A global provider of corporate payment and employee benefit solutions

Tasks

• Developed visualizations for client segments and profiles
• Predicted churn likelihood with machine learning models
• Set up data warehouse and database schema
• Automated data updates for Power BI dashboard
• Implemented alert system for data updates and exchanges
• Created tools for churn retention strategies
• Compiled detailed project documentation

Challenges

Building a robust data infrastructure and predictive models for effective churn retention.

Solutions

• Set up a data warehouse (likely AWS Redshift or Google BigQuery).
• Use scikit-learn models and gradient boosting for churn prediction.
• Automate data processes and implement retention strategies.

Outcomes

Improved client retention and data-driven decision-making through advanced analytics.

Technologies Used