Ski Pass Renewal Prediction: Machine Learning-Driven Customer Retention Analysis

This project focuses on customer retention challenges in the ski industry. Using machine learning technology to predict ski pass renewal probabilities, its core goals are to identify high-risk churn customers, optimize marketing investments, develop personalized retention strategies, and enhance the business value of ski resorts. The project addresses the complexity of renewal prediction caused by the seasonal characteristics of the ski industry (customers are active in winter and dormant in summer) by providing data-driven solutions.

The ski resort industry faces unique retention challenges: ski passes have significant seasonality, and customers' intermittent usage patterns increase the difficulty of renewal prediction. Customer acquisition costs are far higher than retention costs; loyal season pass customers bring stable revenue, while marketing investments for new customers are substantial. This project aims to solve this problem by helping resorts identify churn risks and develop targeted strategies.

The project adopts a binary classification machine learning workflow:

• Feature Engineering: Extract features such as usage frequency (skiing days/number of visits), consumption behavior (dining/rental spending), demographics (age/distance from resort), interaction history (customer service records), and seasonal factors (snowfall amount).
• Model Selection: Test algorithms like logistic regression, random forests, and gradient boosting trees, then select the optimal model.
• Evaluation Metrics: Focus on recall rate (to identify all churn customers), supplemented by accuracy, F1 score, ROC-AUC, etc.
• Feature Importance Analysis: Uncover key factors affecting renewal to support business decisions.

Data analysis reveals core influencing factors:

• Usage frequency is positively correlated with renewal probability; low-frequency customers have high churn risk.
• The farther the residence is from the resort, the lower the renewal willingness (due to commuting costs).
• Family customers have higher renewal rates (skiing becomes a family tradition).
• First-time season pass customers have the highest churn risk; new user experience needs optimization.
• Snow seasons with insufficient snowfall reduce renewal rates (external uncontrollable factor).

The model can be applied in multiple business scenarios:

• Renewal Season Marketing: Generate high-risk customer lists and send personalized emails/conduct follow-up calls.
• Dynamic Pricing: Offer early-bird discounts or installment plans to customers with low renewal probabilities.
• Customer Success Intervention: Proactively contact high-risk customers to understand churn reasons (facilities/services/personal circumstances).
• Product Improvement: Analyze churn customer characteristics to guide operational optimization.

The project faces the following challenges:

• Data Imbalance: Renewal customers are far more than churn customers; need to handle class imbalance issues.
• External Factors: Unexpected events like economic recession, competition, and pandemics may affect model effectiveness.
• Intervention Effectiveness: After prediction, need to design effective retention strategies and measure their effects.
• Privacy Compliance: Need to comply with regulations like GDPR/CCPA to ensure transparent and compliant data usage.

The project can be further upgraded:

• Real-time Prediction: Upgrade from batch processing to real-time stream processing to dynamically update risk scores.
• Multimodal Data: Integrate unstructured data such as social media sentiment and customer service recordings.
• Causal Inference: Understand churn reasons and identify intervenable factors.
• Personalized Recommendations: Recommend product upgrades/add-on services based on customer profiles.
• Automated Intervention: Integrate with marketing automation platforms to achieve a closed loop from prediction to action.