Teradata VantageCloud Lake creates a winning fan experience
- 8 League championships
- 10M Social media followers
- Millions Of fans worldwide
Established in 1925, the New York Football Giants are a storied franchise in the world’s largest market. The Giants have won eight championships and, after twice winning two titles in five years (1986, 1990, 2007, and 2011), are the only franchise with championship victories in four consecutive decades.
The New York Giants, with a recognized global and multigenerational fan base, use cloud analytics to attract and increase fan engagement to deepen relationships and improve loyalty.
“It's incredible that we're going to have our 100th season in 2024. Not a lot of franchises get to celebrate a milestone like that, and the most important thing is to celebrate it with our fans. We have fans who have been passing down their season tickets from generation to generation. Whether you're a new fan or you've been with us for a long time, everything we do with the fan revolves around data, from ticketing to concessions to retail. All the information helps us understand, engage, and give them the experience they care about,” says Russell Scibetti, vice president of strategy and business intelligence at the New York Football Giants.
Analytics identify and engage Giants fans
Social media and changing content delivery platforms create immense opportunities to better engage and understand nuances of fans. Fan engagement no longer exclusively occurs at games, training camp, and fan days. The New York Giants are composed of a set of micro-businesses to increase fan engagement. Whether that occurs through its more than 10 million social media followers, leveraging the Giants App, Giants.com, email marketing, TV, radio, podcasts, and more, the New York Giants have insights into fans that go above and beyond.
Taking inspiration from the League’s approach to cluster modeling and segmentation, the Giants were looking to tap into behavioral, transactional, and demographic data on their fans to identify opportunities to uniquely message and market to the Giants fanbase.
“When the League is looking at cluster modeling, they might look at season ticket buyers as one main cluster—whereas for us, ticketing is such an important part of the business that we decided to break that audience into much greater detail,” Scibetti continues.
The New York Giants use ClearScape Analytics™ in Teradata VantageCloud Lake on Amazon Web Services (AWS) to identify, target, and engage fans. In partnering with Teradata, three fan experience use cases were identified:
- Cluster modeling of Giants fans
- Purchase-attendance behaviors
- Dynamic ticket pricing (price elasticity)
Machine learning K-means cluster models help identify and understand fans
Personalizing fan engagement across channels requires a deep understanding of the various behaviors and traits of specific fans. No two fans are the same, but patterns can emerge to enhance League-provided cluster models with new team-specific segments.
“The first use case we tackled with Teradata was around building cluster models. We’ve seen the league do this successfully to understand different types of NFL fans, and we saw a similar opportunity to understand Giants fans,” says Scibetti.
Using ClearScape Analytics, data scientists and business analysts leverage teradataml for Python-based unsupervised machine learning (ML) K-means cluster models. Separate models group fans into multiple clusters across both season ticket members and non-season ticket members.
Season ticket members are grouped into three clusters based on primary ticketing, secondary ticketing, attendance, merchandise spend, email, demographics and digital behavior:
- Premium experience
- Traditional die-hards
- On the rise
Non-season ticket members are grouped into six clusters based on ticket and merchandise spend, attendance, age, email, sweepstakes, and NFL fan data:
- Top of the market
- Annual outings
- Dabblers
- Shopping-centric
- Digital-first
- Casual engagement
“Seeing the different types of behaviors, transactions, and engagements inside the clusters allows us to see how fan audiences vary. We might have one audience that's more fashion and merchandise-centric, where they skew much more toward buying merchandise on a regular basis, versus someone who buys a ticket once or twice every year. Seeing these different patterns, how someone is in one cluster, and what cluster they might grow into as they continue to engage with the team is really important information for us to understand our fans,” explains Scibetti.
ClearScape Analytics provides powerful, open, and connected ML capabilities. Using the in-database analytics provide flexibility for data scientists and analysts to use their preferred tools, speeding up time to insights and activating value.
