What Is the Difference Between Qualitative and Quantitative Data?

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The difference between qualitative and quantitative data is that qualitative data provides depth, while quantitative data offers scale.

Qualitative data is descriptive and non-numerical. It focuses on why things happen, capturing emotions, opinions, and motivations. Examples include open-ended survey responses, focus groups and interviews.

Quantitative data is numerical and measurable. It answers what, how much, or how often, helping identify patterns and trends. Examples include multiple-choice questions and raw numerical data.

In our example, Sarah’s first step was pulling up her analytics dashboard.

• Quantitative data told her that feature usage had dropped by 40%. That’s the what.
• Qualitative data from customer interviews revealed that users wanted to use the app, but found the interface confusing. That’s the why.

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What Is Qualitative Research?

Qualitative research helps you go beyond numbers and uncover the real motivations behind customer behavior. 

Some common qualitative research methods include:

• User Interviews – Speak directly to customers.
• Focus Groups – Invite customers to share their experience and opinions in a group.
• Observations – Watch how people use your product or service in real-time.
• Open-Ended Survey Questions – Like “What do you find most frustrating about this feature?”

Continuing with our example, Sarah wanted to dig deeper into the why, so she gathered a small group of users for interviews. Here’s what she learned:

Users loved the concept of the feature, but they struggled with navigation. The button placement was unintuitive, and instructions weren’t clear.

This insight was critical—her team was about to scrap the feature, but the problem wasn’t the functionality. It was usability.

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What Is Quantitative Research?

Quantitative research is a method of research that involves collecting and analyzing numerical data to identify patterns, make predictions, and test causal relationships. This data gives you hard numbers that measure trends, validate assumptions (or prove them wrong), and track performance.

Common quantitative research methods include:

• Surveys with Fixed Choices such as multiple choice questions.
• A/B Testing, such as testing different UI designs to see which leads to higher engagement.
• Usage Metrics, like how many users dropped off at each step.

After discovering the issue with usability, Sarah needed to measure how widespread the problem was.

Here’s what her numbers showed:

• Only 30% of new users completed the onboarding flow.
• Over 60% of users abandoned the feature within the first week.
• Users who completed onboarding were 3x more likely to stay subscribed.

The data was clear: improving onboarding could significantly increase retention.

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Key Differences Between Qualitative and Quantitative Research

*Without AI tools like Blix that enable Qual at scale

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When to Use Qualitative vs. Quantitative Research

How do you know which method to use? Let’s take a look.

Use Qualitative Research When:

• You need in-depth insights into customer frustrations and needs.
• The goal is to explore new ideas, test concepts or uncover motivations.
• Your sample sizes are small, but depth of understanding is critical.
• You need to understand the ‘why’ behind the numbers and behaviors.

Examples:

• Launching a new product? Talk to early adopters to uncover what excites or frustrates them.
• Struggling with low user engagement? Conduct interviews to understand what’s missing in the experience.
• Want to improve brand perception? Run focus groups to learn how customers feel about your messaging.

Like Sarah, you can uncover customer challenges like usability issues through interviews—something analytics alone wouldn’t reveal.

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Use Quantitative Research When:

• You need measurable data for informed decision-making.
• Large sample sizes and statistical reliability are necessary.
• You need to track comparisons, trends, and patterns.

Examples:

• Measuring customer satisfaction? Send out NPS surveys to quantify how customers rate their experience.
• Running an ad campaign? A/B test different headlines and track which one drives the most clicks.
• Trying to boost conversions? Analyze heatmaps and click-through rates to see where users drop off.

Like Sarah, you can validate design changes with A/B tests so you know which UI update increases engagement.

The best approach? Use both together to get the clearest insights. Here’s how:

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Can You Combine Qualitative and Quantitative Research? (Mixed Methods Approach)

The best insights come from combining both methods—a strategy known as triangulation or qual-quant. 

This means using multiple research methods to validate findings, reduce bias, and get a more holistic understanding of the problem.

Here are some ways businesses use qual-quant research:

• Marketers test ad messaging with focus groups, then track performance metrics.
• Insight teams analyze open-ended customer feedback and use AI tools like Blix to quantify trends at scale.
• CX researchers use qualitative interviews to develop hypotheses and then conduct large-scale surveys for validation.

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Unlocking Qual At Scale

Historically, doing qualitative research at scale was extremely time-consuming, expensive, and tedious. It required a lot of manual effort to code surveys and interviews to turn qualitative data into numerical, quantitative data.

With the new generation of ai text analysis tools, that’s completely changed. 

Today, we have AI-powered verbatim analysis software like Blix, which does all the heavy lifting for you. Simply upload your qualitative data, click a button, and Blix will turn your open ended text responses into coded quantitative data, ready for analysis at scale.

Book a demo of Blix today to learn how we can help you leverage both quantitative and qualitative research and unlock qual at scale.

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Common Misconceptions About Qualitative and Quantitative Research

🚫 “Qualitative research isn’t reliable.”
✔️ It provides depth and reveals insights you’d never get from numbers alone.

🚫 “Quantitative research is always objective.”
✔️ Survey design and wording can introduce bias.

🚫 “One method is better than the other.”
✔️ They serve different purposes and typically work best together.

🚫 “Qual is only for small samples.”
✔️ AI-powered tools like Blix now allow qualitative insights at scale.

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