SAAS Customer Churn and Segmentation

This capstone project successfully addressed customer retention and revenue challenges for Company X, a leading cloud-based data center SaaS provider. The project tackled issues like low free-trial conversion, customer churn, and reliance on a few large clients. 

To achieve this, the project implemented several key strategies: 

1. Customer Segmentation: The project utilized clustering analysis techniques such as K-means and Principal Component Analysis to identify distinct customer segments. These segments grouped customers with similar consumption patterns within the SaaS cloud computing products. This segmentation will now facilitate targeted marketing strategies and product customization efforts. 

1. Churn Prediction: The project successfully implemented strategies to proactively identify customers at risk of churn. This was achieved by employing classification algorithms like logistic regression, random forest, and XGB models along with hyperparameter tuning. By leveraging historical data on customer behavior and usage patterns, the project trained a churn prediction model, which can now be used to predict churn and prioritize retention efforts. 

1. Key Driver Identification: Through feature importance analysis, the project investigated and identified the key factors influencing consumption patterns.  Additionally, visualization techniques were employed to explore relationships between variables and consumption patterns. This resulted in actionable insights for both product optimization and marketing strategies.