The 50 Best AI Prompts for Quantitative UX Research

Philip Burgess
Aug 22
3 min read

By Philip Burgess - UX Research Leader

Quantitative UX research is all about uncovering the “what” and “how much” behind user behavior. It leverages data from surveys, experiments, analytics, and large-scale usability testing to generate statistically valid insights. While qualitative research explains the why, quantitative research measures the scale and impact.

AI can supercharge this work by helping UX researchers craft better survey questions, analyze large datasets, interpret statistical results, and even generate hypotheses to test. To make your next study easier, I’ve compiled 50 of the best AI prompts for quantitative UX research—organized by stage of the research process.

1. Planning & Hypotheses

“Suggest measurable hypotheses for testing the usability of [feature/product].”
“Generate KPIs to measure success for a new onboarding flow.”
“List survey questions to measure user trust in a healthcare app.”
“What quantifiable metrics can capture engagement in a mobile shopping app?”
“Design Likert scale questions to measure satisfaction with [feature].”

2. Survey & Experiment Design

“Rewrite these survey questions to reduce bias: [insert questions].”
“Create 5 A/B test ideas to improve conversion on a checkout page.”
“Suggest rating scales (e.g., 1–5, NPS, SUS) for measuring ease-of-use.”
“Generate multiple-choice options for a question about device usage.”
“Identify flaws or leading bias in this survey draft: [insert survey].”

3. Data Collection & Metrics

“What metrics should I track to evaluate navigation efficiency?”
“Suggest behavioral data points to log for a prototype test.”
“List quantifiable ways to measure error recovery in usability tests.”
“How can I quantify time-on-task differences between two designs?”
“Create an operational definition for task success in [context].”

4. Statistical Analysis Support

“Explain when to use a t-test vs. ANOVA for usability data.”
“Summarize the difference between correlation and causation in UX data.”
“Provide an example of calculating statistical significance for a survey result.”
“Explain confidence intervals in plain language for stakeholders.”
“Suggest effect size measures relevant to usability testing.”

5. Data Cleaning & Preparation

“Suggest methods to handle missing survey responses.”
“Detect potential biases in this dataset: [insert dataset summary].”
“Propose ways to normalize skewed time-on-task data.”
“Generate Python code to clean and visualize survey responses.”
“How do I identify outliers in user test data?”

6. Interpreting Results

“Summarize key findings from this dataset: [insert summary/statistics].”
“Translate statistical results into stakeholder-friendly language.”
“Generate a plain-English interpretation of an ANOVA result.”
“What does a p-value of 0.03 mean in a usability study?”
“Explain the practical significance of this effect size: [insert value].”

7. Benchmarking & Comparison

“Compare SUS scores across two products and highlight differences.”
“What industry benchmarks exist for NPS in e-commerce?”
“Create a chart comparing pre-test and post-test scores.”
“How do I interpret a 10-point increase in CSAT?”
“Suggest competitive metrics for evaluating mobile banking apps.”

8. Storytelling with Data

“Turn these findings into 3 key insights for executives.”
“Generate a one-slide summary of quant results for a design review.”
“Craft a narrative explaining how conversion improved after an A/B test.”
“Summarize user survey results into 3 stakeholder-friendly bullet points.”
“Rewrite statistical findings for a non-technical audience.”

9. Visualization & Reporting

“Suggest chart types to visualize task success rates.”
“Generate Python code to plot NPS results by user segment.”
“Create a dashboard outline for tracking KPIs over time.”
“How can I best visualize differences in time-on-task between groups?”
“Generate 3 alternative visualizations for Likert scale responses.”

10. Continuous Learning & Optimization

“Suggest ongoing metrics to monitor after launch of a new feature.”
“Create a roadmap for quarterly quantitative UX evaluations.”
“Generate a research ops checklist for managing survey data.”
“What longitudinal methods can measure changes in usability over time?”
“Summarize lessons learned from quantitative studies into a reusable template.”

Final Thoughts

Quantitative UX research gives teams confidence in scale—knowing not just what works, but how well and how widely. With AI, researchers can accelerate design of surveys, statistical analysis, and storytelling with data.

Use these 50 prompts as a starting point. Copy, tweak, and adapt them to your projects. The more you experiment with AI, the faster you’ll unlock insights that matter.