AI in Qualitative Research

In our ongoing effort to identify worthy applications for AI in market research, the realm of qualitative research already stands out. LLMs with their natural language processing and generative capabilities, make the potential for innovation in this area especially promising. LLMs and AI have already demonstrated clear value in assisting with critical tasks intrinsic to every qualitative method. And there’s no going back. Having experimented with various tools over the past year, we are now finding it difficult to imagine conducting a study without making use of them. AI has clearly changed the game with its skill at handling these tasks:

• TRANSCRIPTION & TRANSLATION. When conducting international or multi-lingual qualitative research, LLMs can assist with automatic transcription of interviews and real-time translation of responses, allowing qualitative researchers to work across languages with far greater ease and speed.
• DATA MINING. LLMs can dig into large qualitative datasets quickly, making it easier to find specific quotes and themes, or run cross-references across subgroups or individual respondents.
• TEXT SUMMARIZATION. LLMs can summarize the extensive textual data generated in qualitative studies, making it easier to distill key points or trends.
• THEMATIC ANALYSIS. LLMs can process vast amounts of unstructured text, identifying patterns, themes, and sentiments. Not only is it very good at analyzing open-ended survey responses, it does a bang-up job at analyzing interview transcripts and even focus group discussions, saving enormous amounts of time on manual coding.

This is not to say that human analysts are no longer needed to analyze qualitative data. Quite the opposite. While LLMs are adept at identifying and interpreting common themes, current versions do not necessarily display nuanced interpretation, critical thinking, or emotional intelligence. Human expertise is still essential for deriving rich insights. But AI has proven itself to be a very good listener.