AIでCustomer Feedback Analysisを自動化する

Most businesses treat feedback analysis as a 'sentiment' exercise—seeing how many people are happy versus sad. That's a waste of compute power. The real power of AI here is identifying what I call 'The Friction Gap': the specific, repeatable moments where your product fails to meet the user's mental model. AI doesn't just tell you people are annoyed; it tells you they are annoyed because the 'Checkout' button is hidden on mobile Safari. I recommend a two-tier approach. Use an LLM like Claude via Zapier for quick, low-cost categorisation if you're small. If you're processing over 1,000 pieces of feedback a month, move to a dedicated platform like Viable. These tools are far better at 'semantic deduplication'—recognising that 'it's too expensive' and 'the price point is a bit high' are exactly the same problem. One warning: AI is still remarkably bad at detecting sarcasm and high-context industry jargon. Never fully automate the 'response' side of feedback based on AI analysis alone. Keep a human in the loop to verify the 'Outlier' category, because that’s usually where your next big product breakthrough is hiding.