I’m not a data scientist. I don’t have a data degree or a fancy title. I did honours Maths in school, and I studied Maths for a year in college, but I wouldn’t exactly say I’m good with numbers.
What I am is a researcher. I love asking questions. I love hearing what people think, feel, and experience. That’s the beauty of qualitative research, it captures the emotions and stories behind user behaviors. But it can also be subjective. After all, humans are emotional beings.
As a Product Designer, I’ve come to realise that data comes in many forms. There’s the rich, detailed world of qualitative data from interviews and open-ended responses, and then there’s the hard, measurable world of quantitative data from events, analytics, and databases.
Both are critical for UX. It’s tempting to rely solely on qualitative insights because they’re relatable, human, and often easier to connect with. But when we’re designing products, we need to leverage both types of data to get the full picture.
- Does what customers feel translate into a measurable metric?
- Does what they say about how they use the product align with actual page views or clicks?
- How can we visualise this data to tell a clearer, stronger user story?
Combining qualitative and quantitative data ensures our decisions are grounded in both empathy and evidence. Below, I’ve outlined some of the data sources I use to connect the dots.
User behaviour metrics
User behaviour metrics give us an objective look at how people interact with our product. It’s measurable and repeatable, which makes it easier to spot trends and patterns.
- Task completion rates: Tools like Lyssna (or other usability platforms) can help validate prototypes or live sites. Set users a specific task, then measure:
- Time on task
- Success or failure rates
- Event tracking: Track critical user actions like clicks, form submissions, or video plays. This can tell you what users actually do versus what they say they do.
- Page views and click data: Visualise page views, bounce rates, and click-through rates to understand how users are navigating the product.
Compare this data to interview insights. For example, if users say they struggle to find a feature, does the click data show lots of back-and-forth navigation?
NPS and surveys
Surveys are a powerful way to gather both qualitative and quantitative insights. A simple NPS (net promoter score) survey asks users how likely they are to recommend your product, but you can go deeper:
- Jobs to be done (JTBD) surveys: Ask users two key questions:
- How important is this job to you? (Importance)
- How satisfied are you with how our product helps you do it? (Satisfaction)
The gap between these two answers highlights opportunities for improvement.
Customer support data
Your support team holds a treasure trove of insights. By looking at support data, you can uncover recurring pain points.
- Support ticket topics: Categorise tickets by themes or topics. What are the most common issues users face?
- Keyword analysis: Analyse the language users use in their tickets to identify patterns and frustrations.
- Visualise it: Use graphs or charts to show the volume and frequency of key issues.
Retention and engagement metrics
Retention data reveals whether users stick around, and engagement metrics show how actively they interact with the product.
- Churn rate: What percentage of users stop using your product? What can you learn from this?
- Retention metrics: Measure user cohorts to see how long people stay engaged.
- Engagement metrics: Look at usage frequency, feature adoption, and session duration.
These numbers can tell you a lot about what’s working and what’s not. For instance, if engagement drops after a certain step in the user journey, that’s a clue to investigate further.
Pulling it all together
The real magic happens when you combine these different data sources to tell a cohesive story:
- Start with qualitative research: Talk to users to understand their feelings, frustrations, and needs.
- Validate with quantitative data: Look for patterns in user behaviour, support tickets, and analytics that back up (or challenge) what you’ve heard.
- Visualise the story: Create user journeys, graphs, and charts that make the data easy to share and understand.
For example:
- If users say they’re struggling with navigation, does the click data show confusion?
- If users report frustration with a feature, do the support tickets reflect that?
- If customers say a particular workflow is critical, do the engagement metrics confirm that it’s being heavily used?
Final thoughts
You don’t need to be a data scientist to use quantitative data effectively (although knowing some basic SQL does help!). As a Product Designer, your superpower is connecting the dots between what users say (qualitative) and what they do (quantitative).
By bringing both perspectives together, you’ll tell richer, more accurate user stories, and design products that truly meet user needs.
