AI-Powered Customer Feedback Analysis
Published on 03.12.2025
4 Prompts to Spot Trends in Customer Reviews
TLDR: Stop manually categorizing customer feedback. Use a structured, multi-prompt LLM workflow to turn unstructured rants into actionable engineering tickets. Clean your data first for best results.
Summary: The author recounts the pain of manual "Voice of the Customer" analysis, highlighting its inefficiency and bias. LLMs offer a superior, emotionless alternative for processing vast amounts of feedback. However, simply dumping raw data into a model yields generic, useless insights. The key is a refined workflow involving data hygiene (stripping noise like timestamps and user IDs) and a series of specific prompts. The author recommends batching large datasets to avoid the model "forgetting" information in the middle. The article provides the exact prompts used in this workflow.
Key takeaways:
- Manual feedback analysis is slow, biased, and should be automated.
- Clean your data before feeding it to an LLM to improve the signal-to-noise ratio.
- Don't just use one generic prompt; a multi-prompt chain is more effective.
- Batch large datasets and summarize the summaries for better accuracy.