Customer Satisfaction Regional Analysis

Analysis Complete! 🎉

I’ve successfully executed the Categorical Variable Profiler skill on your customer data. Here’s what was delivered:

📊 Key Insights

Critical Findings:

1. West Region Crisis: 57% dissatisfaction rate vs 0% in other regions - all 4 dissatisfied customers are from West
2. Clothing Problem: 100% of dissatisfied customers purchased Clothing products
3. South Region Excellence: Perfect satisfaction record (100% satisfied/very satisfied)

Statistical Associations (FDR-corrected, all significant):

• Region × Satisfaction (p=0.033, Cramér’s V=0.474) - moderate-large effect
• Region × Product_Category (p<0.001, Cramér’s V=0.780) - large effect
• Satisfaction × Product_Category (p=0.011, Cramér’s V=0.566) - large effect

📁 Generated Files (11 total)

Visualizations:

• 4 distribution bar charts (Region, Age_Group, Satisfaction, Product_Category)
• 6 cross-tabulation heatmaps showing relationships between variable pairs

Data:

• chi_square_results.csv - Statistical test results with effect sizes

All files are saved in /tmp/

⚠️ Important Notes

• Sample size is small (n=30) which limits statistical power
• Chi-square assumptions violated due to small expected cell frequencies
• Results should be validated with larger sample (recommend n≥100)
• Despite small sample, large effect sizes suggest genuine associations

🎯 Top Recommendations

1. Immediate: Investigate West region operations and Clothing product line
2. Short-term: Replicate South region best practices, target 25-34 age group
3. Long-term: Increase data collection, add more variables for deeper analysis

The analysis revealed a clear pattern: West region + Clothing = Dissatisfaction, while other regions with Electronics/Home products show high satisfaction.