Pain Point Prioritizer

Free v1.0.0 3 views

Transform raw pain point lists into prioritized, scored rankings using a systematic JTBD-inspired framework with urgency, frequency, impact, and solvability scoring.

What You Get

Get objective priority rankings to decide which problem to solve first, preventing the common mistake of building for 'nice-to-have' problems instead of 'must-solve' ones.

The Problem

Product researchers struggle with prioritizing which pain points to solve for a given audience because they can identify many problems but can't assess which are most urgent/frequent/valuable, which causes them to build for 'nice-to-have' problems instead of 'must-solve' ones.

The Solution

This skill applies a Jobs-to-be-Done inspired scoring matrix to evaluate each pain point across four weighted dimensions: Urgency (35% - language intensity, desperation indicators), Frequency (25% - occurrence rate, population affected), Impact (25% - quantified cost in time/money/wellbeing), and Solvability (15% - can a product actually fix this). Each pain point receives a 0-100 score per dimension with explicit reasoning, then a weighted final score determines priority ranking. The output includes detailed breakdowns for top pain points, product opportunity angles, and flags for low-confidence assessments. The framework can re-prioritize when new interview data emerges, giving interview mentions higher weight than web research.

How It Works

  1. 1 Parse input to extract pain points with frequency mentions, urgency language, impact indicators, and solvability clues
  2. 2 Score each pain point 0-100 on four dimensions: Urgency (desperation language), Frequency (occurrence rate), Impact (time/money/emotional cost), Solvability (product feasibility)
  3. 3 Calculate weighted final score using formula: (Urgency × 0.35) + (Frequency × 0.25) + (Impact × 0.25) + (Solvability × 0.15)
  4. 4 Generate structured report with dimension breakdown tables, rank explanations, and product opportunity angles for top 3 pain points
  5. 5 Flag low-confidence scores when evidence is weak, add assumptions explicitly, note when interview data should override web research

What You'll Need

  • List of 3-15 pain points with brief descriptions
  • Optional context: frequency data, urgency language quotes, quantified impact, attempted solutions, data source type

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