Customer Retention Strategy for Model Fitness

This project focuses on developing a data-driven strategy to improve customer retention for Model Fitness, a gym chain. Through a combination of data analysis, clustering, and predictive modeling, the project identifies key factors influencing customer churn and provides actionable recommendations to enhance retention.

Project Overview

• Objective: To predict the likelihood of customer churn and to create user profiles that help identify retention strategies.
• Dataset: The dataset contains 4,000 records of gym users with 14 features, including demographic information, membership details, and usage patterns.
• Methodology: The project involves Exploratory Data Analysis (EDA), clustering, and building predictive models.

Key Steps

1. Exploratory Data Analysis (EDA):

   • Analyzed customer data to understand distributions, detect missing values, and identify correlations between features.
   • Visualized key distributions and correlations to derive initial insights.

2. Clustering Analysis:

   • Applied hierarchical clustering to identify five distinct user groups based on similar characteristics.
   • Conducted K-means clustering to refine the user segments and analyze feature distributions within each cluster.

3. Predictive Modeling:

   • Built and evaluated two predictive models, Logistic Regression and Random Forest, to predict customer churn.
   • Assessed model performance using accuracy, precision, and recall metrics.

4. Churn Rate Analysis by Cluster:

   • Calculated and visualized the churn rate for each cluster, highlighting significant variations in customer retention across different user segments.

Findings

• Proximity to Gym: Local users show a lower propensity to churn, indicating that geographic proximity plays a crucial role in retention.
• Friend Promotions: Users who joined through friend promotions demonstrate higher loyalty in certain clusters.
• Contract Duration: Longer contract durations are associated with higher retention rates, especially in Clusters 1 and 4.
• Additional Spending: Users with higher additional spending on gym services tend to remain loyal, particularly in Cluster 4.
• Churn Rates: Cluster 2 shows the highest churn rate (49.2%), while Cluster 4 has the lowest (1.3%), indicating the need for targeted retention strategies.

Recommendations

• Strategic Partnerships: Expand and strengthen partnerships with local and corporate companies to offer exclusive benefits and prolong gym membership.
• Personalization: Implement personalized customer service programs based on user behavior and preferences to enhance the individual experience.
• Technological Innovation: Introduce mobile apps with personalized fitness tracking, event notifications, and other features to increase engagement.
• Community Building: Organize regular group activities and events to foster a sense of community among members.
• Education and Empowerment: Offer educational programs on health, wellness, and advanced training techniques to empower members.
• Continuous Analysis: Establish a continuous data analysis cycle to monitor trends and adapt retention strategies proactively.
• Long-Term Promotions: Design promotional offers to incentivize long-term commitments and maximize the perceived value of gym membership.

Conclusion

This project provides a comprehensive strategy for improving customer retention at Model Fitness by leveraging data analysis, clustering, and predictive modeling. The insights and recommendations are designed to be practical and directly applicable, offering a roadmap for enhancing customer loyalty and reducing churn.