As an avid baseball fan, and with my home team Cleveland Indians and the Chicago Cubs just completing the World Series, it seems like an appropriate time to discuss how analytics has helped to determine winners in baseball and the correlations to the world of banking. In baseball, as in banking, the competition for the consumer’s dollar has never been more fragmented or intense. With new competition and new technology, each organization is looking for an advantage over the legacy and new competitors.
Despite the fact that baseball is considered one of America’s oldest pastimes, the sport is a trailblazer in the use of data. Advancements in player evaluation, customer profiling, pricing models and customer engagement strategies provide interesting case studies that the banking industry could follow.
The business of baseball is becoming more and more dependent on analytics. Most teams front offices are using analytics and big data to progressively drive individual and team performance as well as revenue growth.
But analyzing the data is one thing, and actually using that data to inform and influence organizational decisions is another. In less than 15 years, the culture inside major league front offices has changed so profoundly that where once teams were mocked for using analytics, they’re now mocked for not using them.
The result, a dedication to analytics delivered the Red Sox their first championship in 86 years, and guided the Cubs to their first championship in 108 years. Similarly, the Cleveland Indians, with one of the smallest payrolls in baseball, used analytics to build a team and take the World Series to 7 games.
This year, 68 different points of data are being distributed from a revenue standpoint daily from the Major League office. Even the minor league teams are participating, using data to improve business results. In many cases, the only differentiation is in how this data is being applied.
Collecting Insights on Players
Statistics have always been a part of baseball, with fans and management interested in basic analysis such as batting averages, earned run percentages and strikeouts. But the capture and use of data has escalated with the introduction of new technologies that track the movements of the ball and players on the field. Television networks carrying the games use some of this data to enhance the viewing experience, while teams use the data to help them make tons of decisions, like how to align their defenses from batter to batter or even from pitch to pitch.
Baseball’s digital player evaluation technology (Sabermetrics), made famous by Michael Lewis’ book Moneyball: The Art of Winning an Unfair Game, allows team to know precisely how fast a base runner can run, how far a fielder can travel to grab a fly ball, how quickly a catcher can get a throw to second base, and the skills and tendencies of each batter and pitcher. In fact, camera tracking technology, in every Major League stadium, produces more data per game than ever before.
There are even statistics that seek to determine a players’ entire contribution in a single measurement, using acronyms such as WAR (additional wins contributed vs. an average replacement player) and VORP (value over an average replacement player). As with data and analytics in any industry, these evaluations are usually combined with human ‘gut instinct’ and not followed blindly.
This type of statistical “player evaluation” process continues to be tested in financial services. The linkage between peer and supervisor evaluation and business performance data has been a major part of most banking organization’s performance appraisal process for years.
While most evaluation efforts focus on financial or business performance, external and internal customer satisfaction is becoming a more significant part of employee performance measurement. In the case of the banking industry, studying the relations among these variables can provide insight into branch performance while also providing guidance on hiring of new employees.
There has been push-back, however, around using only statistical measures to evaluate employees at some organizations. Goldman Sachs, Accenture, General Electric and other firms are moving away from annual performance reviews in favor of more-frequent check-ins between managers and employees, saying that numeric analysis can grind down employee morale. It remains to be seen if ‘softer’ metrics achieve the same results as enhanced analytics.
Collecting Insights on Customers
Any time a fan purchases a ticket, they get an account built in a team’s CRM system, with additional data collected from the initial time of purchase onward. Teams usually house this customer and fan data in multiple, siloed systems. They collect data from systems like TicketMaster, Facebook, Stubhub and other 3rd-party services as well as on their own ticket site. Most teams are beginning to aggregate this disparate data into single platforms.
Similar to the banking industry, teams hope to create a 360-degree view of the customer to create experiences that will increase satisfaction, future purchases and return visits. Similar to what is done with players to predict performance, there is a goal to predict a customer’s performance as well. Evaluation of the potential lifetime value, the likelihood to buy season tickets, purchases within the ball park, etc. are used to optimize sales and marketing resources.
Digital technology is being used to collect information about fan behavior, such as what season ticket holders are buying once they’re in the park. Social media software, meanwhile, tracks what’s being said about the game experience and who the major influencers are. Many teams offer social media influencers encouragement for their online dialogue through rewards.
As in banking, understanding the demographics of the customers and the trends year-to-year allows teams to adjust marketing strategies and messaging accordingly. From special promotions (weekend fireworks) to special family and Millennial seating areas within ballparks, data analytics is the foundation for decision making. The data also provides sales reps insight to know where to focus their efforts and adjust their sales messages.
Collecting Insights for Optimum Pricing
One of the first entertainment businesses to implement dynamic pricing, baseball realized that this strategy could increase revenues while supporting season ticket loyalty. If a ticket is $20 when it first goes on sale, but because of demand it goes to $30, $40 by the time that game date arrives, season ticket holders realize the value of their advanced purchase.
Dynamic pricing not just drives revenue for single games (maximizing revenue when demand is high), it also allows teams to have a great piece of leverage when they’re trying to sell tickets since season ticket holders are saving more money than before dynamic pricing.
The lesson in banking is that pricing of services and products can be based on analytics and overall customer value delivered. It is also an excellent validator of the strategy to charge more for immediate access to funds on a remote deposit transaction than for 3 day clearing.
Creating Personalized Experiences
Much like in banking, baseball teams recognize the value and importance of engagement. While teams are pretty confident who fans are supporting at the games, they are less sure of the overall experience during the game. As a result, more and more teams are dedicating resources to improving the experience at all stages of the customer lifecycle.
The fan engagement strategy goes beyond engagement within the ball park to encouraging engagement on multiple media channels (broadcast TV, cable, digital channels, social media and even rebroadcasts). This requires an investment in creating personalized fan experiences through marketing automation, data-driven analytics and omnichannel delivery, allowing marketing and communication executives to better understand each loyal fan.
As data analytics is applied, innovative digital technology such as virtual reality, beacon devices and gamification applications are being tested to improve fan engagement and experiences, at stadiums, at home and through mobile devices. In addition, teams are tracking purchase behavior through digital ticketing systems and making adjustments based on the analytics.
By tracking a fan’s digital ticket or fan card (which holds ticket information and money used for park purchases), teams can create ‘surprise and delight’ points of differentiation for the most loyal fans. These fans may be able to skip long concession lines and/or collect points to use toward future purchases.
This data can also be used for more targeted marketing. For instance, a fan who typically buys a hot dog and soda in the third or fourth inning can receive a mobile message during other parts of the game encouraging them to try something different. This is similar to contextual marketing being tested at several financial institutions.
While social media and digital marketing assist in the fan engagement process, baseball understands that fan engagement involves every touch point along the fan experience journey. This journey includes every moment from initial ticket purchase, to arriving and entering the ball park, to the in-game experience. It also goes beyond the game experience to include other interactions during and after the regular season.
The collection of data about human behavior and the analysis and application of this data is big business in baseball and in banking. The banking industry should feel some solace, however, to know that a 150-year-old game played on grass and dirt is achieving significant results using data on the field and off.