Development of a Predictive Customer Churn Analytics and Retention System

The client faces challenges in effectively analyzing customer engagement and predicting customer attrition due to reliance on limited off-the-shelf analytics tools. Without comprehensive insights into both anonymous and authorized user behaviors, the client struggles to develop targeted retention initiatives and optimize marketing strategies.

A mid-sized marketing agency specializing in data-driven promotion strategies for subscription-based businesses across diverse sectors.

• Enable real-time tracking and analysis of customer engagement metrics, including anonymous and authorized user behaviors.
• Develop a predictive model to accurately calculate churn rate and churn percentile for individual customers.
• Integrate with existing traffic analytics platforms (e.g., Google Analytics, Adobe Analytics) to enrich data collection and analysis.
• Provide actionable insights to support targeted customer retention strategies, thereby reducing churn rates and increasing customer lifetime value.
• Build a secure, scalable, and flexible server-side infrastructure to support continuous data collection, analysis, and machine learning operations.

• Server-side data collection API to gather activity data from website plugins.
• Web plugin for monitoring website visitors and calling APIs to retrieve historical engagement data.
• Analysis of anonymous visitor activity (page views, session duration, clicks, visit frequency) to understand pre-subscription user behavior.
• Monitoring and analysis of authorized user activity (subscription details, payment history, purchase patterns, location mismatches).
• Development of machine learning models to calculate and predict churn rate and churn percentile dynamically.
• Integration modules for traffic analytics tools such as Google Analytics and Adobe Analytics.
• Data storage solutions for high-volume behavioral data (e.g., Google BigQuery, MongoDB, Postgres).
• An internal analytics dashboard for viewing both historical and predictive churn metrics.

• Google Analytics
• Adobe Analytics
• Customer engagement platforms

• Real-time data processing and analytics with minimal latency.
• System scalability to support increasing user activity data volume.
• Secure data handling and compliance with privacy standards.
• High availability and fault tolerance for continuous operation.

By implementing this system, the client aims to increase the accuracy of customer churn prediction, enabling targeted retention efforts. Expected outcomes include real-time churn rate and percentile tracking, leading to more effective marketing interventions, improved customer engagement, and reduced churn rates, ultimately driving higher revenue and customer lifetime value.