Customer Churn Analysis Using Supervised Learning Algorithms

Introduction

Customer churn — the loss of customers to competitors — is one of the most expensive problems in business. For subscription-based companies, reducing churn by just 5% can increase profits by 25-95%. In this study, I investigated how supervised learning algorithms can predict which customers are likely to churn.

The Problem

The dataset contained 10,000+ customer records with features including: - Account age and tenure - Usage patterns and frequency - Customer support interactions - Billing history and payment behavior - Demographic information

The target variable was binary: churned (1) or retained (0). The dataset was imbalanced with approximately 20% churn rate.

Methodology

Data Preprocessing - Handled missing values using domain-appropriate imputation - Encoded categorical variables using one-hot encoding - Scaled numerical features using StandardScaler - Addressed class imbalance using stratified sampling

Feature Engineering - Created interaction features (e.g., support_calls_per_month) - Extracted temporal features from account activity - Built aggregate features from transaction history

Model Training Two primary algorithms were compared: 1. Logistic Regression (baseline, interpretable) 2. Random Forest (ensemble, captures non-linearities)

Both models were evaluated using 5-fold stratified cross-validation.

Results

Random Forest outperformed Logistic Regression across all metrics. The improvement in Recall (0.78 vs 0.72) is particularly valuable for churn prediction — catching more at-risk customers is worth the trade-off of some false positives.

Key Insights

• **Support interactions matter most**: Number of support tickets in the last 3 months was the strongest predictor of churn.

2. **Usage trends beat absolute usage**: Customers whose usage was declining were more likely to churn than customers with consistently low usage.

3. **Early tenure is critical**: The first 90 days show the strongest churn signals. Intervention during this period is most effective.

Business Recommendations

• Implement proactive outreach when support ticket count exceeds threshold
• Create onboarding program focused on first 90 days
• Monitor usage trend metrics (not just absolute usage)
• Use model predictions to prioritize retention team efforts

Limitations and Future Work

• Model trained on historical data; requires periodic retraining
• Did not explore deep learning approaches
• Could benefit from more granular temporal features
• A/B testing of interventions would validate business impact