Survey Report

evaluationcommunicationmediumIntermediate

TL;DR

Statistical analysis and visualization of quantitative survey results.

What is it

The Survey Report presents quantitative research results (surveys, questionnaires) with statistical analysis, visualizations, and actionable conclusions. It combines charts, data tables, and interpretive narrative.

What it is for

✓Present quantitative data in a comprehensible way
✓Validate or refute hypotheses with statistical evidence
✓Complement qualitative findings with scale data
✓Justify decisions with numbers for stakeholders

Research methods that feed it

Online surveys (SurveyMonkey, Google Forms, Typeform)Post-task questionnairesNPS/CSAT/SUS surveys

When to use it

✓After collecting sufficient survey responses
✓To complement qualitative research with quantitative data
✓When stakeholders need 'numbers' to make decisions
✓To establish benchmarks or measure progress

When NOT to use it

✗If you have fewer than 30 responses (not statistically significant)
✗When data isn't representative of your target population
✗As the only research method (always complement with qualitative)

How to create it step by step

1Clean data: Remove incomplete, duplicate, or low-quality responses.
2Analyze basic statistics: Mean, median, standard deviation for each quantitative question.
3Visualize results: Create appropriate charts (bars for comparison, pie for distribution, line for trend).
4Segment: Cross data by relevant segments (user type, tenure, device).
5Interpret findings: Convert data into actionable insights connected to business.
6Present with context: Include sample size, margin of error, and limitations.

Tips for small teams

Use Google Sheets or free tools for basic analysis
No advanced statistics needed — percentages and trends are enough to start
Visualize top 5 findings — don't chart everything
Always include sample size next to each data point

Common mistakes

✗Presenting percentages without sample size context
✗Making generalizations with small samples
✗Not segmenting data by user type
✗Using inappropriate charts (3D, pies with 10+ categories)
✗Confusing correlation with causation

Contextualized example

Context: Post-purchase satisfaction survey in e-commerce, 450 responses.

Finding: Overall NPS = 42 (good). But when segmented: mobile NPS = 28 vs. desktop = 58. Mobile experience has significant issues. 67% of dissatisfied mobile users mentioned 'slow checkout process' as the main reason.

Phase

evaluationcommunication

Audience

stakeholdersproduct-team

Complexity

medium

Experience level

Intermediate

Related deliverables

Related methodologies

Free tool by UXR — UX Research Consulting in Chile