Customer, order, and product information was spread across CRM, operational systems, and spreadsheets, making it difficult to identify which customers needed attention and why.
Customer Growth & Retention
Turning Fragmented Customer Data into Ready-to-Approve Retention Actions with AI
Connects customer, order and product data to flag at-risk accounts and prepare retention actions, cutting review time by 70%.
Company Context
Specialty Food Manufacturing & Distribution
Built With
Structured customer/order data pipelines feeding the analysis layer.
Capabilities Demonstrated
AI AgentsRAG & Business KnowledgeBusiness-Rule ValidationHuman-in-the-LoopAuditabilityEvaluation & Observability
A unified customer analytics layer that moves from raw data to a prepared action: Customer + Order + Product Data → Customer & RFM Analysis → Retention & Growth Signals → AI Agent Investigation → Recommended Action → Ready-to-Approve Outreach. The system doesn't stop at "this customer is at risk"; it moves to "here's why, here's what we recommend, here's the action we've prepared. Approve?"
Key Capabilities
Unifies customer, order, and product data into one analytics layer
Runs RFM (recency, frequency, monetary) analysis and derives retention/growth signals automatically
AI agent investigates flagged accounts and explains the drivers behind customer risk
Prepares a targeted, ready-to-approve retention action for every flagged account
Routes every prepared action through a human approval gate before it reaches a customer
A 70% reduction in time to identify priority accounts and prepare follow-up actions.
65–80% of AI-prepared retention actions approved by account teams without modification.
Human-in-the-loop approval gate before any customer-facing action ships
Audit trail on recommended-vs-approved actions
Deterministic RFM scoring and segmentation rules, kept separate from the AI-driven investigation and explanation layer
Evaluation of recommendation quality against approval and rejection outcomes over time