pm-feedback

# pm-feedback — User feedback analysis Part of the Personal Corp framework — running a one-person business through AI agents. Structure raw feedback into a decision-driving insight report. Built-in classification, sentiment, theme clustering, NPS, trend analysis, source triangulation, and persona extraction. ## Inputs | Field | Required | Notes | |---|---|---| | Feedback data | yes | Excel / CSV / pasted text / review screenshots | | Purpose | no | Product improvement / satisfaction / topic-specific (e.g. post-launch reaction); default product improvement | | Time range | no | For freshness tagging and trend analysis | | Source channels | no | Multiple channels enable triangulation | **Mode:** ≤ 20 items → close-read mode (item-by-item with detailed reading); > 20 → statistical mode (auto-classify + aggregated report). ## Step 1 — Pre-process data - Drop exact duplicates - Merge near-duplicates (similarity > 90%), record merge count - Ultra-short items (< 5 chars, no substance like "good"/"bad") → counted separately, not in deep analysis - If a rating column exists (1-10 or 1-5 stars) → extract for NPS - Identify source channel (in-app feedback, app store, support ticket, social media, etc.) ## Step 2 — Classification **Six-category taxonomy:** | Category | Criterion | Example | |---|---|---| | **Feature request** | User wants something not yet built | "I'd like batch export" | | **Bug report** | Existing feature behaves incorrectly | "Save button loses my data" | |...