Customer Problem
A leading US commercial real estate and rental housing company faced unpredictable tenant churn with many lease non-renewals. This caused revenue instability, increased operational costs for tenant acquisition and unit preparation, and limited insight into why tenants left or who was likely to leave next.
Data Challenge
The client had scattered data across lease records, tenant behavior, service requests, and payment history. The challenge was to integrate and cleanse this diverse data, handle missing values, and extract meaningful features to predict churn accurately. Additionally, data privacy and governance needed to be ensured.
How Fusefy Uses Generative AI to Accelerate Data Science
Fusefy leveraged generative AI to accelerate data exploration, feature engineering, and model development. Generative AI assisted in automating data preprocessing scripts, generating synthetic data to augment training sets, and producing explainable model insights. This reduced development time from months to weeks and enhanced model interpretability for business users.
Ideation Studio
Fusefy conducted AI design thinking workshops with the client’s stakeholders to identify key churn drivers and prioritize use cases. The ideation studio fostered collaboration between data scientists, property managers, and business leaders, ensuring the solution addressed real-world challenges and was user-centric.
Architecture and Project Plan
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- Data Platform: Microsoft Fabric OneLake and Data Warehouse centralized tenant data.
- Data Governance: Azure Purview ensured data lineage and compliance.
- ML Platform: Azure ML Studio hosted the gradient boosted trees churn model with monthly batch scoring.
- Visualization: Power BI dashboards delivered actionable insights to property managers.
- Cloud Infrastructure: Azure provided scalable, secure compute resources.
- Programming: Python was used for model development and automation.
The project plan included data integration, model development, dashboard creation, and iterative feedback cycles aligned with lease renewal timelines.
Synthetic Data Generation
To address data sparsity and enhance model robustness, Fusefy generated synthetic tenant data reflecting realistic lease and behavior patterns. This synthetic data augmented training sets, improved model generalization, and preserved tenant privacy by reducing reliance on sensitive real data.
Code Generation
Generative AI tools were employed to automate code generation for data preprocessing, feature engineering, and model evaluation pipelines. This automation accelerated development, ensured coding best practices, and enabled rapid iteration on model improvements and dashboard features.
Model Card
| Attribute | Description |
|---|---|
| Model Type | Gradient Boosted Trees |
| Input Features | Lease data, payment history, service requests, tenant demographics, neighborhood factors |
| Output | Tenant churn risk score and key contributing factors |
| Performance Metrics | AUC-ROC: 0.87, Precision: 0.81, Recall: 0.78, F1 Score: 0.79 |
| Explainability | Feature importance and tenant-level churn drivers provided via dashboard |
| Update Frequency | Monthly batch scoring aligned with lease cycles |
| Security & Privacy | Data lineage and governance via Azure Purview; synthetic data used to enhance privacy |
Final Outcomes
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- Improved Retention: Early identification and targeted interventions reduced tenant churn.
- Cost Savings: Lower turnover decreased marketing, unit prep, and onboarding expenses.
- Enhanced Tenant Experience: Proactive engagement made tenants feel valued, improving community satisfaction.
- Operational Efficiency: Teams transitioned from reactive to data-driven retention strategies, reducing workload.
- Rapid Deployment: Generative AI accelerated development, delivering a functional solution in weeks.
- Scalable & Secure: The solution leveraged Microsoft Fabric and Azure for enterprise-grade security and scalability.
This AI transformation has positioned the client to face future churn risks with confidence. With data-driven playbooks, predictive dashboards, and a centralized tenant intelligence hub, the organization is now equipped to anticipate, act, and adapt — no matter what shifts occur in the housing market.
Tenant churn may once have been a mystery. Today, it’s a manageable metric — thanks to Fusefy’s generative AI solution.
AUTHOR
Gowri Shanker
@gowrishanker
Gowri Shanker, the CEO of the organization, is a visionary leader with over 20 years of expertise in AI, data engineering, and machine learning, driving global innovation and AI adoption through transformative solutions.
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