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Phase 6
Interpreting Qualitative vs Quantitative Data
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Data is the foundation of modern product management, but not all data speaks the same language. Quantitative data delivers numbers, patterns, and measurable performance; qualitative data offers stories, motivations, and lived user experiences. The real strength lies in combining them to answer both the “what/how much” and the “why/how” of product decisions. This module explains the differences, advantages, and limitations of each data type, shows when and how to apply them, and introduces mixed-method approaches. By the end, you’ll be able to interpret and integrate both forms of data for richer, more accurate insights that drive user-centric growth.
1) Quantitative vs. Qualitative Data
Quantitative data is measurable information expressed in numbers—objective and structured. It answers “what,” “how many,” and “how often.” Examples: DAU, conversion rate, churn %, revenue.
Qualitative data is descriptive, narrative, and context-driven—the “human side” of product behavior. It answers “why” and “how.” Examples: interview transcripts, user stories, session replays.
2) Mixed-Method Approaches (The Best of Both Worlds)
Most product teams get the deepest insights by blending both data types. This validates assumptions and connects outcomes to causes.
- Exploration → Validation: Use qualitative research to uncover user needs, then confirm patterns quantitatively.
- Quantitative → Explanation: Spot anomalies in your metrics, then investigate with interviews or open feedback.
3) Key Takeaways
- Quant measures what is happening; qual explains why it’s happening.
- Each has strengths and weaknesses—neither is sufficient alone.
- Use a mixed-method approach for full visibility and better decisions.
- PMs should collect, interpret, and integrate both into actionable decisions.
Resources
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