Sports analytics leaders are now using data to understand fans as well as they know their players.
Thanks to the long marriage between statistics and professional athletics, some of the most advanced users of analytics tools are found in sports. But as the use of analytics spreads from the playing field into business operations, the industry is addressing many of the same adoption challenges that confront business leaders in every sector.
The recent 2018 MIT Sloan Sports Analytics Conference (SSAC) delved into how teams and leagues are using analytics to boost revenue, and how they’re managing transitions in culture and strategy.
Know Your Customer
The rapid uptake of digital ticketing in sports is helping teams better identify their true customers for live events: the fan attending the game, rather than the individual who purchased the ticket. That’s important information for loyalty programs like “Magic Money” — credits awarded to fans of the NBA’s Orlando Magic that can be spent on seat upgrades and other purchases made via the team’s mobile app. The team is also running predictive analytics to forecast attendance and identify season-ticket holders who may be no-shows for a particular game. It can then proactively email those fans to offer the opportunity to transfer their tickets to someone else. When the effort is successful, it captures parking or concession-stand revenue that might otherwise be lost.
Outside the arena, Ticketmaster Entertainment Inc.’s data group is pioneering a “verified fan” program with an eye to putting high-demand tickets directly into the hands of people who want to attend an event, not resellers whose jacked-up prices pinch consumers and don’t benefit venues or performers. Fans register online, and Ticketmaster uses a scoring algorithm to weed out brokers and bots, according to Ticketmaster senior vice president and head of data and marketing services John Forese, who spoke at the conference. Verified fans then receive a code to use when tickets go on sale.
At ESPN Inc., the quest for deeper insight into customers includes bringing fans into its media lab to observe them as they watch a game. It uses eye-tracking and heart-rate sensing, and maps their emotional state based on what is happening during the game, Vikram Somaya, ESPN’s senior VP, global data officer said at SSAC. The sports media giant has built a broad taxonomy of in-game events that it can code in real time, such as scoring a goal, a turnover, or an injury to a player. Understanding how fans are reacting in those moments and being able to act on the insight immediately can help ESPN improve results for marketers, Somaya said. ESPN can deliver specific messages at times when an audience may be more receptive: For example, running an ad for an insurance company before a hotly contested game has resulted in better performance for the advertiser.
Analytics leaders must identify what they most want to know about their customers. “What are the relevant pieces of information to get from a fan as we continue down the path of knowing who they are?” asked Doris Daif, senior vice president for customer data strategy at the NBA. When her group knows a fan’s favorite team and favorite player, for example, the likelihood of successfully selling that individual merchandise or a streaming package increases by a multiple of five to 15 — even if that information isn’t used to customize a creative message, she said.
As in other industries, more data sharing will be key to gaining the insights that improve fan engagement, according to Daif. “In the next five to 10 years, the consumer has to be king — and they haven’t been,” she said. “It’s naive to think that any team, league, or media company can operate in a vacuum. We need to start partnering around data.”
Get Your Data House in Order
The volume of data available in the sports industry is exploding, said Jessica Gelman, CEO of the Kraft Analytics Group, a subsidiary of Kraft Group LLC, based in Foxborough, Massachusetts. “Ninety percent of the data we have today was created in the last two years, and it’s doubling annually. It’s been a competitive advantage, and now teams, leagues, and partners understand that they need to get their house in order to deal broadly with data as an asset.”
Given the array of new data sources, organizations must have a big-picture, more strategic view of technology investments, Gelman said in an interview after the conference. “For example, you may invest in a new social media tracking tool or have Wi-Fi in your stadium. It’s not just about having these new technologies, but knowing what data you’re going to pull out of them in order to gain a better understanding of both your customer’s experience and your overall business operations. Then, leverage those insights through data management, a data warehouse, data visualization, to help you make decisions. Many organizations understand that they need something, but the application of that technology to actual decision-making — what I call, ‘so what?’ — is a real hurdle.”
Convert Your Culture
Traveling that last mile from insight to influence is central to a successful data and analytics strategy and requires leadership support and attention to changing the organizational culture.
“Culture is the most important thing,” Brian LaFemina, senior vice president for club business development at the NFL, said at SSAC. There has to be buy-in at the ownership level to the idea that it’s no longer a “gut” business, he added. “You aren’t going to press a button — now we’re an analytics-driven organization.” In his experience, creating a team that mixes functional experts with data analysts has helped build organizational support for incorporating analytics in decision-making.
The Orlando Magic began its move to analytics with a data warehouse nine years ago, Chief Operating Officer Charles Freeman told conference attendees, and some on the business side were initially reluctant to give up their gut instincts for recommendations from data analysts. “It takes time to get the organization to an analytical mindset,” he said. “Find that person who can interpret data and explain it to the front-line folks who can really do something with it. You can have all the analytics in the world, but if you can’t do something with it effectively, it’s all for naught.”
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