What problem does it solve? Scattered customer feedback across support tickets, sales calls, surveys, and user research is hard to prioritize. This Skill structures multi-channel qualitative data into atomized problem statements, segment-tagged signals, and a usefulness assessment matrix so product teams can decide what to build based on evidence rather than loudest-voice requests. ## Core Features & Use Cases - Multi-Channel Ingestion Pipeline: Design an abstract 4-stage architecture (Ingestion, AI Synthesis, Persistence & Telemetry, Interface) with illustrative tools like n8n, Whisper, pgvector, and PostHog. - Problem Atomization: Separate root user friction from requested solutions, then cluster and deduplicate problems using vector embeddings. - Usefulness Assessment Matrix: Rate core user jobs as Fully Met, Partially Met, or Not Met, weighted by customer segment and ARR risk. - Use Case: Given 150 support tickets, 20 sales call recordings, and in-app survey responses, synthesize them into a structured problem repository showing which enterprise deal-blockers (e.g., missing multi-currency invoicing) carry the highest revenue risk. ## Quick Start Use the voc-insights-pipeline skill to synthesize our support tickets, sales win/loss notes, and survey results into atomized problem statements with a usefulness assessment matrix and pipeline architecture.
