AI Automated Tagging
The AI Automated Tagging feature utilizes advanced artificial intelligence algorithms to automatically assign relevant tags and labels to user feedback and testing data. By analyzing the content and context of the feedback, this feature accurately categorizes and organizes the data, enabling efficient search, filtering, and analysis. AI Automated Tagging saves time and effort, allowing users to quickly identify trends, patterns, and key insights within large volumes of user feedback.
The AI Summaries feature leverages artificial intelligence algorithms to generate concise and informative summaries of user testing sessions and feedback. It automatically extracts key points, trends, and insights from the data, condensing it into easy-to-understand summaries. This saves time and effort in reviewing and analyzing user feedback, providing users with quick access to the most relevant and actionable information. AI Summaries streamline the decision-making process, enabling teams to make informed decisions based on a comprehensive understanding of user experiences.
Bring your own Testers
The Bring Your Own Testers feature allows users to invite and include their own selected testers in the user testing process. It provides the flexibility to engage specific individuals or a targeted user group who are already familiar with the product or possess specific expertise. This feature facilitates a more personalized and tailored testing approach, leveraging the insights and perspectives of testers closely aligned with the intended user base.
Extension of Tester Targeting Data
The Extension of Tester Targeting Data feature allows for a more detailed and specific selection of testers based on extended demographic and behavioral data. This ensures that user tests are conducted with the most relevant participants, enhancing the quality and relevance of feedback received.
The Unmoderated Interviews feature allows for the execution of user interviews without the need for a moderator. Participants can independently provide feedback, thoughts, and opinions on specific topics or tasks. This feature offers flexibility, convenience, and enables asynchronous user research, making it suitable for gathering insights at scale and accommodating diverse schedules.
The Sentiment Analysis feature utilizes advanced natural language processing algorithms to analyze and extract the sentiment expressed in user feedback. It automatically identifies and categorizes the sentiment as positive, negative, or neutral, providing valuable insights into the overall user sentiment towards a product or experience. This feature helps to gauge user satisfaction, identify areas for improvement, and make data-driven decisions to enhance the user experience.