A Netflix-style recommendation engine on steroids, a Google AdWords beater and a souped-up game simulator are among a number of machine learning models at work behind the scenes at online bookmaker Sportsbet, the company has revealed.
“Our goal is to improve the customer experience at every customer interaction,” said Tony Gruebner, the company’s general manager, analytics, insights and modelling.
Speaking at the Gartner Data and Analytics Summit in Sydney last week, Gruebner explained: “One of the best ways of doing that is to build great data products utilising the company’s wealth of data – which is exponentially growing – and also machine learning and artificial intelligence capabilities.”
It makes around 1.7 billion price updates, offers punters 11 million markets and takes 240 million bets every year. On the biggest gambling day of the year – Melbourne Cup day – the company takes on more than 60,000 new customers, and peaks at around 500 bets per second. But gambling is a highly competitive market.
“Great data products allow you to get a jump on your competitors and achieve market share, because it’s very hard to replicate,” Gruebner said.
The bookmaker is currently trialling a recommendation engine, similar to the ones that have helped Netflix and Amazon.com dominate their markets.
“If you go on our site, even on a weekday morning, there’s tens of thousands of things you can bet on and hundreds of thousands of markets and millions of selections. That’s something that our customer love – they love the huge range available to them,” Gruebner said.
“The problem with this however is that the chances are customers are coming with one action in mind. They want to bet on a specific thing…so it’s like looking for a needle in a haystack,” he added.
What was needed was a recommendation engine. And a fast one.
“The extra nuance we have at Sportsbet is the most of the events that we offer have a five or ten min betting window. A horse race for example is a five minute window. Netflix might do a recommender on a movie that’s going to stay on their catalogue for years,” Gruebner said.
“We are doing models on events that are there for five minutes before another lot come in. So there’s this huge churn through of options which makes for a real modelling challenge.”
The answer is what the company calls ‘lookalike models’. Via deep learning models, every customer is matched up to 100 customers who are most similar to them.
Based on the lookalikes behaviour, predictions are made about the customer in questions next action, and recommendations made in realtime.
The models also are able to improve themselves, based on what a customer actually did next.
“If they were wrong the first time, they need to find a way of getting smarter,” Gruebner said.
Although the tool hasn’t yet been used “in anger” testing has shown some impressive results, with recommended next bets reaching accuracy levels of around 40 per cent.
“It gives our marketers and website optimisation teams a real tool to avoid that problem of the needle in the haystack. We can serve the content that’s right for you, while still offering that large amount of products,” Gruebner said.
A bid to beat Google
Sportsbet, like its competitors, is a significant user of Google AdWords through which businesses bid on how high advertising is placed after specified keywords are searched.
How much should be bid?
“This is an interesting problem because for a lot of common words this is relatively easy. We have a lot of information on those words – if someone puts ‘betting’ or ‘Sportsbet’. And we know what the cost per acquisitions. We know what the return on investments is on those words,” Gruebner said.
It is with the less obvious searches that things get tricky, for example ‘storm’: the name of the Melbourne rugby league team and, of course, a weather event.
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