Qualitative data is non-numerical information that captures the qualities, characteristics, and meanings of customer experiences. It can take various forms such as open-ended survey responses, interview transcripts, customer reviews, user-generated content, and observational data from field studies. The richness and depth of qualitative data allow customers to express themselves in their own words, surfacing underlying emotions, perceptions, and pain points that quantitative data often misses. Businesses use qualitative data collection methods like interviews, focus groups, open-ended surveys, document analysis, and field observations to gather meaningful insights. Top companies leverage qualitative data to drive customer-centric innovation and differentiation, such as Slack designing intuitive user interfaces, Netflix informing content creation with user reviews, and Glossier co-creating products with their community. However, analyzing qualitative data at scale can be challenging due to subjectivity, bias, and difficulty in categorizing unstructured data. AI-powered tools are now being used to automate the analysis of large volumes of qualitative data alongside quantitative metrics, enabling businesses to unlock the value of this data at scale.