What pro soccer knows about predictive analytics marketing

By Jeff Alford, SAS Insights editor

In the post-Moneyball era, sports franchises across the globe are better equipped make catch-or-release decisions about athletes based on data and predictive analytics.

Of course there is another group that you could argue is even more important to the success of a sports team – the fans. The most loyal will stay with team through good times and bad, but without a broad base of supportive fans, the economic reality is that a franchise can fail.

Competition for sports fans attention is greater than it has even been, so now more than ever, sports leagues must use data and analytics to foster and nurture fan loyalty. Major League Soccer (MLS), the professional soccer league in the US and Canada, has realized this and is using marketing analytics to enhance its marketing efforts.

Using predictive analytics marketing

Instead of the marketing-as-usual approach of sending bulk, impersonal emails to the fan base, MLS realized that it needed to think differently. They’ve developed a four-phase approach that relies on predictive analytics. Their goal is to move fans (customers) from one phase to the next based on a series of activities.

Predictive analytics marketing can answer questions such as:

  • What will my revenue look like in the second half of the year?
  • What activities produce the best return?
  • How can I optimize marketing practices?

The questions that this type of analysis can answer are numerous and varied. Because every organization is different, you need to decide which questions are most important for your organization to answer.

The following is a summary of MLS’ fan marketing strategy, and you can download our white paper Marketing Automation Drives Sports Fan Engagement to get more ideas about how this approach might work for you.

Download the paper

What to track

MLS uses predictive models to ehanced each phase of their customer engagement strategy:

  • Data acquisition – will our databases continue to grow? Are we adding new data sources?
  • Engagement –will our marketing efforts improving our response rate?
  • Monetization – will revenues increase?
  • Loyalty – will there be an increase in the fan base?"

Based on email response data, the personalized email saw a a 39 percent increase in unique click rate versus the static email.
Charlie Sung Shin, Director of CRM and Analytics, Major League Soccer

Phase 1: Data acquisition

MLS understood they needed to link the efforts in each phase to a tangible goal, which facilitated being able to track ROI and establishing strong key performance indicators (KPIs). The goal of this first phase is data collection, from internal as well as through third parties. Solid and effective data management practices are crucial here (data integration, data quality, data governance and master data management).

Phase 2: Customer engagement

Once MLS got their data under control, they began communication with the fan base in several ways that were personalized because on the fan profiles they were able to identify once they made their data more meaningful. Email newsletters was the primary tool for fan engagement in this phase. One newsletter focused on individual teams based on affinity and location data and the other was a static email that promoted the matches to be broadcast that week. Can you guess which performed better? The response rate of the personalized newsletter had a nearly 40 percent better click rate than the traditional, static email.

Phase 3: Monetization

Turning the email campaign success into revenue was the next task. Increasing ticket, merchandise and digital subscription sales was the focus for this phase. Based on fan profiles and transaction histories, MLS used predictive analytics to identify the best offer for customers based on their profiles.

Most organizations use predictive analytics marketing to get customer responses or increase purchases, as well as promote cross-sell opportunities. Predictive models helps attract, retain and grow the most profitable customers and maximize their spending.

Phase 4: Loyalty

Fans don’t show loyalty to leagues but rather to their favorite club. Most clubs build their own loyalty program and again predictive marketing plays a role in determining which incentives will work for which fans – incentives they can’t buy – such as meeting players and coaches, for example. 

And it not just professional soccer that is finding the financial rewards. The NBA’s Orlando Magic is using predictive analytics marketing to boost ticket sales and to improve customer retention.


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