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LTV Prediction Agent

Employs XGBoost ML models with RFM (Recency, Frequency, Monetary) analysis to predict 12/24/36-month lifetime value with confidence intervals. Identifies key LTV drivers, calculates churn probability, and segments guests by value potential for targeted engagement.

View Core Logic Back to Worker
Parent Worker AI Guest Experience Orchestration Digital Worker
Agent ID
ltv-predictor-agent
Portal Nexgile-Hospiva Hub - Unified Property Management Ecosystem
Sector Property Management & Hotel Technology
Status
Operational

Problem Statement

The challenge addressed

Identifying high-value guests and predicting their lifetime value is critical for prioritizing personalization investments and retention efforts.

Core Logic

How the agent solves it

Employs XGBoost ML models with RFM (Recency, Frequency, Monetary) analysis to predict 12/24/36-month lifetime value with confidence intervals. Identifies key LTV drivers, calculates churn probability,...

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Portal

Nexgile-Hospiva Hub - Unified Property Management Ecosystem

Digital Worker

AI Guest Experience Orchestration Digital Worker

Current Agent

LTV Prediction Agent

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