AI Helps Property Management Company Maximize Their Business
Company Size
1,000+
Region
- Asia
Country
- Singapore
Product
- H2O Driverless AI
Tech Stack
- Machine Learning
- Amazon Web Services (AWS) Lambda
- Java
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
PropertyGuru is a leading property management company based in Singapore. They connect property seekers to real estate agents with the mission to help people make confident property decisions by providing them with relevant content, actionable insights, and world-class service. Users of their app upload thousands of photos of their listings for rent or sale every day. In a fast-moving mobile-first real estate market like Singapore, they needed their app experience to be responsive, accurate, and be able to operate at scale at the same time.
The Challenge
Property Guru, a leading property management company based in Singapore, handles a large volume of listings and had looked to leverage AI and machine learning (ML) for multiple use-cases - image moderation, predicting churn, forecasting credit, measuring performance of listings. They realized early-on in their development that they needed machine learning techniques to manage user data, user retention and ensure the customer experience on their app lives up to their reputation. Doing this manually was not scaling so there was a real need to automate their ML process.
The Solution
PropertyGuru turned to H2O Driverless AI to implement AI for multiple use-cases. They found that they could use Driverless AI for the entire end-to-end ML pipeline including uploading data from most of their sources into Driverless AI - images, churn, tabular data, etc. They could visualize this data in a few sections using the AutoViz capability and detect outliers and anomalies. They were able to build the model much faster using pre-existing recipes such as the churn models available. In addition, they also took advantage of the automatic model building process - feature selection, feature engineering, hyperparameter tuning, and deployment. Lastly, they were able to seamlessly deploy multiple models directly into Amazon Web Services (AWS) Lambda service, from within Driverless AI. They were able to deploy different models simultaneously using Java objects and see their performance on live data.
Operational Impact
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