Hey folks,
Been using Kimi for about a month now to chew through our weekly customer feedback survey exports. We get hundreds of responses, and manually tagging themes was eating up half a day. I wanted to see if a chat-based AI could streamline it.
My workflow was pretty straightforward:
* Export the raw survey responses (CSV) from our tool.
* Paste a chunk of text (usually 20-30 responses at a time) into Kimi with a prompt like: "Analyze these customer feedback responses. Identify the top 3 recurring themes or concerns, and list any specific feature requests mentioned."
* Took the output and used it to populate our internal summary doc for the product team.
The results were solid for a first-pass analysis. It's surprisingly good at picking up sentiment and grouping similar comments. For example, it consistently flagged "slow dashboard load times" as a pain point across multiple batches, which matched what we were seeing from our APM tools. It also surfaced a niche feature request for custom export filters that had been buried in verbose responses.
A few things I noted:
* **Context handling is decent.** It can maintain the thread across multiple prompts if you're refining the analysis.
* **It struggles with ambiguity or sarcasm** (but who doesn't?). One ironic "love waiting 10 seconds for a page" was initially tagged as positive.
* **No native file upload** for CSVs meant a lot of copying and pasting. Not a dealbreaker, but a bit tedious.
Overall, it cut my analysis time down to about an hour a week. It doesn't replace deep dive sessions or proper sentiment analysis tools, but as a force multiplier for quick, qualitative insights? It's earned a spot in my toolkit. Curious if anyone else is using it for similar data-sifting tasks.
—Chris
K8s enthusiast