Customer Feedback Triage

Overview

Most PMs sit on a chaotic inbound stream — Slack DMs, support tickets, sales call notes, CSAT comments, NPS verbatims, in-app feedback widgets, partner emails, exec one-liners — and have no system for converting that stream into prioritization signal. The default mode is reactive: whoever shouts loudest wins, the loudest channels (sales, executives) dominate, and the actual user job stays invisible.

This skill provides a workflow and a Python tool for handling that stream. Inputs are raw feedback items from many channels. Outputs are deduplicated, categorized, scored, and routed items, plus acknowledgment responses for the customers who sent them.

The frameworks behind it are Marty Cagan's separation of request from opportunity from solution, Noriaki Kano's model of feature-quality categories, Reforge's customer-development model, and ProductPlan's request-management playbook. Detail lives in the references below.

Core Capabilities

• Intake & normalization — one verbatim record per ask across 8 channels (support, sales, social, NPS, in-app, exec, partner, interview)
• Dedup & clustering — group items by underlying opportunity even when literal requests differ
• Kano + Cagan classification — Basic/Performance/Delight/Indifferent/Reverse, and Request → Opportunity → Solution
• Segment-aware scoring — kano_weight × log10(volume+1) × segment_weight × (1 + strategic_alignment) as a coarse router (not a final RICE)
• Response discipline — Will-build / Exploring / Won't-build templates; always acknowledge, sometimes commit, rarely promise
• Feed-forward routing — to prioritization, discovery, backlog, bug tracker, or strategy

When to Use