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SPONSORED BY TERADATA

How the NY Giants use Teradata

to personalize fan experiences

Russell Scibetti

AIM106- S

(he/him)

VP of Strategy & BI

New York Giants

Hillary Ashton

(she/her)

Chief Product Officer

Teradata

Agenda

CX of the future

A Giants journey

Giants 2.0

Infrastructure

Fan data sources

Results

How we use the data

Q&A

Key trends impacting CX

Data layer

complexity

MarTech stacks are

evolving rapidly to deliver

on customer expectations,

resulting in data silos. To

combat the increasing

complexity and volume of

data from disparate

sources, marketing teams

are investing in customer

data platforms to create a

unified view of

customers.

Hyper-

personalization

With the explosion of data

and digital experiences,

consumers expect hyper-

personalization across

every touchpoint of their

journeys. Enabling these

hyper-personalized

experiences requires deep

individual customer

insights delivered in a

timely way.

Customer journeys

Consumers expect a

consistent and seamless

experience whenever and

wherever they interact

with brands. This is

driving enterprises and

marketing teams to shift

from optimizing

individual touchpoints to

managing end-to-end

journeys.

Evolving privacy

landscape

The pressure to comply

with complex privacy and

security regulation is

increasing as marketing

capabilities struggle to

keep up with intensifying

customer demands and

pro-privacy initiatives

from browser providers.

Most common challenges to creating a great CX

• Data silos, data volume, complexity, latency, and a lack

of granularity prevent unified customer views

• Scattered and immature analytics capabilities prevent a

deep understanding of individuals

• Inability to deliver one-to-one contextual

personalization in real-time leads

• Lack of integration in the marketing ecosystem creates

inconsistency and prevents activation of insights

• Best-of-breed point solutions have proliferated data

silos, with an average 30 MarTech platforms deployed

Organizations try to

solve CX problems by

adding more products

to the MarTech stack

Bringing tomorrow's customer experience

to you today

Traditional

• Product focus

• Process digitalization

• Segment of many

• Batch

• Multichannel

• Data silos

Best in class

• Customer focus

• Journey optimization

• Segment of one

• Events

• Omnichannel

• Data-driven

Future

• Individual focus

• Effortless experience

• Hyper-personalized

• Contextual

• Virtualization

• Always on

View of the Fan

• Ticket Sales[object File]

• Attendance and Resale

• Point of Sale

• E-Commerce

• Web

• Email

• SMS

• Surveys

• League Data

• Demographics

ECOMMERCE

A giant journey

Phase 1

Working "outside in"

Focus on operational

platforms: CRM, marketing

automation, sales enablement

Gain traction with customer-

facing departments

Phase 1:

Operational

platforms &

processes

COVID

pause

Phase 2:

Data

integration &

consolidation

Phase 3:

Long-term

scalability &

insights

Phase 2

Internal infrastructure

Integration of multiple data feeds

into consolidated warehouse

Move on-premises resources

into the cloud

Phase 3

Long-term growth (current)

Focus on identity,

speed, insights

Position the department

for future needs

Cross-functional data application

“Democratization” of data: access to insights across the organization

Fan engagement Sponsorship valuation Revenue forecasting

Giants data architecture

Users

Visual

Studio

On-premises DMZ

Kore Software – US West 2

Amazon S3

(Managed Application)

– US West 2

Teradata VantageCloud

T H E C O M P L E T E C L O U D A N A L Y T I C S A N D D A T A P L A T F O R M

Unlock data. Activate analytics. Accelerate value.

