Analytics 50: How big data innovators reap results

Five winners of the 2016 CIO.com and Drexel University Analytics 50 awards share details of their projects, lessons learned and advice.

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Branden Moore, director of analytics and insights for the Philadelphia 76ers. Drexel University Lebow College of Business

Branden Moore, director of analytics and insights for the Philadelphia 76ers.

Philadelphia 76ers: Winning fans without winning

In the 2015-16 NBA season, the Philadelphia 76ers earned the dubious distinction of having one of the worst seasons in NBA history, with a 10-72 record. The franchise also set the record for the longest losing streak in professional sports, at 28 games. And all that followed two other very poor seasons.

Despite the team’s struggles, fans have remained loyal. The Sixers earned a No. 5 ranking in NBA season ticket sales for the 2014-15 and 2015-16 seasons, and they’re currently No. 2 in the NBA for new season ticket sales.

But the organization was concerned that season ticket holders who had already spent three years waiting for “next year” would begin to lose patience. And by sports industry standards, the Sixers have a relatively small service and retention team, with only six account executives responsible for more than 8,000 season tickets. During the renewal period, it took the six-person team more than four weeks to work through their accounts and contact all the fans on their lists individually. Hoping to make the process more efficient, the organization charged its analytics team with finding a way to use data to help account executives prioritize their time so they could maximize the renewal rate.

Fill those seats

“Season ticket members are the lifeblood of our organization,” says Braden Moore, the team’s director of analytics and insights. “We want each seat to be filled with a passionate season ticket holder for all 41 games. And it’s even more important to make sure the seats are filled for seasons to come.”

To start, Moore, who previously worked in quantitative risk management at the Federal Reserve, and the analytics team gathered all the demographic and psychographic information they could get their hands on — tenure, location, purchase and attendance histories, demographic data in the team’s Acxiom system, CRM touch points, email marketing behavior and more. They then ran the data through machine learning processes (including logistic regression, support vector machines, random forests and decision trees) and developed a two-pronged model that incorporated the following:

  1. Logistic regression to predict each prospect’s likelihood of renewal. This was used to set a base forecast and to determine overall priorities.
  2. A decision tree to gain insights on breaking points of consumer behavior. This was used to tell the story to the account executives in a digestible way. It also identified which types of interventions and levers yielded the most success.

“The Philadelphia 76ers service and retention team is the best in the business — they are ranked No. 2 in the NBA in customer service — and they have been my greatest resource in determining where the information gaps were that would help the team hit its goals for the season,” Moore says. “I definitely wanted to make a model that was useful and delivered insights.”

“We didn’t necessarily have any metrics or KPIs specific to the model,” he adds. “Instead, we had the organizational revenue and retention targets. One of the organization’s core values is ‘Collaboration Wins.’ Therefore, it wasn’t about the success of this analytics project as much as it was a piece of the overall picture.”

With the full support of the executive leadership team, the project included an individualized attack plan for each account executive based on the value and tenure of their accounts. This, Moore says, enabled the salespeople to better understand the intricacies of the retention process, their client value and chances of renewals so they could better focus their time.

Moore says the changes instantly increased the speed and impact of initial sales. In the first week, accounts renewed, seats renewed and overall revenue improved by 3 percent to 4 percent. The service and retention team exceeded the NBA’s projections by 8 percent, and the current renewal rate is second among all non-playoff teams (19 percentage points ahead of the next non-playoff team).

“The team was excited for the results, but as with any new process, it took a little time to put into perspective why the new process was important,” Moore says. “On the surface, listing off coefficients and regression statistics doesn’t seem to help service season ticket members more effectively, but taking time to explain the information allowed the team to utilize key takeaways from the model to add an extra level of strategy when organizing their time in the hectic renewal season.”

Don’t give up

Moore’s advice to executives planning an analytics project is simple (and applies to the Sixers on the court as well): Don’t be discouraged by failure.

“Keep trying,” he says. “Not every project will lead to a robust model with clear takeaways, but you’ll learn something from each iteration.”

“Take time and do your homework,” Moore adds. “I’ve stumbled across numerous new methods or algorithms that I’ve used in subsequent projects just from continuing to research and learn. The field is continuously evolving, so we as professionals have to as well.”

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