At a Glance
This article provides an overview of UserTesting’s artificial intelligence and machine learning capabilities and how UserTesting is committed to putting safeguards in place to ensure that data generated from AI is always managed in a secure, compliant manner.
On this page:
UserTesting has been investing in AI/ML since 2019, optimizing our machine learning models to not only speed up time to insights but to help teams focus on more strategic work by surfacing insights that drive customer-centric decision-making across the enterprise.
AI powers every stage of the research lifecycle, accelerating everything from the way organizations connect to their customers to post-test analysis and insight summarization. By processing multiple data streams—video, audio, text, and behavioral data—and training models on contextually-relevant datasets, UserTesting’s AI capabilities help you pinpoint key insights without spending hours watching videos or connecting the dots across data. AI-generated insights always point to the source data so that you have a way to back up the AI-generated insights.
Our team at UserTesting will continue to evaluate the newly available technology while ensuring that we’re training commercially-ready ML models that free our customers from worries about data security and privacy.
How UserTesting Uses AI
UserTesting currently supports AI/ML capabilities in the Platform and continues to research best ways to deliver value through emerging technology. Our implementation is designed to deliver efficiencies across all stages of research to accelerate the way teams collect, analyze, and take action on user insights.
Here is a list of our current AI/ML capabilities:
- Interactive Path Flows: Visualizations that show how contributors navigate a website or prototype. They are generated by behavioral data as contributors complete a task.
- Sentiment Path: An interactive visualization laid on top of the Interactive Path Flow that automatically evaluates and summarizes sentiment (positive/negative) feedback from web-based experiences.
- Intent Path: An interactive visualization laid on top of the Interactive Path Flow that groups specific customer behaviors (e.g., browse, add to cart, search) based on that individual’s intent.
- Keyword Map: An interactive visualization that evaluates verbal tasks and draws out adjectives that contributors used most frequently.
- Sentiment Analysis: An ML-generated indicator that surfaces moments of negative and positive sentiment when reviewing a completed session in the UserTesting video player.
- Smart Tags: An ML-generated indicator that highlights themes (e.g., easy, pain point, suggestion) in the video player and for written tasks.
- Friction Detection: An ML-generated indicator that offers insight into where contributors had difficulty interacting with websites or prototypes during tests.
- AI Insight Summary: An AI-generated tool that summarizes the tasks and findings captured in Interactive Path Flows and verbal tasks.
At UserTesting, we care deeply about the privacy and security of our customers' data. We are dedicated to enterprise-grade information security and the protection of confidential data including customer data, such as video files, and contributor data.
As we advance our AI capabilities, we ensure that the data being used to train our models rely on aggregated, anonymized data that comply with the contractual agreements we have in place as well as rules and policies protecting Personal Identifiable Information (PII).
Our customers’ data belongs to our customers. Our teams will continue to explore emerging AI/ML technologies for their potential to accelerate how our customers uncover relevant human insights, and we will do so alongside our security team who will put safeguards in place to ensure that data is always managed in a secure, compliant manner.
Need more information? Read these related articles.
Want to learn more about this topic? Check out our University course.
Please provide any feedback you have on this article. Your feedback will be used to improve the article and should take no more than 5 minutes to complete. Article evaluations will remain completely confidential unless you request a follow-up.