone who is involved in the product design process, from user researchers to UX designers to success metric analysts, needs to have a firm understanding of quantitative data and how to use it effectively.

Examples of Quantitative Data in UX Design

Quantitative data can come in many forms. Some examples include:

  • Likert scale responses from user surveys
  • Completion rate percentages from usability test tasks
  • Conversion rate percentages from A/B tests
  • # of website visitors from web analytics data
  • Dollar amount of revenue from sales data

Quantitative data can also be gathered through more creative means, like card sorting or tree testing. But at the end of the day, it all boils down to the numbers themselves.

A photo of a laptop with visualized data graphs.

What’s the Difference Between Quantitative and Qualitative Data?

Quantitative data is numerical information that can be measured and analyzed. Qualitative data, on the other hand, is non-numerical information that can take many different forms, such as interviews and open-ended survey responses.

When Should I Use Quantitative vs Qualitative Data in My UX Design Process?

In general, quantitative data is best used to understand what users do, while qualitative data is best used to understand why they do it. Both are important for understanding how users interact with a product or service.

Why is Quantitative Data Important in UX Design?

UX design works best when it's data driven. This means that designers must be constantly looking at factual data in order to inform their decisions and iterations. 

Without quantitative data, it would be impossible to understand how users interact with a product, what areas need improvement, and whether or not a design is successful.

Types of Quantitative Data Collected in UX Design

There are many different types of quantitative data that can be gathered during the product design process. Here are a few…

User Demographics

This type of data can be used to understand who your users are and what their needs might be. Examples of quantifiable user demographics include:

  • Age 
  • Income level in dollars
  • Number of people in household

Task Completion Rates

Task completion rates refer to the percentage of users who are able to successfully complete a specific task. This metric is often used in usability testing in order to assess how easy it is for users to use a product.

Time on Task

Time on task is the amount of time it takes for a user to complete a specific task.

Number of Clicks

The number of clicks refers to the number of times a user has to click in order to complete a specific task.

Error Rates

Error rates refer to the percentage of users who make errors while using a product.

Conversion Rates

Conversion rates refer to the percentage of users who complete a desired action, such as making a purchase or signing up for a newsletter. This metric is often used to assess the success of a product or design.

Engagement Levels

Engagement levels refer to the amount of time that users spend interacting with a product. This metric is often used to assess the success of a product or design.

A photo of quantitative data graphs including CTR, quality score, and cost per conversion.

How to Collect Quantitative Data in UX Design?

There are many different ways that quantitative data can be collected. The most common methods include: 

  • User surveys are used to gather information about a variety of topics, such as user demographics, customer satisfaction levels, task completion rates, etc. 
  • Usability testing provides valuable insights into how users interact with a product and can help identify areas for improvement.
  • A/B tests allow designers to test different versions of a design to see which one performs better. 
  • Web analytics data can be used to track things like pageviews, unique visitors, bounce rates, and more. 
  • Sales data can be used to understand things like conversion rates, customer lifetime value, and other important metrics.

How to Analyze Quantitative Data

On its own, quantitative data is simply a number. 

It's not until you start to analyze it that it becomes truly useful. There are many different ways to analyze quantitative data, ranging in complexity, but some of the most common methods include:

  • Trend Analysis, which looks at data trends aggregated over a span of time.
  • Correlation analysis, which is a method of understanding the relationship between two variables.
  • Regression analysis, which is a method of understanding how one variable affects another. 
  • Multivariate analysis, which is a method of analyzing multiple variables at the same time.

Common Data Analysis Tools for UX/UI Designers

There are a number of different data analysis tools that UX/UI designers can use to understand how users interact with their product.

Some of the most common tools include:

  • Web analytics tools like Google Analytics and Adobe Analytics
  • A/B testing tools like Optimizely and Visual Website Optimizer
  • Usability testing tools like UserTesting and Crazy Egg
  • Data visualization tools like Tableau and Excel

Each of these tools has its own strengths and weaknesses, so it's important to choose the right one for your needs.

No matter which tool you use, the most important thing is that you're able to glean valuable insights from the data you collect.

What Are the Pros and Cons of Quantitative Data?

Quantitative data has a number of advantages and disadvantages...


  • It’s objective, when collected randomly and with a large enough sample size, so it is much less likely to be affected by the inherent biases in more personalized outreach and feedback methods.
  • It’s easy to collect and analyze, so designers can constantly refer to updated data to inform decisions.
  • It’s reliable, and doesn’t change based on researcher bias.


  • It can be difficult to interpret, since it is usually presented in the form of numbers and graphs, which can be tough to understand if you're not familiar with statistical analysis.
  • It can be misleading, since it only provides a snapshot of a product or service, and doesn’t take into account the full context of how users interact with it. 
  • The interpretation is easily affected by research bias, so the conclusions you draw from analyzing quantitative data may not be accurate.

Despite these disadvantages, quantitative data is still a valuable resource for designers. When used correctly, it can provide insights that would otherwise be difficult to obtain.