Qualitative & Quantitative (Match made in heaven) Flashcards
(21 cards)
Are quantitative and qualitative research methods in conflict?
No, they are equals—each serving a different but equally vital role in understanding users.
What do Quantitative methods reveal vs. Qualitative
What is happening—such as behavior patterns, drop-off points, conversion rates, and statistical trends.
Why it’s happening—such as user motivations, confusion, mental models, and emotional drivers.
How do the most successful product teams use these methods?
They balance both approaches in a continuous feedback loop to inform, validate, and improve design decisions.
What is a Data-driven Persona?
A data-driven persona balances quantitative data (from analytics, surveys, CRM data) with qualitative research (from interviews, contextual inquires, etc.) to produce well-rounded, actionable user archetypes.
Data-driven personas combine the empathy and storytelling of traditional personas with the scale and precision of behavioral data. By integrating qualitative insights with quantitative evidence, they provide a powerful, well-rounded lens for making informed design decisions—especially valuable in complex systems or when serving multiple distinct user groups.
Instead of creating personas from assumptions, data-driven personas are validated representations of real user segments backed by evidence.
Components may include:
- Demographic trends (if relevant)
- Platform/device usage data
- Common user journeys or flows
- Task success/failure rates
- Search or navigation patterns
- Wants, needs, pain points (from interviews or observational research)
- Mental models or workflows tied to specific user goals
What is a Customer Journey?
A visual or narrative representation of the touchpoints a customer experiences while interacting with a product or service. It helps teams understand not only what users do, but also how they feel at each stage.
How can Journey maps vary in scope?
- Broad ecosystem-level journey (e.g., from researching cars online to test-driving and financing)
- Focused, task-specific journeys (e.g., onboarding for a new SaaS tool)
What does a Quantitative Journey capture?
Steps taken, time on task, drop-off points, latency, conversions
What does a Qualitative Journey capture?
Emotions, goals, pain points, thoughts, moments of delight
What does Data-Driven Journey capture?
Combines both quantitative & qualitative to create a rich, accurate, and actionable picture
What are the benefits of Data-driven Journey?
- Show the complete experience, not just surface behaviors
- Identify emotionally charged pain points that metrics alone can’t capture
- Uncover bottlenecks or frustrations that affect engagement or conversion
- Enable better cross-functional alignment (UX, marketing, service, ops)
- Provide a foundation for defining and measuring Key Performance Indicators (KPIs)
- Guide product roadmaps and service design priorities
What is Data-Driven Design?
The practice of making product and UX decisions based on real, measurable data- primarily from quantitative research methods such as analytics, A/B testing, and behavioral metrics.
Unlike design that’s guided only by customer interviews, stakeholder opinions, or creative intuition, data-driven design grounds decisions in objective evidence, helping teams optimize experiences more effectively and reduce risk
What are the key Data Source used in Data-Driven Design?
- Web/system analytics: What users are clicking, where they drop off, and how they navigate
- Funnel analysis: Where users abandon multi-step flows (e.g., checkout, sign-up)
- A/B testing results: Which design variant performs better based on user behavior
- Heatmaps & clickmaps: Where users are focusing or ignoring content
- Session replays: How users are interacting with specific elements
- Event tracking: Frequency and sequence of specific user actions
What are the Benefits of Data-Driven Design?
- Reduces bias in decision-making
- Reveals blind spots in usability or conversion
- Increases confidence in design direction
- Helps teams prioritize by impact, not assumption
- Enables continuous optimization of the user experience
What are key collaboration points with your Analytics Team?
- Define trackable events that align with UX goals
- Set up custom dashboards to monitor performance of specific flows
- Brainstorm hypotheses together for A/B tests
- Interpret data as a team, blending analytical and design perspectives
Why combine methods to uncover the root of UX questions?
The best way to understand a complex user problem is to pair qualitative and quantitative methods, allowing you to see both the scale of behavior and the reason behind it.
Rather than choosing between methods, ask:
“What is the root question I’m trying to answer, and what type of data (qualitative or quantitative) would best inform it?”
What are the best quantitative and qualitative method pairing for researched focused on Workflow & Behavior?
Quantitative:
Analytics, heatmaps, event tracking
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Quantitative:
Contextual inquiry, usability testing
What are the best quantitative and qualitative method pairing for researched focused on Desirability & feedback
Quantitative:
Surveys, NPS, CSAT
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Quantitative:
Interviews, diary studies, feedback sessions
What are the best quantitative and qualitative method pairing for researched focused on Navigation clarity
Quantitative:
Funnel analysis, A/B testing
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Quantitative:
Tree testing, open card sorting
What are the best quantitative and qualitative method pairing for researched focused on Emotional response
Quantitative:
Sentiment analysis, scaled surveys
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Quantitative:
Think-aloud protocols, user observation
What should researchers keep in mind when using emerging or hybrid research methods?
They should approach them with clarity and intent, ensuring the methods align with research goals to avoid confusing or inconclusive results.
Do all research methods work well together or apply to every question?
No, not all methods work together, and not every method fits every research question.
