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Predictive Analytics Platform for Tenant Retention in Commercial Real Estate

gloriumtech.com

Real estate

Challenge in Tenant Retention for Commercial Real Estate

The client manages extensive commercial real estate portfolios but faces significant tenant churn. They require data-driven insights to predict churn probabilities, implement proactive retention strategies through targeted discounts/promotions, and understand causal factors influencing tenant decisions.

About the Client

Commercial real estate management firm operating across the United States with a focus on tenant retention strategies

Goals of the Predictive Analytics Solution

Develop a machine learning pipeline for churn probability prediction
Enable continuous model improvement through incremental data training
Provide explainable AI insights into churn drivers
Integrate with existing property management systems

Core System Capabilities

Machine learning-based churn prediction engine
Third-party data integration for tenant behavior analysis
Interactive dashboards for churn risk visualization
Automated notification system for retention offers
Scheduled model retraining framework
Feature importance analysis for decision-making

Technology Stack Requirements

Python

Microsoft SQL Server

Scikit-learn

Pandas

NumPy

Jupyter Notebooks

External System Integrations

Cloud file management systems
Email communication platforms
Property management databases
Scheduling/automation tools

Operational Requirements

Horizontal scalability for growing data volumes
Real-time prediction response SLAs
Data encryption and access controls
Model performance monitoring dashboard
Audit logging for compliance purposes

Expected Business Impact

Implementation of this solution is projected to reduce tenant churn by 15-20% through proactive retention strategies, improve operational efficiency by automating churn risk analysis, and increase revenue predictability through data-driven lease management decisions.