Turning messy research data into clear insights — Lyssna talk

What the video covers

Hien from Lyssna’s product team demonstrates the Synthesize feature, which transforms raw survey responses into organized findings and actionable insights. The demo covers both AI-generated summaries and manual summary options, showing how researchers can work at the question, section, and test levels to identify patterns and themes without hours of manual analysis.

Who it’s for

UX researchers and product teams who use Lyssna for survey-based research and want to see exactly how the AI summarization feature works in practice. Also useful for teams evaluating research platforms and comparing how different tools handle the analysis phase.

Key takeaways

1. Summaries work at multiple granularity levels. Researchers can generate summaries for individual questions, entire sections, or the full test. This flexibility lets you produce both detailed findings and executive-level overviews from the same dataset.

2. AI-generated content is editable, not final. The tool generates a first draft that researchers can modify, adding their own observations and interpretations. This positions AI as a starting point rather than a finished output.

3. Manual summaries remain available for control. For researchers who prefer to write their own synthesis, manual summary blocks can be created alongside or instead of AI-generated ones, supporting a mixed workflow.

Worth watching if…

You are evaluating Lyssna as a research platform or already use it and want to understand how the Synthesize feature can reduce the time between collecting responses and presenting findings.