H2O.ai Case Studies AI Helps Property Management Company Maximize Their Business

Edit This Case Study Record

	AI Helps Property Management Company Maximize Their Business H2O.ai

AI Helps Property Management Company Maximize Their Business

H2O.ai

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The data science team was able to iterate with new and existing models much faster than before.
Impact #2	Using Driverless AI enabled the non-technical teams to interact with the data more easily.
Impact #3	The marketing team got a head-start with predicting customer churn rather than starting afresh with building the model.

Impact #1

The data science team was able to iterate with new and existing models much faster than before.

Impact #2

Using Driverless AI enabled the non-technical teams to interact with the data more easily.

Impact #3

The marketing team got a head-start with predicting customer churn rather than starting afresh with building the model.

Download PDF Version

Overview

AI Helps Property Management Company Maximize Their Business

Operational Impact