Scientific Games VP of Analytics & Insights Cameron Garrett explains how his team takes inspiration from Moneyball and applies it to the lottery industry.

In a pivotal scene from the 2011 film Moneyball, Oakland A’s general manager Billy Beane, played by Brad Pitt, sits at a table and tells his staff of older veteran baseball talent scouts: “We’ve got to think differently.”

A major motion picture based on a 2003 book by Michael Lewis, Moneyball tells the story about how the As went from an average ballclub to one of Major League Baseball’s best by taking a highly analytical approach to scouting players. Challenging commonly-held beliefs and re-evaluating their strategy, the As built a team that would win a record 20 consecutive games in 2002 and prove that harnessing data can yield amazing returns. As a result, many teams adopted the ‘sabermetrics’ innovated by the As.

To push the lottery industry forward, Scientific Games continues to put its money on analytics. Recently, the company made even more investments in its research capabilities, developing technology and adding data scientists to help lotteries think differently.

Leading the charge is Cameron Garrett, VP of Analytics & Insights, Lottery, for Scientific Games. His efforts to drive the industry’s analytical future are already being recognised. In February he was one of three Scientific Games executives to feature in the Gaming Intelligence HOT 50 2019.

Rethinking analytics

“At the bottom of the Oakland experiment was a willingness to rethink baseball” – Michael Lewis, Moneyball.

To paraphrase this quote, at the bottom of Scientific Games’ insights-focused approach to helping their customers is a willingness to rethink analytics. As lottery systems have become more complex, there is more data available to lotteries than ever before.

“The industry has a clear desire to become more datadriven, and it’s something we feel on a daily basis,” says Garrett, who holds an MSc in information systems from Northwestern University. “Of course, this requires the proper resources, including the time, the people and their focus.”

With so much data available, it only makes sense to analyse it in a modern and efficient way. Garrett points to the company’s iLottery systems and the SCiQ instant game ecosystem as two examples of how new technology has yielded high-value data. They bring “a wealth of consumer behaviour and engagement data, and an entirely new focus on gleaning insights at retail,” he says.

This data goes full circle when it informs Scientific Games’ innovation of new products and technologies that make buying, playing and redeeming lottery games more convenient and fun for consumers.

Fielding a team

“If you challenge conventional wisdom, you will find ways to do things much better than they are currently done” – Bill James, Moneyball.

Like a baseball manager, Garrett has built a team of skilled professionals who excel at their individual positions, yet play well together.

When he joined as a marketing analyst in 2008, analytics were not as prevalent in the lottery business as they are today, and he was one of just a few Scientific Games’ employees focused solely on analytics.

Garrett has since created the company’s first Analytics & Insights department. Including Garrett, the group comprises nine members - enough to field a baseball team - with plans in the works to add more data science leadership. Each member of the team performs a key function.

“We structured the team by enabling the right roles to be focused on the right tasks, and they have diverse skillsets, backgrounds and capabilities,” Garrett says. “Our experience shows us the best way to configure these resources to maximise analytical value.”

Scientific Games is already reaping the benefits of this approach.

“It allows the company to take on complicated predictive analytics projects that would otherwise be left to consultants who don’t know the nuances of our business as well as we do,” says Garrett.

Additional benefits include the ability to rapidly build and deploy prototypes and solve complex data integration challenges without burdening other internal groups.

Covering the bases

“If you have 12 different pitchers, you need to speak 12 different languages” – Rick Peterson, Moneyball.

Scientific Games’ Analytics & Insights group supports the full line-up of services that the company provides, including instant games, lottery gaming systems and digital, and will be adding new verticals such as sports betting as the company expands. In what can seem like speaking several different languages, they provide the comprehensive insights that each diverse business unit values. The goal is to help the company’s account teams provide better customer support and be able to answer lottery questions with a bullpen of resources readily available.

“Internally, we have made some great strides to empower our sales and account teams with these capabilities,” says Garrett. “Instead of a culture of ‘We’ll get back to you’, we want to be proactive in bringing ideas to the table, being able to answer lottery industry and customer performance questions on the fly and have a deep understanding of what drives industry trends.”

Garrett’s team is beginning to support external customers and put data directly in lotteries’ hands as well, with the company’s core business intelligence product, Infuse. Infuse combines data from players, games, retailers, equipment and logistics to offer lotteries meaningful insights, macro-level trends and key performance indicators from the industry.

“Our people are working tirelessly across technology, business and operations to launch Infuse for several key customers,” says Garrett.

Squeeze play

“Hypothesise, test against the evidence, never accept that a question has been answered as well as it ever will be” – Bill James, Moneyball.

Squeezing the most value out of a vast amount of data takes special talents, and Scientific Games’ Analytics & Insights group is always seeking better ways to answer industry questions. Garrett and his team are actively refining their processes, data structure and outputs to maximise value.

However, one of the challenges they face doesn’t lie in the mountains of data they process. It comes from an industry culture that can be very conservative and resistant to change. This is particularly difficult from a data science perspective, which requires change in order to effectively measure and provide recommendations.

There is a lack of variety on a number of high-level topics, says Garrett, such as testing different payout strategies, analysing various types of prize structures on a granular level and trying new game concepts.

“When you ask ‘What is our optimal payout percentage to maximise gross gaming revenue?’ and we don’t see any payout variety, it becomes borderline impossible to answer that question scientifically,” he concludes.

Calculated risk

“No matter how successful you are, change is always good. There can never be a status quo” – Billy Beane, Moneyball.

Virtually every industry is becoming reliant on data, from transactional information to social media, to guide decision-making. With millions of transactions occurring every day at retail and online, there is no shortage of data at lotteries’ disposal. Lotteries have a choice of either making some changes to stay in the game or sticking with the status quo.

“In an industry that is looking to become more data-driven, we have to be open to accepting that it will come with some ‘risks’ of pulling levers,” says Garrett. “But in my mind, those risks should be responsibly embraced as opportunities to test, learn, refine and ultimately improve our ability as an industry to give consumers what they want, and maximise the funding returned to beneficiaries.”

Though they might not be taking the field against the New York Yankees anytime soon, the team members of Scientific Games’ Analytics & Insights group are helping lotteries play to win for the many good causes they support around the world.