Turn customer feedback into insight, faster

UserTesting provides analytics and visualizations that help teams understand user sentiment, behavioral patterns, and experience flows. UserTesting provides features such as AI Insight Summary, interactive path flows, click maps, sentiment analysis, keyword maps, and measurable outputs that help teams turn feedback into actionable product improvements. For related reading, explore UX metrics.

Yes. UserTesting provides automated summaries through AI-powered features like AI Insight Summary and Instant Insights. AI Insight Summary generates an on-demand summary of task results by analyzing verbal, text, and behavioral data to identify themes and key findings, while the platform page also emphasizes AI-powered analytics that translate raw results into actionable insights. See AI-powered insights to learn more.

Yes. UserTesting provides sentiment analysis to help teams identify moments of positive and negative sentiment during completed sessions. As part of the UserTesting platform, sentiment analysis highlights positive and negative experiences during user testing sessions. 

UserTesting provides interactive path flows and click maps to help you visualize how users move through an experience. Interactive path flows show how people navigate across multiple screens and can reveal where users get stuck, while click maps show which elements users interacted with most on a single screen and where they clicked first. Together, these tools support visualizing user journeys for more accurate results.

Yes. UserTesting provides multiple ways to pinpoint friction. Our platform can automatically detect points where users encounter issues or express frustration by analyzing behaviors like rage-clicking or repetitive scrolling and cross-referencing them with verbal feedback. Its interactive path flows and sentiment features also help teams see where users are getting stuck or reacting negatively. For related reading on spotting usability issues. 

Yes. UserTesting helps teams analyze large volumes of qualitative data faster by combining AI-powered summaries, sentiment analysis, keyword maps, and visualizations in one place. Instead of relying only on manual review, teams can quickly spot themes, compare reactions, and interpret findings across many sessions at once. This makes it easier to scale analysis while still applying strong qualitative research methods.