Overview

Strategic-frameworks deliver high-level, actionable business insights for executives by combining prioritization, sentiment analysis, and funnel evaluation. It provides visual artifacts like 2x2 priority matrices, pain curves, halo-removal adjustments, disapproval drivers, and multi-stage funnels to reveal true sentiment and guide strategic actions.

How This Skill Works

Each capability is implemented as Python tooling. priority_matrix.py plots a 2x2 matrix of Importance vs. Satisfaction/Performance; pain_curves.py maps dissatisfaction across cohorts; halo_removal.py adjusts ratings to remove brand/halo bias; disapproval_analysis.py surfaces drivers of negative sentiment. For flow and evaluation, conversion_funnel.py calculates multi-stage funnels (e.g., Awareness to Conversion) and ipsative_analysis.py normalizes variables within respondents to reveal relative intra-personal preferences.

When to Use It

• Prioritize initiatives by plotting Importance vs Satisfaction/Performance to identify high-impact gaps
• Diagnose drivers of negative sentiment with disapproval analysis and halo removal
• Visualize and optimize customer journeys using conversion funnels (Awareness -> Conversion)
• Adjust survey scores to remove halo bias and reveal true sentiment
• Reveal relative intra-personal preferences with ipsative analysis

Quick Start

1. Step 1: Import your dataset with fields for features or initiatives, Importance, Satisfaction/Performance, and cohort labels
2. Step 2: Run the relevant scripts (priority_matrix.py, pain_curves.py, halo_removal.py, conversion_funnel.py, ipsative_analysis.py) as needed
3. Step 3: Export visuals and concise executive summaries for briefings

Best Practices

• Keep priority matrices small (2x2) with clearly labeled axes
• Use up-to-date, high-quality data for Importance, Satisfaction, and bias adjustments
• Run halo_removal before presenting sentiment scores to executives
• Pair pain_curves with disapproval_analysis to surface cohort-specific issues
• Cross-validate findings with funnel results and qualitative feedback

Example Use Cases

• SaaS product team uses priority_matrix.py to map feature importance vs user satisfaction for roadmap prioritization
• E-commerce retailer uses pain_curves.py to identify cohort-specific pain points across segments
• Marketing teams visualize Awareness-to-Conversion funnels with conversion_funnel.py
• Survey team applies halo_removal.py to correct halo bias in satisfaction ratings
• Product team uses ipsative_analysis.py to compare intra-personal feature preferences

Frequently Asked Questions