How machine learning is helping Virgin boost its frequent flyer business
Companies that are able to adapt to a world where innovation is increasingly driven by machine learning, or artificial intelligence more broadly, are the ones that will come out the other end of the tunnel and thrive, according to Oliver Rees, GM of Torque Data at Virgin Australia.
Rees, whose data analytics consultancy firm Torque Data was acquired by Virgin Australia in 2015, told ZDNet that one of its tasks has been “reengineering [Virgin’s] analytical capability”, ensuring the airline is well-prepared to embrace the opportunities that are offered by machine learning.
While not new to machine learning, Virgin Australia has been seeking better methods of developing, applying, and assessing machine learning algorithms, recently turning to Massachusetts-based company DataRobot, which operates on the belief that automated machine learning will not only increase productivity for data scientists, but also open up the world of data science.
Rees told ZDNet that Torque, as the data analytics arm of Virgin, has been investigating ways to improve customer experience for members of Virgin’s Velocity Frequent Flyer loyalty program.
“We want people within our program to be able to redeem points for great experiences, and to do that, we want to be able to better predict when is the best time for particular people to redeem points and what should they be redeeming them against,” Rees said.
“For a given individual or a group of people, we want the ability to be able to better understand what it is that they might be really interested in doing at a certain destination — from both a business and a leisure point of view — and how can we serve them better at that destination, and then be able to tailor that to their specific requirements and serve that up to them in a meaningful way.
“I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.”
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.
A lot of information is provided willingly by customers, Rees said, but the company also uses previous purchases to predict future preferences.
“There’s a lot we can impute from what they do already, but also by actually looking at events and the type of events that people like to attend — that’s a really powerful piece of information,” Rees said.
“It all comes back to using what we understand already about people, and not necessarily as individuals either, but as groups, so we look at tribes and cohorts … Often people want guidance around what do people like me do and how can I as an individual benefit from the learnings of others.
“We need to not only understand people based on what they tell us themselves, but also what people like them are telling us, what experiences people like them are enjoying … that’s where the competitive battleground is.”
Using technology provided by DataRobot, which raised more than $124 million since its inception in 2012, Rees said Torque has been able to build new predictive models at one-tenth of the time it had previously taken, and the models are up to 15 percent more accurate than previous ones.
“We have the ability to run multiple different statistical techniques against the same dataset in a very short space of time and have this competitive element whereby the models compete against each other for the best outcome,” Rees said.
