The Other ‘Moneyball’: Using Analytics to Sell Season Tickets

Greer_Stadium_blue_seats_2

The Milwaukee Brewers are hardly the most successful team on the field in Major League Baseball. They have never won a World Series, and have only been in the playoffs four times since moving to Wisconsin from Seattle (where they were the Pilots) in 1970.

Yet despite their lack of on-field prominence, the Brewers excel in using analytics to retain season and partial-season ticket holders, according to Major League Baseball senior manager of advanced analytics Matt Horton.

They are updating their analytics constantly. They are a model for what we do off the field with analytics Twitter ,” said Horton, who spoke at the recent Wharton Customer Analytics Initiative conference on a panel titled, “What Are the Best Indicators of a Baseball Season Seat Holder Renewing Their Season Seats?”

When people think of analytics in baseball, they more than likely envision Moneyball, the movie based on Michael Lewis’s book about the Oakland Athletics, and how the organization used analytics to build a better team with players undervalued by other franchises.

While it’s good business for major league franchises to field winning teams, it is also important — because they are businesses — that they attract and retain loyal fans. So, according to Horton, and his co-panelist, Milwaukee Brewers data analyst Diny Hurwitz, teams want to keep up with the latest in analytics to help them do so.

“We are concentrating [on] people spending a lot of money — season ticket holders. That is really a very small number of customers.” –Matt Horton

The ultimate purpose of using analytics in his niche of the sports world, said Hurwitz, is to get more people to purchase season (or at least multiple-game) tickets, induce them to actually use them, and find ways to have them purchase them year after year, irrespective of the team’s place in the standings.

In 2000, all major league clubs agreed to start using MLB Advanced Media to operate their websites, noted Horton. “While they may not do this on the field, both small- and big-market clubs would have the same experience online,” he said. “There are certainly a lot of different ticket buyers. Some people go to one game a year and others do a bit more.

“But we are concentrating here [on] people spending a lot of money — season ticket holders,” said Horton. “That is really a very small number of customers, but Diny and I started working on this in earnest in 2011, and we are already in the third iteration of data analytics.”

Among the main things Horton and Hurwitz were looking for were indications of loyalty, which they determined was a key factor in those who would renew season ticket or multi-game plans. In that context, they said, it is one thing to buy a ticket, but another to use it. For that reason, they mine data from the ticket scanners each major league club uses when fans enter the stadium to attend a game.

“Clubs capture the data to see if the ticket they sold for those plans is used,” said Horton — though admittedly, a ticket could be used by someone other than the buyer. “But even in that case, someone is actually going to the game,” he said.

Analyzing E-mail

Hurwitz then added three more steps for his analysis.

First, every email that the Brewers get from fans is analyzed. Presumably, Hurwitz noted, people who take it upon themselves to communicate with the club directly are potential loyal customers. Next, he partnered with a demographic data provider to better hone age, income, residence and gender data, among other variables, for use by the sales staff. Finally he sought more traditional and less absolutely quantifiable data: The Brewers survey season ticket holders at the end of each year to see what the fans think can be improved, with the goal of making sure they return again and again.

Hurwitz added that each season, he looks at the data in different ways to see if it suggests better methods for the Brewers’ ticket sales staff to use. “When you do analytics, you have to remember that before you implement new processes and new ways of looking at data, you have come from someplace,” said Hurwitz, meaning that you can’t throw out what has been done in the past completely, but must instead learn to modify it. “When looking at new strategies, though, you have to re-create the wheel.”

“The only thing these clubs have in common is that the teams play major league baseball. Every market can be different.” –Matt Horton

The Brewers employ just 24 ticket sales reps, each of whom must deal with thousands of customers each season. No matter what his metrics say, Hurwitz noted, there is only so much he can recommend for those heavily taxed sales reps to do.

For instance, last year, he thought he could collect more relevant data on potential season ticket buyers if he could get each sales rep to ask customers even one or two questions about their purchasing decisions. “But the sales reps had to make so many calls and were working so hard that they did not want to take the five or 10 seconds more — so we determined it was not worth the extra time for that data,” said Hurwitz.

Hurwitz then tried to determine if it would be a good idea to have the sales reps prioritize their calls — whether some prospects were more likely to renew than others if they got an earlier call. He presumed the data would show that the reps should call the most likely to renew first. Yet it turned out that the opposite was true: Data showed that calling less-likely renewals earlier was more productive.

