Something went wrong!
Hang in there while we get back on track
Churn Driver Identification for Marketing
Multi-variable customer analysis identifying churn drivers through box plots, scatter plots, and distribution overlays. Includes risk scoring formula and audience-specific visualization recommendations.
Input
What you provide to the skill
I have 200 customer records with age, purchase frequency, average order value, and churn status. I want to identify which factors drive churn for my marketing team.
Output
What the skill generates for you
CUSTOMER CHURN ANALYSIS - CHART RECOMMENDATIONS & INSIGHTS
DATA QUALITY REPORT
Dataset: 200 customer records with complete data
- No missing values across all variables
- Minimal outliers: 1 age outlier (0.5%), 5 purchase frequency outliers (2.5%)
- Balanced churn distribution: 59.5% retained, 40.5% churned
- Data quality: Excellent - ready for analysis without preprocessing
RECOMMENDED CHART #1: Comparative Box Plots ⭐ BEST CHOICE
Why it works for your marketing team:
Box plots simultaneously show the distribution, central tendency, and spread of each metric while making the difference between churned and retained customers visually obvious.
What it reveals:
- Purchase Frequency: MASSIVE difference - churned customers averaged 3.7 transactions/month vs 6.5 for retained
- Average Order Value: SIGNIFICANT difference - churned customers spent $54.48/order vs $80.09 for retained
- Age: NO meaningful difference - churn is age-agnostic
Best practices applied:
- Color-coded: Blue for retained (positive), red for churned (warning)
- Statistical significance annotated on each panel
- Mean lines (dashed green) and median lines (solid black) both shown
RECOMMENDED CHART #2: Two-Factor Scatter Plots with Quadrants
Why it works:
Reveals the COMBINED effect of two variables, identifying high-risk customer segments.
What it reveals:
- “Champions” (high frequency, high value) - mostly retained
- “Churn Zone” (low frequency, low value) - heavily churned
- Pattern shows frequency matters MORE than order value
CHARTS TO AVOID ❌
Pie charts for metric comparison - Cannot compare retained vs churned on multiple metrics
Radar/spider charts - Only 3 variables makes this sparse and hard to read
3D visualizations - Adds visual clutter without information value
KEY FINDINGS
PRIMARY: Purchase Frequency is the Dominant Churn Driver
- Churned customers: 3.7 transactions/month
- Retained customers: 6.5 transactions/month
- Difference: -43.5% (statistically significant)
Action: Flag any customer who drops below 4 purchases/month for intervention
SECONDARY: Average Order Value Matters, But Less
- Churned: $54.48 average order
- Retained: $80.09 average order
- Difference: -32.0%
TERTIARY: Age is NOT a Factor
- No statistical difference between groups
- DO NOT waste budget on age-based segmentation for retention
HIGH-RISK CUSTOMER PROFILE
Churn Risk Score:
- Purchase frequency < 4/month: +50 points
- Average order value < $60: +30 points
- Both conditions: +20 bonus points
Risk levels: 0-30 (Low), 50-60 (Medium), 80-100 (High - immediate intervention)
About This Skill
Get intelligent chart type recommendations based on your data structure and visualization goals, plus publication-ready visualizations with matplotlib/seaborn.
View Skill DetailsMore Examples
Process Compliance Temperature Analysis
Quality control analysis of manufacturing temperature data against specifications. Features histogram with spec limits, process capability metrics (Cp/Cpk), and tiered corrective action recommendations.
Product Line Performance Comparison
Analyzes quarterly sales data for multiple product lines, recommending grouped bar charts and line charts for executive audience. Includes chart avoidance guidance and strategic action items.