Churn Prediction

Predict customer churn using AI to reduce attrition and improve retention strategies

Business impact

• Customer Retention Rate — Increases as at-risk customers are proactively retained through targeted actions
• Churn Rate — Decreases by identifying and addressing customers likely to leave early
• Cross-sell Rate — Improves by enabling personalized offers to engaged customers
• Conversion Rate — Rises due to timely, relevant retention campaigns
• Customer Lifetime Value — Increases as customers stay longer and spend more
• Agent Productivity — Enhances by focusing efforts on high-risk customers with AI insights

Data requirements

• Customer Demographics (Structured) — Used to segment and profile customers for churn risk
• Service Usage Data (Numeric) — Analyzes behavior patterns indicative of churn
• Transaction History (Structured) — Tracks payment and purchase behavior for churn signals
• Customer Interaction Logs (Text) — Captures engagement touchpoints to assess satisfaction
• Event Streaming Data (Numeric) — Provides real-time behavioral data for timely predictions

AI methods and techniques

• Predictive AI — Models customer churn likelihood based on historical and behavioral data
• Generative AI — Generates personalized retention offers and communication content
• Agentic AI — Automates decision-making for next best actions in retention campaigns

AI models and model families

Random Forest, XGBoost, Artificial Neural Networks, Quantum-enhanced Random Forest, Transformer-based models

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Achmea, Itau Unibanco Hldg Sa Ky.