“ClearScape Analytics has been great for us in terms of executing on our model building because of the flexible nature. We have the built-in capabilities with ClearScape Analytics. My background happens to be more in R, whereas my primary analyst’s background is more in Python. We can bring those models into VantageCloud Lake, test, and deploy really fast,” Scibetti elaborates.
Identifying games for which fans are most likely to purchase tickets
Having identified clusters for season and non-season ticket members, the New York Giants address ticket purchase behaviors on the primary and secondary ticketing market. With only 10 games played at home per regular season, personalizing messages based on fan clusters can help to drive additional ticket sales and attendance.
ClearScape Analytics’ time-series and nPath analytics identify patterns and paths of various purchase and attendance combinations.
“Understanding the fan journey of purchase to attendance means understanding the different patterns of when people buy tickets, which games they do and don’t attend, and whether they resell their ticket or buy from us,” describes Scibetti. “These help us target messaging, better predict attendance, and optimize pricing.”
Insights uncovered challenge common conceptions.
“Even for a sport with only 10 home games, we identified more than 8,000 different combinations of purchase to attendance. There really is no one-size-fits-all fan journey,” says Scibetti.
The 8,000+ combinations mean the Giants no longer focus solely on a common “next game” mentality.
“Using these analytic techniques, we learned that for [individual game buyers] who attended the home opener, they were six times more likely to skip the next game and go to a game further on in the schedule. This is really important because we can use that to shape our marketing,” Scibetti shares.
Dynamic ticket pricing optimizes single-game pricing
It’s not enough to treat a game as a single product. Each game creates new variables that impact future games. Market demand for tickets is dependent on factors such as when the game is held in the season (e.g., early or later in the season), team performance, competitive matchups (e.g., division opponents or rivalries), and day and time of games (e.g., Sunday afternoon, Sunday night, Monday night, and Thursday night).
“In sports, a common practice is variable pricing and dynamic pricing. Essentially, not every game is going to have the same demand. Understanding the different demand curves for our games is absolutely critical to have a good pricing strategy,” outlines Scibetti.
To address variable product (game) demand, demand curves are created in VantageCloud Lake. A demand curve groups historical games based on willingness to pay (WTP). Additional demand curves are created for different time periods—for example, May to August time periods versus full-season time periods.
“We make pricing changes throughout the season. When a team wins a couple of games, that can shift a demand curve pretty quickly. As the team’s doing better, demand goes up. However, that may only affect certain games. It’s not just about being able to cluster similar games together as part of the demand curves—it’s also identifying which games are most likely to shift from cluster to cluster as team performance changes,” details Scibetti.
VantageCloud Lake delivers harmonized data for trusted insights
VantageCloud Lake leverages a next-generation cloud-native architecture. This allows customers, like the New York Giants, to meet increasingly diverse analytics and data needs to execute all workload types at scale, including ML, decision support, data science, data engineering, transactional, and reporting.
As an existing AWS cloud customer, the New York Giants added VantageCloud Lake to take advantage of centralized data in shared object storage using AWS S3, while making it easy to build and deploy powerful analytics with ClearScape Analytics.
By integrating and harmonizing data from a variety of source systems, the New York Giants have trust in the data to make strategic business decisions that will better serve fans.
According to Scibetti, “You can't have trust in the data without connectivity and making sure your platforms can talk to your other systems. There are too many times when you have three different versions of the truth. So, making sure everything is operating from the same data is how you know you can trust your analysis.”
VantageCloud Lake unlocks all the benefits expected in a cloud solution, plus Teradata’s differentiated technology stack—including the industry-leading Analytics Database, ClearScape Analytics, and Teradata QueryGrid data fabric. This means that VantageCloud Lake easily connects into the New York Giants’ existing AWS footprint, allowing the Giants to expand analytics capabilities and focus on understanding fans, building and executing models, and doing it at scale.
“We have a very robust data feed from our different systems, from the League to our ticketing partners. There are no challenges when it comes to volume or speed. VantageCloud Lake layers right in, giving us the analytical resource we need to really take our game up a notch,” concludes Scibetti.
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