SUMMARY
Purpose: A Data Analysis Report (Quantitative Research) distils numeric data into actionable design insights, helping teams make evidence-backed product decisions.
Design Thinking Phase: Define
Time: 2–5 days depending on dataset size and scope
Difficulty: ⭐⭐
When to use:After large-scale usability testing or surveysTo validate assumptions gathered during qualitative researchTo inform MVP prioritisation or A/B test strategy
What it is
A Data Analysis Report (Quantitative Research) is a structured summary of measurable user data — often collected through surveys, analytics, and product usage — translated into actionable findings for product and design teams. It bridges the gap between statistical trends and human-centred design strategy.
📺 Video by NNgroup. Embedded for educational reference.
Why it matters
Design decisions backed by quant data are harder to dispute — they instil confidence, alignment, and speed across cross-functional teams. A strong Data Analysis Report can help you detect patterns in usage, track changes over time, and set measurable baselines for impact.
When to use
- When you need to complement qualitative insights with statistical rigour
- When stakeholders request "hard numbers" for prioritisation
- When expanding into new markets where sentiment and behaviours vary
Benefits
- Rich Insights: Helps uncover user needs that aren’t visible in metrics.
- Flexibility: Works across various project types and timelines.
- User Empathy: Deepens understanding of behaviours and motivations.
How to use it
Start by clearly defining your research questions and hypotheses. Then:
- Collect Data: Use tools like Typeform, Google Analytics, or Mixpanel to track user activity or responses.
- Structure Your Dataset: Clean and organise it by user segment, time period, or flow.
- Run the Analysis: Use methods like frequency distribution, cross-tab analysis, or correlation as needed.
- Simplify the Signal: Focus on 3–5 key metrics that tie directly to your design goals.
- Visualise the Data: Use bar graphs, funnel charts, and heatmaps where appropriate.
- Synthesise the Implications: Don't just show numbers — explain the "why it matters" for the design team.
Example Output
Summary Finding: 72% of first-time users dropped off at the payment form on mobile, compared to 54% on desktop.
Implication: Indicates a likely usability issue in the mobile checkout experience — potentially due to field length or validation friction.
Design Action: Prioritise redesign of the mobile payment UI in the next sprint and test performance impact in A/B prototype sessions.
Common Pitfalls
- Overfocusing on vanity metrics: Prioritise insights that influence design outcomes, not just usage popularity.
- Skipping segmentation: Results need to reflect the diversity of your user base to be useful for UX decisions.
- Poor data visualisation: A cluttered or unclear chart undermines the value of strong analysis.
10 Design-Ready AI Prompts for Data Analysis Report – UX/UI Edition
How These Prompts Work (C.S.I.R. Framework)
Each of the templates below follows the C.S.I.R. method — a proven structure for writing clear, effective prompts that get better results from ChatGPT, Claude, Copilot, or any other LLM.
C.S.I.R. stands for:
- Context: Who you are and the UX situation you're working in
- Specific Info: Key design inputs, tasks, or constraints the AI should consider
- Intent: What you want the AI to help you achieve
- Response Format: The structure or format you want the AI to return (e.g. checklist, table, journey map)
Level up your career with smarter AI prompts.Get templates used by UX leaders — no guesswork, just results.Design faster, research smarter, and ship with confidence.First one’s free. Unlock all 10 by becoming a member.
Prompt Template 1: “Summarise Key Conversion Drop-offs Across User Segments”
Summarise Key Conversion Drop-offs Across User Segments
Context: You are a UX researcher analysing a new purchase flow for a SaaS product.
Specific Info: You’ve collected event data across desktop and mobile, showing completion rates for each step in the funnel by user segment.
Intent: Identify statistically significant drop-off points and segment-level differences that impact conversion.
Response Format: Return a summary table of major drop-offs with segment comparison, followed by a short list of design hypotheses.
If data formatting or segmentation is unclear, ask clarifying questions first.
Suggest one follow-up experiment or research probe to test one finding.
