Good user research requires two types of data: qualitative, and quantitative
Both quantitative and qualitative data provide specific insights into a product’s overall usability. However, without proper understanding of what kind of information you can get from these types of data, you won’t reap the benefits. Worse, you may read the data wrong and come to incorrect conclusions.
The most common mistake is to do only one or the other. Typically, qualitative data is sidelined. Without numerical data and user insights to back each other up, you end up with an incomplete picture of issues surrounding your product. So what exactly does this data look like, and what information can be gleaned from it?
What is Quantitative Data?
Quantitative data is numerical data used to gauge the user’s experience. It comes in the form of analytics, questionnaires, heatmaps, and other forms of testing that generates quantifiable answers. It is considered to be an indirect way to test usability.
These metrics help you find the “wheres” and “whats” of usability issues.
What is qualitative data?
Qualitative data is direct data derived from user testing. This type of data gives you the “how” and “why” of usability issues. By observing a test group, you can directly ask users why they have issues with the product.
By taking both sets of data, you can get a complete picture of the issues getting in the way of users’ experience on your platform. For example, heatmaps show 60 percent of users completing tasks with a new feature. With user testing, you can observe users trying to complete the new task.
While the majority of your test subjects complete the task, they unanimously report confusing directions which make them unsure of their next steps. Both sets of data in tandem give the complete picture of the issue, and a starting point with how to solve the problem.
Getting insights with your data to work together
Bounce rate
The bounce rate of a page is the rate at which users leave it without taking action, such as navigating to a new page or making a purchase. Various page analytic trackers can give you insights into a page that should be converting users, but is not.
Analytics can show you where the problem pages are. Meanwhile, user tests and interviews can illuminate why they aren’t converting.
User flow
Because of the nature of user testing, researchers have a limited pool of users to test at a time. A limited pool of users can mean the results vary, with no clear correlations. With complex tasks, such as a full user flow, and various ways to carry it out, user testing may give a sense of where to start.
However, detailed heatmaps and analytics tracking users through their journey can provide a snapshot of a larger data pool. Since quantitative data can gather a more accurate average of users, it can inform details that qualitative data may not be able to efficiently capture.
Evaluate your metrics
Download our tool to build qualitative questions for the metrics you collect.
Adoption rate
Adoption rate measures the percentage of people using a feature. It can gauge the success and viability of a feature, especially when something new is being introduced. While it can provide valuable insight into where a product overall needs work. What that work looks like will depend on the results of qualitative testing.
Without feedback from qualitative data, there are no clear actions or directions to take to fixing a low adoption rate. Conversely, understanding the reasoning for a high adoption rate can tell you more about the goals and behaviors of users. This information will direct future features’ development and ensure a high adoption rate.
Average time on a page
The average time on a page is self explanatory. Time spent on a page can give insight into how well a page is serving its purpose. For instance, a landing page meant to provide some information and a call-to-action may have a shorter average time than an item list on a store page.
Qualitative data can provide details on how effective a page is. Continuing the example, the short time spent on a landing page could be due to users finding the aesthetics uninteresting. The item list may have unclear calls to action that slow the user from adding products to their basket.
Customer Retention
Your customer retention rate is the percentage of customers who remain customers after a certain period of time. This can be a good metric for determining what keeps customers coming back. After conducting qualitative studies and making changes to a product, success can be determined by tracking customer retention rate.
If, over the course of several months, analytics are showing an increase in customer retention after implementing something new, the data from the studies could be applied for future projects. You will have a clearer picture of who the target persona is and how to predict their behavior.
Stronger together
A combination of qualitative and quantitative data can pinpoint problem areas and provide actionable solutions. Relying on one type without the other leads to an incomplete picture, and can lead you to develop solutions that don’t help users. The errors from incomplete research can become costly to fix down the road.
Quantitative and qualitative data work together
By conducting thorough research, you can not only fix current problems, but have a benchmark for future research and improvements to your product.