“The best customers would be buying within a call or two,” said Hurwitz. “So if someone is mostly likely to renew, why should that be the first call to make? They may even renew online without any phone call at all. But you may be missing out on leads if you let people who are less likely to renew go until later.”

On and Off the Bandwagon

According to Hurwitz and Horton, their most unusual finding was that the team’s on-field performance was not necessarily linked to whether fans would renew season tickets. “There are ‘on the bandwagon’ fans who will not renew if the team does not do well,” said Hurwitz, “But we have people who have had 81-game plans since 1975. Now that is a fan.”

It has become apparent based on the analytics, said Horton and Hurwitz, that the kind of customer who buys a season ticket plan — or even, in some cases, a 10- or 20-game plan — is a completely different kind of fan than the occasional game attendee.

“If you have someone who has renewed for 10 years and he finds out that someone waited to renew and got better benefits, then it will hurt you in the long run.” –Diny Hurwitz

“We have tens of thousands of people around the country who go to two or three games a year,” said Horton. The ultimate goal of the analytics in this case is to figure out how to turn those occasional visitors into season ticket buyers. Some may not have that kind of money to spend, and a large majority will, of course, want to parcel out their entertainment dollars among many kinds of leisure activities, rather than putting such a significant sum toward a single sport. Ultimately, though, there is a belief that the season ticket holder — or at least the partial season ticket holder — is the best kind of fan, the one who will upgrade seats or buy team paraphernalia. And that’s the kind of fan Major League Baseball is asking analytics experts to find.

Hurwitz said he is not afraid to help other major league teams with their marketing, nor does he have any qualms about sharing his analytical algorithms and models with them. “If you are Kohl’s, it is not likely that you are going to share data and results with Target,” said Hurwitz. “But Major League Baseball is different. We don’t care whether someone goes to a Pittsburgh Pirates game. The more likely a fan is to buy a ticket — that is one step up the scale to buying more tickets, and maybe soon a 20-game plan.”

Horton also encourages this collaboration from his perch at the league’s main office. “The only thing these clubs have in common is that the teams play major league baseball,” said Horton. “Every market can be different. You do not just roll out one set of data and have it in the same model. Everyone can help everyone, which is why we have everyone doing analysis.”

The Personal Side

Horton and Hurwitz stress, though, that there is a personal element to sales, something analytics may only touch on. “You have to have a certain kind of sales tone in each market and with each type of customer,” said Hurwitz. “Sometimes it is really good to treat customers differently, but you have to be careful with giving discounts, especially late in the cycle.

“It is one thing, say, if you are on an airline and you find out that your seatmate got a bargain because he booked six months earlier. That you can understand,” he noted. But some teams may want to lower the price of season tickets as time goes on, especially when it comes to customers who have supported the franchise for ages. “If you have someone who has renewed for 10 years and he finds out that someone waited to renew and got better benefits, then it will hurt you in the long run,” Hurwitz pointed out.

“It will encourage people to wait, which is not what you want,” he added. “We are hoping that analytics, in the end, will refine all that and we will get more and more people to buy bigger plans.”

Citing Knowledge@Wharton

Close


For Personal use:

Please use the following citations to quote for personal use:

MLA

"The Other ‘Moneyball’: Using Analytics to Sell Season Tickets." Knowledge@Wharton. The Wharton School, University of Pennsylvania, 21 July, 2015. Web. 11 December, 2016 <http://knowledge.wharton.upenn.edu/article/the-other-moneyball-using-analytics-to-sell-season-tickets/>

APA

The Other ‘Moneyball’: Using Analytics to Sell Season Tickets. Knowledge@Wharton (2015, July 21). Retrieved from http://knowledge.wharton.upenn.edu/article/the-other-moneyball-using-analytics-to-sell-season-tickets/

Chicago

"The Other ‘Moneyball’: Using Analytics to Sell Season Tickets" Knowledge@Wharton, July 21, 2015,
accessed December 11, 2016. http://knowledge.wharton.upenn.edu/article/the-other-moneyball-using-analytics-to-sell-season-tickets/


For Educational/Business use:

Please contact us for repurposing articles, podcasts, or videos using our content licensing contact form.