This case study analyzes a generated subscription dataset to uncover patterns driving customer churn. Using R, the analysis explores payment failures, support tickets, usage, and tenure to identify actionable insights. Key findings reveal that two payment failures represent a critical churn threshold, while elevated support ticket activity signals additional retention risk. The report includes aggregated data, visualizations, and evidence-based recommendations to guide proactive customer retention strategies.

This case study analyzes Divvy’s bike-sharing dataset, covering the last 12 months of trips up to February 2025. The data was obtained from the official Divvy public repository (https://divvy-tripdata.s3.amazonaws.com/index.html ). The main objective is to compare usage patterns between casual riders and annual members to better understand rider behavior.