## Analyze the Dataset

27.customer_churn_prediction.csv

Bayes: I'll analyze this customer churn prediction dataset and create a comprehensive plan for the analysis.

Plan

### Data Source

customer_churn_prediction

**Estimated**: 21 min

### Data Quality Assessment and Preparation

- Load dataset, examine structure, handle missing values in contract_type and payment_method, check for duplicates and outliers

### Churn Rate and Distribution Analysis

- Analyze overall churn rate, examine distribution across all features (tenure, charges, usage, support calls, contract type, payment method)

### Feature Relationships and Correlations

- Identify correlations between features, analyze numerical vs categorical feature impacts on churn, create visualizations showing key relationships

### Customer Segmentation and Risk Profiling

- Segment customers by behavior patterns, identify high-risk profiles, analyze support calls impact, compare churned vs retained customer characteristics

### Key Drivers and Insights

- Summarize main churn drivers, provide actionable recommendations for retention, highlight priority customer segments

### Comprehensive Report Generation

- Generate complete report with all findings, visualizations, insights, and business recommendations

### Data Quality Assessment and Preparation

Load dataset, examine structure, handle missing values in contract_type and payment_method, check for duplicates and outliers.

Data Quality Assessment and Preparation

Data Cleaning and Preparation

**Cleaned Churn Data**  
| Customer_id | Tenure_months | Monthly_charges | Total_usage_gb | Support_calls | Contract_type | Payment_method | Churn |
| ----------- | -------------- | ---------------- | ---------------- | ------------- | ------------- | -------------- | ----- |
| CUS1000 | 29 | 75.63 | 386 | 6 | Monthly | Credit | 1 |
| CUS1001 | 15 | 26.03 | 495 | 0 | Monthly | Credit | 0 |
| CUS1002 | 8 | 33.3 | 414 | 0 | Two-Year | Credit | 0 |
| CUS1003 | 21 | 37.34 | 250 | 1 | Monthly | Bank Transfer | 0 |
| CUS1004 | 19 | 43.56 | 203 | 1 | Annual | Credit | 1 |
| CUS1005 | 23 | 99.67 | 499 | 3 | Annual | Debit | 1 |
| CUS1006 | 11 | 75.75 | 122 | 2 | Two-Year | Credit | 1 |
| CUS1007 | 11 | 50.74 | 250 | 2 | Monthly | Credit | 1 |
| CUS1008 | 24 | 78.97 | 345 | 1 | Two-Year | Credit | 1 |
| CUS1009 | 24 | 93.22 | 66 | 2 | Annual | Debit | 1 |

### Summary of Data Quality Assessment

The dataset has been thoroughly examined and cleaned – it contains 500 customer records with 8 features, had 50 missing values in the payment_method column (10%) which were filled with the most common value "Credit", no duplicates or invalid data, and is now ready for analysis with a notably high churn rate of 86.20%.

### Churn Rate and Distribution Analysis

Churn Rate and Distribution Analysis

#### Key Insights from the Churn Distribution Analysis:
1. **Overall Churn Crisis**: 86.2% churn rate (431 customers) vs only 13.8% retention (69 customers) - this is an extremely high churn rate indicating a critical business problem.
2. **Tenure is a Strong Differentiator**: Retained customers have significantly longer tenure (median 26 months) compared to churned customers (median 17 months), showing that newer customers are much more likely to leave.
3. **Higher Charges Drive Churn**: Churned customers pay higher monthly charges (median $64.13) compared to retained customers (median $38.87), suggesting price sensitivity is a major churn driver.
4. **Usage Patterns are Similar**: Total usage GB is nearly identical between churned and retained customers (both around 275-280 GB median), so usage alone doesn't predict churn.
5. **Support Calls Indicate Dissatisfaction**: Churned customers make significantly more support calls (median 2 calls) compared to retained customers (median 0 calls), suggesting support issues correlate with churn.
6. **Monthly Contracts are Highest Risk**: Monthly contracts have the highest churn rate at 90.6%, followed by Two-Year (84.8%) and Annual (80.7%), indicating contract flexibility increases churn risk.
7. **Payment Method Shows Minor Variation**: Bank Transfer has slightly higher churn (89.6%) compared to Credit (84.9%) and Debit (84.8%), but differences are relatively small.
8. **Customer Base Composition**: Most customers are on Monthly contracts (233), followed by Annual (155) and Two-Year (112), with Credit being the most common payment method (205).

### Feature Relationships and Correlations

**Strongest Churn Predictors (in order of importance):**
1. **Monthly Charges** (r = 0.311) - Positive correlation: Higher charges → Higher churn risk.
2. **Support Calls** (r = 0.309) - Positive correlation: More support contacts → Higher churn risk.
3. **Tenure Months** (r = -0.278) - Negative correlation: Longer tenure → Lower churn risk.
4. **Total Usage** (r = -0.010) - Virtually no correlation: Usage is NOT a churn predictor.

### Customer Segmentation and Risk Profiling

Customer Segmentation and Risk Profiling Analysis

### Key Drivers and Insights

1. **High-Risk Segments**: Premium New and Budget New segments show 100% churn rates.
2. **Critical Risk Group**: 30 customers in the critical risk group with high support calls average.
3. **Best Performance Segment**: Budget Loyal segment retains 67.9%.

### Comprehensive Report Generation

Bayes: I understand. Let me now create a comprehensive summary document for step 4 completion.

Key Churn Drivers and Actionable Recommendations Analysis

### Strategic Recommendations & Next Steps

1. **New Customer Experience**: Focus on the first 6 months as a critical retention period for new customers.
2. **Price Sensitivity Management**: Introduce a value-based pricing strategy to address the churn caused by high monthly charges.
3. **Improved Support Resolution**: Implement a first-contact resolution program to manage churn risk associated with support calls.
4. **Replicate Success Factors**: Enhance the strategies that worked for the Budget Loyal segment to improve retention for all customers.