AI Case Study

Virgin Australia cuts down the time it takes to build predictive models by up to 90% while boosting accuracy by up to 15% with machine learning

Virgin Australia aims at improving its Velocity Frequent Flyer loyalty program by determining the best time for their customers to redeem their loyalty points. By using machine learning the airline is aiming at predicting who is most likely to travel, prices and the type of travel customers prefer. With the technology, Virgin Australia has managed to reduce the time it takes to build predictive models by up to 90%, while boosting accuracy by up to 15%.

Industry

Transportation

Airlines

Project Overview

Using DataRobot's automated machine learning service, Virgin Australia is looking to build models that can predict the types of people that are more likely to travel, the types of travel people are likely to undertake, the prices that travellers are willing to pay, the importance of accommodation relative to travel, and the importance of experience compared to travel. (zdnet)

Reported Results

Using DataRobot's automated machine learning service, Virgin Australia has been able to cut down the time it takes to build predictive models by up to 90 percent, while boosting accuracy by up to 15 percent. (zdnet)

Technology

Predictive analytics

Function

Marketing

Customer Management

Background

Benefits

Data

Previous purchases to predict future preferences, personal information and preferences data