Customer Interview Analysis

Show notes

In This Episode:

• Why their early stance on AI-powered interview synthesis has shifted
• What Teresa learned from running 15 interviews through ChatGPT and Claude
• How AI raises the floor for beginners but accelerates experts even more
• The importance of separating analysis from synthesis
• Why most teams struggle with customer interview synthesis in practice
• What happens when interviews pile up — and whether AI can realistically help
• The risks of relying on AI when your interviewing skills aren’t solid yet
• A vision for “expert + AI” synthesis that’s both fast and high-quality
• The ongoing debate about AI-led customer interviews
• A detour into PC-building that perfectly illustrates the limits of AI support

Key Takeaways:

• AI isn’t magic. It can help, but only if your interviews are strong and you provide the right context.
• Beginner + AI is usually better than nothing. But the real performance gains come from expert + AI.
• You still need to synthesize every interview individually. Dumping transcripts into an LLM isn’t a shortcut.
• Customer understanding is a competitive moat. Outsourcing it entirely will cost you in the long run.
• Empathy comes from human interaction. AI can’t replace the experience of talking directly to your customers.

Resources & Links:

• Follow Teresa Torres: https://ProductTalk.org
• Follow Petra Wille: https://Petra-Wille.com

Mentioned in this episode:

• ChatGPT
• Claude
• The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview
• The Interview Coach by Teresa
• Customer Interview Analysis: Where AI Helps and Hurts by Teresa