Background: A subscription-based business experienced increasing customer churn rates that negatively affected recurring revenue growth. While customer data was available, the organization lacked visibility into the factors contributing to customer attrition.
Objective: The objective of this project was to analyze customer behavior, identify churn indicators, develop predictive models, and provide actionable recommendations to improve customer retention.
Challenges: The organization faced difficulties understanding customer engagement patterns, identifying at-risk customers, and measuring the effectiveness of retention strategies. Historical customer data was distributed across multiple systems and lacked standardized reporting.
Data Engineering: Customer transaction records, subscription histories, support interactions, product usage data, and engagement metrics were collected and consolidated into a centralized analytical environment. Data preprocessing included handling missing values, feature engineering, and behavioral metric creation.
Analytical Approach: Exploratory data analysis was performed to identify relationships between customer behavior and churn outcomes. Statistical methods were used to uncover patterns related to subscription duration, feature adoption, support ticket frequency, payment history, and customer engagement levels.
Predictive Modeling: Machine learning models were developed to predict the probability of customer churn. Features such as login frequency, account activity, support interactions, subscription age, and purchasing behavior were incorporated into the predictive framework. Model performance was evaluated using precision, recall, and ROC-AUC metrics.
Visualization and Reporting: Executive dashboards were designed to provide visibility into churn trends, customer health scores, retention rates, and churn risk segments. Automated reporting systems delivered weekly insights to customer success and management teams.
Key Findings: The analysis identified several leading indicators of churn, including declining product usage, reduced engagement frequency, and unresolved support issues. High-risk customer segments were clearly identified, enabling proactive intervention strategies.
Business Impact: The organization significantly reduced churn rates by implementing targeted retention campaigns based on analytical insights. Customer lifetime value increased, recurring revenue improved, and customer success teams became more proactive in managing at-risk accounts.
Tools and Technologies: Python, Scikit-learn, SQL, Power BI, Tableau, Excel, PostgreSQL, Jupyter Notebook, Machine Learning Models, and Business Intelligence Tools.