VOC Product Opportunity Map

Problem: VOC volume makes product prioritization harder

User feedback is scattered across tickets, app-store reviews, communities, and interviews. Individual comments often get treated as requirements, while the real question is which features are important but underperforming, which are already good enough or overbuilt, and which are simply loud minority complaints. Prioritizing directly can turn into “build what the loudest user asks for.”

How the skill works

The skill converts earlier VOC analyses into a product opportunity map, focusing on positioning, calibration, and evidence traceability rather than summary generation.
- Input: it can consume outputs from 1-Generative VOC, 2-Industry VOC clustering, 3-FACT structuring, 4-Sentiment/pain scoring, 5-JTBD three-truth analysis, and 6-KANO requirement ranking.
- Clustering and scoring: requirements are mapped to feature modules or user-journey nodes, then scored by frequency F and emotional intensity I to calculate heat.
- Two-axis positioning: user importance is the X-axis and current satisfaction is the Y-axis, producing core opportunity, competitive advantage, low-priority improvement, and over-service quadrants.
- JTBD translation: for P0 items, it asks “When [context], I want [motivation], so that [outcome],” pushing beyond feature-level fixes.
- Output: it returns a quadrant visualization, an opportunity list, and key insights. P0 items must include original VOC evidence and concrete product actions.

Boundaries and caveats

It works best when a reasonable amount of VOC already exists and some structured analysis has been completed. If there are fewer than 10 raw feedback items or more than three missing upstream analyses, data should be supplemented before producing the map. It also does not replace behavioral data: frequently used but quiet features need validation with metrics such as retention, funnel conversion, or usage logs.