All examples for Smart Chart Recommender

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)