Development of a Customer Churn Prediction System for B2B & B2C Industries

The client faces significant risks of customer abandonment which impact revenue and customer lifetime value. Existing systems lack an early warning capability to anticipate potential churn until it occurs, limiting proactive engagement and retention strategies. Additionally, there is a need to understand specific risk factors contributing to churn across various sectors and to support informed decision-making for retention efforts.

A large-scale consumer goods and services company seeking to proactively manage customer retention and reduce churn through predictive analytics.

• Develop a machine learning-based predictive model capable of identifying potential customer churn up to 90 days in advance.
• Implement an integrated early warning system to flag at-risk customers and enable proactive retention initiatives.
• Create an internal analytics dashboard to facilitate real-time monitoring, risk factor analysis, and tailored retention strategies.
• Optimize customer retention costs and maximize customer lifetime value by enabling targeted intervention.
• Improve overall customer retention rates and reduce churn metrics within key sectors.

• A machine learning model trained to predict customer churn risk up to 90 days in advance.
• An early warning alert system that flags high-risk customers in real-time.
• A user-friendly internal dashboard displaying risk levels, contributing factors, and historical trends.
• Tools for segmentation and analysis of risk factors to tailor retention strategies.
• Data ingestion pipelines integrating customer data from various operational systems.

• Customer Relationship Management (CRM) systems for customer data access
• Operational systems supporting customer transactions and interactions
• Analytics and reporting tools for dashboard visualization

• System scalability to handle large volumes of customer data across multiple sectors
• Real-time data processing capabilities for immediate risk alerts
• High system availability and reliability
• Data security and compliance with relevant privacy standards, e.g., GDPR

By implementing the predictive customer churn system, the client aims to reduce churn rates, potentially by a significant margin (e.g., up to 20-30%), improve customer retention cost efficiency, and enhance decision-making processes through real-time insights. The solution will enable the client to proactively retain at-risk customers, increasing customer lifetime value and overall revenue stability.