Qualitative vs. Quantitative Data for Product Managers
Did you know that the right brain/left brain theory is a myth? It is impossible to only use one side of the brain. For example, musicians are considered creative, right-brained folks. But reading music and following rhythmic patterns requires analytical — or left-brained — thinking. This myth applies to any profession but especially product management. You cannot rely solely on numbers or feelings to make decisions. You need both.
Qualitative and quantitative data work in concert — with you interpreting the details needed to compose something sonorous.
Let's start with some definitions. Qualitative data is non-numerical information, often expressed in narrative form. It might include customer feedback or observations. Quantitative data refers to anything you can measure using numbers, such as usage metrics or poll results.
Now you might think qualitative data is too anecdotal — especially in the age of data-driven product management. Many of us are taught that hard data is more reliable and closer to the truth. And that the only way to drive value for customers and the company is to use metrics (not emotions) to guide all of your product decisions.
It is true that you need numbers, but numbers only tell part of the story.
You also need to incorporate insights from real humans. Your job is to build a product that makes a difference in the lives of real people — one that generates genuine love and adoration. But if you do not deeply understand what your customers think, feel, and hope for, it is tough to deliver a solution that truly meets their needs.
Great product managers want to understand customers, uncover problems, and validate solutions — gathering as much knowledge as you can to paint a full picture that informs your go-forward direction. Effectively using qualitative and quantitative data together starts with knowing the differences between each type and how they supplement one another:
What is the purpose?
Qualitative data is used to understand your customers on a personal level. It is a glimpse into their individual feelings, experiences, and motivations. What problems are they trying to solve? What frustrates them? What delights them? How would they rate their overall experience with your product?
Quantitative data is used to precisely measure customer behavior or expectations. It is useful for identifying patterns among a broader population of users. How often do customers take a certain action when using your product? What features do they rarely use?
What are some collection methods?
Collect qualitative data with open-ended questioning or observations. Common collection methods include:
Collect quantitative data with closed-ended experiments or questioning. Common collection methods include:
How do you combine the information?
Gather qualitative data to explore the unknown. Insights into the real-life experiences of your customers can help you identify new opportunities to pursue. You might sit in on customer success calls and read through support tickets to uncover unmet needs and form ideas for how to solve them. But because this information is subjective and based in opinion, it will need to be validated — that is where quantitative data comes in.
Gather quantitative data to verify qualitative findings. In the example above, maybe you have identified one or two new features that you think might benefit your customers. You could then conduct a quick poll or survey to ask customers whether or not they would use these features and if so, how often. The results confirm or deny your assumptions — helping you determine the right opportunities to pursue.
What are the outcomes?
Qualitative data helps you bring empathy into product decisions. Numbers will never tell you exactly what your customers really want or why they take certain actions. But when you relate to the pain and experiences of the real people using your product, you set yourself up to deliver a truly valuable solution.
Quantitative data helps you bring objectivity to your product decisions. Opinions and emotions can vary from person to person. And some customers are louder than others — distorting your perception of how widespread certain customer experiences really are. Quantifying customer feedback helps you prioritize what to build next.
Qualitative and quantitative data complement each other — each providing what the other is missing.
For example, you might notice patterns in quantitative data that you already track — maybe a majority of your customers are not using a certain feature. You could then layer in qualitative research, such as 1:1 interviews, to uncover why customers are not using that feature. This will help you better understand how to solve the problem.
But remember that the insights you gather are only beneficial if you know what you want to achieve. Define a bold vision and stay true to it. Data might not tell you where to go, but it can help you evaluate the pros and cons of potential approaches you might take to reach that vision.
How do you use qualitative and quantitative data together to build products?
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