Want more Insights from SAS? Subscribe to our Insights newsletter. Or check back often to get more insights on the topics you care about, including analytics, big data, data management, marketing, and risk & fraud.
What Major League 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 to 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 the 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 ever 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. The organization developed a four-phase approach that relies on predictive analytics. The 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 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. You can also download our white paper Marketing Automation Drives Sports Fan Engagement to get more ideas on how this approach might work for you.
What to track
MLS uses predictive models to enhance each phase of its customer engagement strategy:
- Data acquisition – Will our databases continue to grow? Are we adding new data sources?
- Engagement – Will our marketing efforts improve 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 39% 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 it needed to link the efforts in each phase to a tangible goal, which facilitated being able to track ROI and established strong key performance indicators (KPIs). The goal of this first phase is data collection, from internal as well as third-party sources. 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 the data under control, communications with the fan base became more personalized, based on fan profiles that were more detailed thanks to the data improvements made. Email newsletters were the primary tools for fan engagement in this phase. One newsletter focused on individual teams based on affinity and location data; the other was a static email that promoted the matches to be broadcast that week. Can you guess which performed better? The response rate for the personalized newsletter had a nearly 40 percent better click rate than the traditional, static email.
Phase 3: Monetization
Turning the email campaign's 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.
Most organizations use predictive analytics marketing to get boost 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. 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.
And it isn't just professional soccer that is reaping the financial rewards. The NBA’s Orlando Magic uses predictive analytics marketing to boost ticket sales and improve customer retention.