Machine learning-powered predictive analytics are generating a significant uplift in campaign response rates and member satisfaction for Virgin Australia’s frequent flyer program, Velocity, according to its CEO, Karl Schuster.
While new technology is allowing the Velocity Frequent Flyer program to deliver personalised offers at scale, Schuster says it is people at the heart of the success. The scarcity of local analytics talent and the rise of automated solutions has made data engineers in particular a prized resource.
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How Virgin Australia uses predictive analytics
Velocity Frequent Flyer has been “aggressively” growing its member base in recent years with a focus on customer experience through innovation.
The company uses the data one would expect, like flight history and payment methods, and combines it with data points from its broader loyalty program, which can draw in information on wider member spending habits.
The data helps anticipate the most valuable offer for members and their propensity to respond, with the ultimate goal of driving engagement.
Velocity partners with DataRobot, a firm which automates much of the machine learning algorithms responsible for matching offers, allowing the program to operate at scale and encouraging adoption on the business side.
“The machine learning predictive analytics allow us to personalise our communication with members so that we deliver offers to them that are most relevant to their lifestyles, interests and goals,” Schuster told Which-50.
“[We are] predicting not only the members who are most likely to respond but also which offers are most likely to appeal to them. This is personalisation at scale, and also increases member engagement with the communications that we send them, which is part of our strategy.”
With machine learning doing much of the heavy lifting in terms of data analysis, prediction, and delivery there is less of a reliance on rare data analysts. Velocity still relies heavily on its data engineers, however, to maintain and manage databases. Velocity’s data engineers are especially valuable, Schuster says, because they understand the role in the context of customer experience.
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Research suggests marketers are investing heavily in analytics but confidence in the technology trails all other marketing activities, with talent remaining a widespread roadblock.
According to the Velocity Frequent Flyer CEO, “The biggest challenge of running this predictive analytics program is finding and keeping great analysts and data planners.
“These planners are a scarce resource and we are lucky to have some great people in our team who really understand the importance of the member experience – not just the power of the data.”
Finding talent locally is a “difficult task” because of the short supply of data analysts but Virgin is alleviating the problem with technology and data engineers.
“Great engineers – like the team here at Velocity – are the new ‘unicorns’,” Schuster said.
“As automation becomes ever more vital the engineers will become increasingly important to the business models of the future.”
Responsible data use
Predictive analytics walks a fine line between anticipating and preemptively responding to customer behaviour and unsettling consumers, often unaware of modern data collection and use — rampant in some instances.
The responsible use of data, however, is rarely creepy, Schuster says, a point reinforced by members’ positive response to personalisation and relevant messaging.
“We place great emphasis on respect – a principle which helps us ensure we are responsible in the way data is used. Members recognise this and appreciate the way it helps them to target their behaviours and reach their travel or personal goals sooner.”
For organisations considering predictive analytics, Schuster urges a fail fast approach with a well resourced program.
“Don’t be afraid of failing fast, but just ensure that you learn faster. I believe it’s also best practice to hire the best people that you can afford – don’t cut corners as the people are more important than the technology.”
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