Learning to Bat .400

Sports teams are learning that big data can help them reach that magic number and hit .400. There are fantastic new ways that teams are learning to market and post numbers that have rarely been seen. No doubt you've heard about big data, and have a general understanding of what its all about. Simply put, Big Data is nothing more than a term identifying the large mass of digital information that is being collected about individuals. In this blog, I will be talking about business-to-consumer sales: the data that businesses collect about consumers. The mass of data collected about a consumer from all of their interactions with a business on-line, in person, and through customer rewards programs. 

Today’s blog will discuss a real-life example of how major league American sports teams are using big data in their business-to-consumer marketing (b2c marketing) initiatives. 

You are probably aware of how your grocery store uses a rewards program to collect data about you. When you sign up, they get your demographic information and then can tie all that with every transaction at the register (POS.) And that data collection grows bigger with every new trip to the store. As a result, the grocery can begin to offer promotions that are directly relevant to your interests. 

Professional sports franchises in America are now taking advantage of the potential marketing windfall that may come from collecting and using big data. For example, in the winter of 2013, the Pittsburgh Penguins, a national league hockey team began “PensPoints ®. PensPoints is a rewards program using a smartphone app.

According to their website:

PensPoints is a smartphone app that tracks and rewards fan activity. Earn Codes for  points each time you attend Penguins home games, make eligible purchases, listen to broadcasts and more!

Fans are driven to sign up for the app and can earn points via activities that can then be redeemed for Penguin’s merchandise. Users earn points for a food or drink purchase at a concession, as well as for merchandise, during home games. Other promotions award users who watch pre-and post-game shows on TV and then enter an on-screen code. These promotions drive viewership to regional sports networks, enabling higher ad buys. Scavenger hunts are also used that send fans around the venue looking for specific displays. The primary intent here is to expose fans to as large an array of on-site concessions and merchandise as possible. Essentially, these hunts increase foot traffic.

Related to this, Major League baseball in February 2014 began installation of  IBeacon technology from Apple. This is micro-location technology that will be able to ID fans that have Bluetooth as they enter the park. Teams may offer coupons, perhaps, but at least initially, IBeacon will serve to make the fan experience smoother. One value will be helping fans find their seats via the fastest route. 

Another area in sports where the existence of Big Data is changing the landscape is the introduction of dynamic pricing. Dynamic pricing has been used in the airline industry for decades. Essentially, prices for a product or service are dynamic, constantly changing as demand rises or falls over time in the short-term. Historically, performing arts and sporting events have maintained set prices for any specific seating area. Prime front row seats cost more; prices fall as the seat’s location becomes less optimal. This model doesn’t take into account factors that might be increasing demand at specific times. With that arrival of big data, sports teams have the potential to use all this data to identify when demand might increase and thus allow seat prices to rise. (It is a standard free market notion. As demand rises for a fixed supply of seats, there will be an increasing willingness by the consumer to pay more) Recognizing this, The San Francisco Giants were the first in Major League Baseball to introduce dynamic pricing. Data and new software devised pricing algorithms that allowed the Giants to factor in any number of demand altering situations. Weather, star pitcher, popular team rivalries as well as time and day of the week all combine to identify a demand pattern that could now be identified to create a fluctuating price schedule for each individual game, and seating section. 

How well this will be accepted by fans remain a major question, but this certainly is a growing technology that isn't going to go away by any means.

Visit Mindmatrix to learn more about how big data can be used to help your sales team bat .400