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When it comes to measuring success, people tend to gravitate toward numbers. Numbers feel concrete. Numbers seem to tell a clean story. That’s why when setting Objectives and Key Results (OKRs), many insist that key results must be strictly quantitative. But here’s the truth: qualitative data is still data—it’s just a weaker indicator. And sometimes, a weak indicator is the only one you have, especially in the early days of a product.
Understanding Quantitative and Qualitative Data
Quantitative data is what most people think of when they hear “data.” It’s numbers, percentages, and statistics—things you can measure precisely. Additionally, it is available in a large enough sample size to be considered reliable (a.k.a. statisticly significant.) If you want to track revenue, user growth, or conversion rates, you use quantitative data. It’s clear and easy to compare over time.
Qualitative data, on the other hand, is descriptive. It’s about what people say, how they feel, and what they want. It comes in the form of interviews, surveys, user feedback, and observations. And while it’s not as neatly measurable as quantitative data, it carries more information per data point. A single user interview can reveal insights that no amount of raw numbers ever could.
What Do You Want to Do With Your Data??
The first question to ask is, “How will I use my data?” My rule of thumb is to use qualitative data to uncover problems and needs, while quantitative data helps validate solutions. Solutions originate from your creative thinking, shaped by the data you’ve gathered—including secondary research.
In the early days of a product, you don’t have much to measure. Maybe you don’t have enough customers to generate trustworthy quantitative data. Maybe the metrics you do have are so volatile that they don’t provide a stable signal. In these cases, qualitative data fills the gap.
Imagine you’re building a new product, and you survey 500 potential users. You find that 63% of them say they would use your product. That’s an interesting statistic, but does it really help you make a decision? Not really. It doesn’t tell you why they would use it, what problem it solves for them, or how urgent that problem is.
Now imagine you instead talk to 12–25 people across different market segments. They all tell you they desperately want the same feature. They explain why it matters, how they would use it, and how they currently struggle without it. That information is invaluable. It guides product decisions, shapes marketing strategy, and helps you prioritize what to build.
The Right Way to Use Qualitative Data in Key Results
That said, there are still rules. Progress and tasks are not results. “Talk to 15 users” is not a key result; it’s an activity. Instead, key results should focus on what you learn or achieve from qualitative insights. Here’s how you do it:
- Turn themes into measurable changes. If users consistently say they are confused by your onboarding, a key result might be: “Decrease reported onboarding confusion in user interviews from 80% to 30%.”
- Use qualitative data to refine your quantitative targets. If interviewees all demand a particular feature, you might set a key result like: “Launch feature X and see 70% of beta users adopt it within the first month.”
- Track sentiment shifts over time. A key result could be: “Increase user-reported satisfaction with our pricing model from 2/5 to 4/5 in surveys.”
Final Thought: Don’t Dismiss the Power of Stories
Qualitative data often comes wrapped in stories—real accounts from users who are struggling, excited, frustrated, or inspired. These stories are powerful. They help teams align, they bring urgency to problems, and they reveal opportunities that numbers alone never could.
So yes, use qualitative data for key results. Just make sure it’s tied to real insights and changes in behavior. Some data is always better than no data—and when it comes to making decisions, rich qualitative insights often beat shallow numbers.