In our last article, we discussed how to optimize your UX research using qualitative analysis. This week, we’re covering how to improve the user experience using quantitative data. Both methods are crucial to fully understand how to improve the UI/UX design with direct feedback from real users. While taking a qualitative approach to research has advantages, the results don’t give you a complete picture and can be objective. Breaking down the numbers and using large data sets is necessary to improve your product’s UX and conduct comprehensive user experience research.

In the following article, we will discuss the basics of quantitative (quant) data, how quant differs from qual analysis, and UX professionals can use standard quantitative research methods to improve their product’s user experience.

What is a Quantitative Analysis?

Quantitative research refers to any methods that incorporate numbers. A successful quantitative analysis consists of using large data sets to put an actual number on your user research.

The analysis will lead to the UX professional having a better idea of ‘how much’ ‘how often’ or ‘how many’ users identify issues within the product design. For example, during usability testing, the research will find how long it takes for users on average to complete a specific task on a webpage. If the customer discovery is adequate, they will have an extensive data set to understand how easily users can navigate the site and if significant changes need implementing.

How is Quantitative Analysis Used in UX Research?

Methods including web analytics, A/B testing, tree sorting, and card sorting are ways to gather data to identify UI/UX design problems. One of the most common quantitative metrics used in UX research is bounce rate. Website analytic tools give researchers the average number of visitors who visit a webpage and leave rather than visiting any other pages.

Quantitative analysis is used to give UX professionals a non-biased, generalized view of the user’s experience. Any analysis that focuses on data is considered taking a quantitative approach to UX research.

The Difference Between Quantitative and Qualitative Research

Where quant data dive into the numbers, qualitative research gets a personal perspective of the user’s experience. Observations, thoughts, opinions, comments are all types of qualitative data that can’t be measured in an analytical dashboard.

Both methods have the same goal of understanding the user is experiencing the UI/UX design, but they take many different approaches. To gather sufficient qual data, UX professionals have to be skillful communicators and gain insight into how the user truly feels about the product.

Each method is essential. We don’t value one over the other instead view them as different tools that help us better understand the user experience.

Advantages of Quantitative User Research

  • Statistics are awesome! Reporting a 10% increase in traffic or a 50%/50% new to return visitor rate moves the needle for most people.
  • Quantitative data is easy to collect using technology.
  • Numbers aren’t objective or biased.
  • Large data sets offer significance that anecdotal accounts can’t.

Disadvantages of Quant Data Research

  • Numbers aren’t personal. You aren’t getting into the psyche of the user when looking at large data sets.
  • Surveys with yes or no answers can skew results.
  • In many cases, an interview or conversation can lead to a solution more accessible than data.
  • Anecdotal accounts can be more persuasive to some people. Hearing personal testimony rather than looking at numbers is more emotional and can be influential.

3 Types of Quantitative Testing Methods

Now that we understand the basic concept of quant analysis and how it differentiates from qualitative data, it’s time to go over some of the methods used in your research. Below are three of the most common ways to gather quantitative data to improve a product’s UI/UX design.

Web Analytics

Analytics is one of the most significant advantages of operating an online business. Tracking visitors, session times, bounce rate, and frequency of visits can all be analyzed using analytics. The most common is provided by Google Analytics, but many companies offer insights into various metrics measuring user activity on websites.

Collecting data from web analytics is done over time and should be readily available to UX professionals. However, using the metrics in a market research report, identifying problems, and implementing solutions are where most user experience researchers fail.  

A/B Testing

UX professions using A/B testing to research the user experience create two live versions of the same UI and test how each version performs. The process can be done using software specifically designed to test a user interface.

A successful A/B test will change one element and measure page performance. For example, an eCommerce site can test two different CTA buttons head-to-head and see which gets the most clicks.

Card Sorting

Card sorting consists of presenting participants with a list of content items and putting them into categories. Then, the participants are instructed to label the categories to make the most sense to them or are given pre-selected categories. A card sorting study can be conducted in person or using software.

Hopefully, the researcher will have a substantial number of categories and groups of content to create datasets that can be implemented in their product.

How to Use Quantitative Data to Improve the User Experience

Implementing solutions based on quantitative data can be challenging. With methods like A/B testing or card sorting, the solutions are relatively straightforward, but web analytics can be harder to interpret and create appropriate solutions. The key is to have a clear plan and objectives before conducting user research and diversifying your methods.

Balance is Key to Improve the User Experience

Utilizing multiple UX research methods with quantitative and qualitative data is critical to finding solutions that will improve user experience. Taking a multifaceted approach is the only way to conduct meaningful data to successfully create real-world results.

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