You clearly have a pile of customer service chat logs, DMs, and tickets in hand, yet when it’s time to review, all that’s left is “everyone is complaining.” That’s the classic sign that conversation analysis never really got off the ground: no intent extraction, no grouping, no quantification—let alone root-cause analysis. I usually use the ChatGPT + Claude + Gemini trio to “squeeze” the text dry, then have Midjourney produce visualization assets. The efficiency is top-tier.
Step 1: First, turn conversations into clean data
No matter whether the source is online customer support or a community, start with minimal cleaning: remove sensitive information such as phone numbers and order numbers, and keep the context in the format of “one user message + one agent message.” For NLP-based conversation analysis, noise is the biggest enemy—add a bunch of filler and the results will drift.
Step 2: Use ChatGPT to build a tagging system and do intent recognition
I have ChatGPT generate a tag tree first, then label each conversation line by line. A commonly used prompt is: Please generate for the following dialogue: intent, sentiment, key entities, whether it has been resolved, and next-step suggestions, and output as CSV columns. It’s great for “quickly setting up the framework,” but the downside is that when it encounters domain-specific jargon, it sometimes pretends it understands.
Step 3: Use Claude for long-text attribution and insights
Claude is more reliable with long conversations and long tickets. I’ll dump in a full week of records and have it produce Top issue clustering + root-cause hypotheses + verifiable data points. It’s very smooth at writing postmortem reports, but you need to watch it so it doesn’t write “speculation” as “fact.”
