In this episode of Disambiguation, I had the pleasure of speaking with Ranjitha Kumar, Chief Research Scientist at UserTesting. We delved into the exciting intersection of AI and customer experience research, particularly focusing on how large language models (LLMs) are transforming the way we gather and analyze user feedback.

Ranjitha shared insights into her role at User Testing, where she leads the AI product strategy. We discussed the various stages of customer experience research where AI can play a pivotal role—from creating tests and identifying target users to analyzing results and contextualizing findings within a broader dataset. The conversation highlighted the challenges of integrating AI, especially when dealing with multimodal data streams that include both explicit feedback, like survey responses, and implicit feedback, such as clickstream data.

One of the key takeaways was the potential for AI to enhance qualitative research by providing deeper insights into user emotions and behaviors. Ranjitha emphasized the importance of human oversight in the AI process, ensuring that researchers can verify AI-generated insights against raw data to avoid misinformation.

We also explored the future of AI in this space, envisioning a seamless integration of qualitative and quantitative research methodologies. Ranjitha discussed the potential for AI to not only analyze data but also generate design alternatives, creating a holistic system for optimizing digital experiences.

As we wrapped up, Ranjitha recommended the People Plus AI research website from Google, which offers valuable guidelines for user-centered machine learning. This episode provided a fascinating look at how AI is reshaping customer experience research and the exciting possibilities that lie ahead.

Thank you for joining us, and I hope you found this discussion as enlightening as I did! Don't forget to subscribe and leave a rating if you enjoyed the show.