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.
Prompt Template 2: “Turn Survey Responses into Design Priorities”
Turn Survey Responses into Design Priorities
Context: You are a product designer reviewing 1,200 quantitative survey responses after a new feature release.
Specific Info: The survey used Likert scale questions to assess confidence, clarity, speed, and satisfaction.
Intent: Extract statistically important patterns that suggest which experience areas most need improvement.
Response Format: Return a ranked list of design opportunity areas, with supporting data points per item.
Ask for clarification if survey questions or scoring scale are unclear.
Follow up by recommending one UI or UX pattern that could address the most critical issue.
Prompt Template 3: “Audit Experiment Impact by User Intent”
Audit Experiment Impact by User Intent
Context: You are a UX lead reviewing the outcome of an A/B test for an onboarding optimisation.
Specific Info: You have access to metrics split by use-case intent (e.g. quick preview vs deep setup).
Intent: Understand how design changes affected users with differing goals, and whether outcomes were universally positive.
Response Format: Create a matrix showing variant performance by intent group, with brief interpretation notes.
Validate if control vs variant naming is needed before proceeding.
Suggest one stakeholder insight slide to communicate the results effectively.
Prompt Template 4: “Generate Visual Summary of Top UX Friction Areas”
Generate Visual Summary of Top UX Friction Areas
Context: You are preparing a stakeholder readout blending survey data and analytics for a global travel app.
Specific Info: You’ve tagged issues with frequency and severity scores based on quant metrics.
Intent: Communicate the most critical interaction problems visually using a prioritisation format.
Response Format: Return a simple 2x2 matrix (e.g. Impact vs Frequency), listing each insight in the appropriate quadrant.
Ask if design themes or personas need to be included.
Suggest one visual tool to present this in a Figma or Slide deck.
Prompt Template 5: “Detect Trends in Feature Usage Over Time”
Detect Trends in Feature Usage Over Time
Context: You are analysing feature adoption metrics after three monthly releases on a B2B dashboard tool.
Specific Info: The features include widgets, PDF export, and collaboration tagging.
Intent: Spot upward/downward usage trends and compare across accounts.
Response Format: Deliver a line chart-style summary and bullet list of recommendations.
Ask for clarification on temporal filters or user type breakdown if needed.
Recommend one next step for correlating trends with qualitative feedback.
Prompt Template 6: “Cluster Users by Interaction Patterns”
Cluster Users by Interaction Patterns
Context: You are working with a data analyst to group mobile app users by behaviour.
Specific Info: The dataset includes session length, feature usage count, and task success rate.
Intent: Identify user clusters to support persona or journey refinement.
Response Format: Return a brief written summary of 3–4 distinct behavioural clusters including traits and design implications.
Ask if noise data or outliers should be filtered.
Suggest a visualisation method to support stakeholder presentations.
Prompt Template 7: “Spot Anomalies in Sequential Task Completion”
Spot Anomalies in Sequential Task Completion
Context: You are evaluating tasks in a multi-step form flow for a financial product.
Specific Info: Completion rates spike at one unexpected point midway through, and feedback scores do not explain it.
Intent: Explore what may be causing the anomaly, such as returning users, known bugs, or server latency.
Response Format: Provide hypotheses with supporting indicators and recommend data to validate them.
Ask for clarification on the platform, live status, or expected behaviours.
Suggest a follow-up qualitative method to explore the anomaly.
Prompt Template 8: “Benchmark KPI Movement Post-Redesign”
Benchmark KPI Movement Post-Redesign
Context: You are presenting results of a responsive redesign update across mobile and web.
Specific Info: Your KPIs include task success, NPS, and engagement metrics before and after launch.
Intent: Identify what’s improved, what regressed, and where attention is needed next.
Response Format: Use a before-after comparison table with design commentary column.
Flag if audience context (exec vs design team) changes the level of detail.
Recommend one stakeholder artefact to communicate this roadmap-wide.
Prompt Template 9: “Correlate Satisfaction Scores with Behaviour”
Correlate Satisfaction Scores with Behaviour
Context: You are mapping CSAT results with user behaviours inside a marketplace app.
Specific Info: You have clickstream data and satisfaction surveys tagged by session ID.
Intent: Understand which actions contribute positively or negatively to satisfaction levels.
Response Format: Output key behavioural predictors of sentiment in a simple cause-effect table.
Ask if demographic slices are relevant for this analysis.
Follow up with an idea to prototype for high-impact predictors.
Prompt Template 10: “Translate Quant Findings into UX Hypotheses”
Translate Quant Findings into UX Hypotheses
Context: You are leading a synthesis sprint using survey and usage analytics from recent product events.
Specific Info: You’ve found several statistically significant patterns in user flow data.
Intent: Turn data points into testable UX hypotheses suitable for prototyping, testing, or workshops.
Response Format: Deliver 3–5 "If we... then users will..." format hypotheses.
Check if the data points were stabilised or if they’re in active experimentation.
Recommend one cross-functional workshop activity using these hypotheses.
Recommended Tools
- Google Analytics 4 — for user journey quant data
- Looker Studio — for building dynamic dashboards from raw UX data
- Typeform/Qualtrics — for survey creation with export features
- SPSS or R — for advanced statistical analysis
- Figma — to visualise quantified flows or friction points
