AI Agentic Churn Prevention System
Orchestrates 7 specialized AI agents that predict churn with explainable ML, perform root cause analysis using NLP sentiment analysis, design personalized interventions with ROI optimization, and execute retention strategies through optimal channels and timing..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Aria - Orchestrator Agent
Churn prevention requires coordinating data engineering, ML inference, analysis, strategy design, and execution across multiple specialized agents with proper sequencing and handoffs.
Core Logic
Coordinates the entire 6-stage churn prevention pipeline, managing task delegation, priority balancing, and approval decisions. Oversees workflow progression from data collection through execution, handles final intervention approvals based on cost thresholds and customer value, and ensures proper agent handoffs.
DataFlow - Data Engineer Agent
Churn prediction requires aggregating customer data from multiple sources (CRM, billing, usage, support) with consistent quality and freshness for accurate ML inference.
Core Logic
Extracts 32+ features from feature store across demographics, behavioral, transactional, and engagement categories. Performs data validation, quality checks (targeting 94.5%+ quality score), and pipeline execution. Computes derived features and ensures data freshness for downstream ML consumption.
Neural - ML Specialist Agent
Predicting which customers will churn and understanding why requires sophisticated ensemble models with explainability for actionable insights.
Core Logic
Runs churn prediction using XGBoost + Neural Network ensemble v3.2.1. Preprocesses features, executes model inference, computes SHAP explainability values, and calibrates confidence scores. Outputs churn probability, risk tier classification (High/Medium/Low), and top contributing factors with their impact magnitudes.
Insight - Analyst Agent
SHAP values indicate what predicts churn but not the underlying customer story. Understanding root causes requires pattern analysis and sentiment interpretation.
Core Logic
Analyzes SHAP values alongside behavioral signals to identify root cause narratives (e.g., 'Life event - usage decline - price sensitivity'). Finds similar customer patterns from historical data (analyzing 100+ similar cases), performs NLP sentiment analysis on support transcripts, and identifies specific churn triggers.
Strategy - Strategist Agent
Multiple intervention options exist (discounts, upgrades, pauses, rewards) but selecting the optimal strategy requires balancing success probability, cost, and long-term customer value.
Core Logic
Evaluates multiple intervention strategies using historical success data and ROI projections. Calculates expected success probability, revenue protection, intervention cost, and payback period for each option. Selects optimal approach (e.g., 33% Family Plan discount with 82% success probability and positive expected ROI).
Engage - Communicator Agent
Retention messages need personalization, empathetic tone, optimal timing, and appropriate channel selection to maximize response rates.
Core Logic
Crafts personalized retention messages with appropriate emotional tone based on customer sentiment analysis. Selects optimal send time based on engagement patterns (e.g., Saturday 8 PM), chooses best channel (email, SMS, in-app), and generates A/B test variants for continuous optimization.
Sentinel - Monitor Agent
Intervention execution requires scheduling, delivery confirmation, customer response tracking, and real-time performance monitoring.
Core Logic
Schedules intervention delivery at optimal times, monitors execution status, tracks customer responses, and generates performance alerts. Provides real-time tracking with unique intervention IDs, measures actual versus predicted outcomes, and feeds results back for continuous model improvement.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
10 technologies
Architecture Diagram
System flow visualization