Infrastructure for Customer Churn & Retention Prediction

Whether operational knowledge is systematically recorded

• Customer interaction records must be systematically captured across all channels (orders, inquiries, returns, complaints) with consistent timestamps and account linkage
• Purchasing frequency, order volume, and product mix history must be stored in a queryable format with at least 24 months of longitudinal depth

How explicitly business rules and processes are documented

• Churn signal taxonomy must formally define leading indicators such as declining order frequency thresholds, payment delays, and reduced SKU diversity
• Retention intervention workflows must be formally documented with escalation paths to account managers and approval thresholds for discount authority

How data is organized into queryable, relational formats

• Customer segmentation schema must classify accounts by tier, contract type, and strategic value so retention scoring can be prioritized by business impact

Whether systems expose data through programmatic interfaces

• Model output scores must be accessible to CRM and sales tooling via a defined API contract so sales reps receive churn alerts in their existing workflow

How frequently and reliably information is kept current

• Retraining cadence must be defined with triggers for concept drift, linking model performance monitoring to scheduled review cycles