Implementing ML-Driven Customer Churn Prediction and Automated Marketing Workflow in ECommerce Platform

The client faces difficulties in effectively predicting subscription cancellations and automating personalized marketing campaigns. Existing manual processes limit campaign efficiency and responsiveness, leading to suboptimal customer retention and increased operational costs.

A mid-sized online retail or SaaS ecommerce company seeking to enhance customer engagement, retention, and marketing automation through machine learning and cloud-native solutions.

• Develop and integrate machine learning models to accurately predict customer churn probabilities based on behavioral data.
• Create a configurable, automated email marketing workflow leveraging serverless cloud architecture.
• Enable dynamic customer segmentation for targeted marketing interventions.
• Reduce manual effort and time required for campaign creation through template management and automation.
• Improve system flexibility, scalability, and maintainability by migrating to a cloud-based infrastructure.

• Machine learning models to calculate and update customer churn probabilities dynamically.
• Automated email campaign generation based on churn prediction data, including personalized content tailored to customer segments.
• Configurable campaign management interface, including scheduling, segmentation, and reusability of templates.
• Data collection and analysis pipeline to gather user behavior data during email workflows.
• Dashboard visualizations displaying predicted churn rates, campaign analytics, and revenue impact estimations.
• Frontend template builder allowing drag-and-drop creation of email blocks to expedite campaign design.

• Customer data repositories and behavioral analytics platforms
• Email delivery services (via APIs)
• Billing and subscription management systems
• Campaign analytics and reporting tools
• APIs for external configuration and campaign triggers

• Scalable architecture supporting multi-tenant environment with multiple clients.
• High availability and fault-tolerance to ensure uninterrupted campaign delivery.
• Data security and compliance with global regulations (e.g., GDPR).
• Response times for campaign configuration and personalization should be under 2 seconds.
• Automated deployment pipelines for continuous updates and model retraining.

The implementation aims to significantly increase customer retention rates through accurate churn prediction and targeted email campaigns. It is expected to reduce manual campaign setup time by up to 70%, lower operational costs via automation, and boost overall revenue by effectively re-engaging at-risk users. The scalable cloud infrastructure will support future growth and feature expansion.