VantageCloud Lake includes the same industry-

leading analytic capabilities as Teradata Vantage

on a new next-generation, cloud-native architecture

• ClearScape Analytics

• Teradata Analytics Database

• QueryGrid

• Object Store access

VantageCloud Lake brings the power of Vantage

to more diverse use cases throughout your

organization so you can drive innovation at scale

Teradata

VantageCloud Lake

Compute clusters

• Stateless query

processing to support

ad hoc, exploratory, or

departmental needs

• Multiple clusters

with access to a

single data store

• Policy driven auto-scale

elasticity by cluster

Console

• Web-based, self-service

console for system and

user administration

• Web-based query editor

• Query and billing

monitoring

Object storage

• Massively scalable

low-cost storage

• Teradata-managed lake

file system is fully ACID-

compliant and optimized

for performance, including

caching, optimized spool,

indexed metadata

• Customer-managed

object storage enables

shared access as well as

read/write for Parquet

formats and read for

JSON and CSV

Software fabric

• Specialized service that

passes both data and

instructions throughout

VantageCloud Lake

• Orchestrated ecosystem

integration for pushdown

query processing to other

endpoints, including

the VantageCloud

Enterprise Edition

Primary cluster

• Manages query

planning and the

distribution of work

across the environment

• Manages the high-

performance enterprise

block storage for select

operational queries

• Manages the

data dictionary

Primary cluster

Object storage

Console

Compute clusters

AI/ML BI/Reporting ELT

Finance Marketing

QUERY FABRIC

Highly optimized in-database

analytic functions

Geospatial/Temporal

Hypothesis testing

Machine learning

Multivariate statistics

Descriptive statistics

Data cleansing/

transformation

Feature engineering

Pathing analytics

Digital signal processing

Time-series forecasting

Leverage languages

and tools of choice

Languages

Use preferred languages

R, SQL, Python, SAS

Partner integrations

Seamlessly integrate to

the ML/AI ecosystem

Bring your

own analytics

Open analytics framework

containers and model

sharing

Operationalized at scale

to drive transformative results

Deploy models at scale

Easily operationalize all models,

even externally developed ones

Integrated ModelOps

Comprehensive lifecycle management

and governance

ClearScape Analytics

*Not all logos are represented

Access

REST, SQL, SAS,

PYTHON, R, Java

Orchestration

QueryGrid, NOS

Replication

TPT, DSA, SFTP,

Native APIs

Capabilities gap & Teradata fit to initiatives

Phase 3 required more than just integration

Expanded analytics with lean staff

Models that can refresh in near real time

Ability to leverage existing KORE investment

Collaboration with Teradata identified multiple use cases

Cluster modeling

Fan journey and time-series analysis

Next best product recommendation

Enhanced revenue/pricing optimization

Giants cluster modeling

Challenge

Personalize

engagement

to different fan

audience

Background

League-provided

cluster models may

not align to team-

specific segments

Approach

Leverage Fan Golden

Records and CDP attributes

to build 1 or more NYG-

specific cluster models

• Model 1: Focus on STM

Season Ticket Members

• Deeper volume of behavioral data

• Model 2: Overall fans

• Less reliance on ticket-specific data

Giants Season Ticket Member clusters

Inputs

Primary ticketing, secondary ticketing,

attendance, age, HHI, Fanatics, email, web

Results

• Premium experience – smallest group but highest spend, also active on secondary market,

highest Fanatics spend, highest HHI, top email activity but less web-form engagement

• Traditional die-hards – middle tier of spend varied across locations, highest age, 2nd highest

income, moderate digital activity

• On the rise – lowest lifetime spend, lowest age/income combination, most digital web

engagement, but less active on email

Giants non-season ticket member clusters

Inputs

Single-game ticketing, attendance, age, HHI, gender, Fanatics, email,

sweepstakes, NFL fan data

Results

• Disengaged – minimal history, no ticket spend, min. shop spend, even-gender split

• Digital-first – minimal ticket spend, most email activity, 2nd highest shop spend, youngest

• Shopping-centric – top shop spend, lighter digital engagement, slightly more male

• Dabblers – some ticket/Fanatics spend, but very low, skews younger and more female

• Annual outings – mid-range single-game spend, light Fanatics, mid-tier age/income

• Top of the market – high secondary market spenders, mid-range Fanatics, very male, older

Giants purchase/attendance behaviors

Challenge

What game(s) are

fans most likely to

purchase/attend

next?

Background

Ticket purchases

(primary and

secondary) and

attendance are

anchor points in

the fan journey

Approach

Time-series analysis

focused on

• Ratio of sales per day

leading up

to each game

• Most likely game to

attend next

Giants purchase/attendance behaviors

Identify

patterns

in game

selections

8,459 different purchase/

attendance game

combinations in 2022

Use combinations to

personalize messaging

for additional game sales

Don't always focus on "next

game" – home opener, multi-

game attendees almost ~6x

more likely to skip next game

for one further out

Time Period Demand Curves

Challenge

Better understand price

elasticity by game and

location for optimal plan

and single game pricing

decisions

Approach

• Group historical games based on the shape of Willingness to Pay (WTP) demand curves

• Create different curves for different time periods (e.g. May to August vs. Full Season)

• Apply K-Means clustering using intercept, slope, average price, etc. as predictors to categorize

games based on volume and price, with price elasticity values per cluster

Additional work in progress

Pricing optimization

Deep dive into secondary market resale

margins by game, location, timing, and

team performance

Expansion of fan journey

Integrate digital, ecommerce, and staff

interactions into analysis of fan journey and

what drives purchase behaviors

Digital

engagement

Ecomm Parking

Attendance/

Ingress

Transit

Post-event

Concessions/

Retail

Activations

Egress

Common thread: Fan experience

Who:

Identity and clusters

What:

Price & product mix

When:

Fan journey

Where:

Physical vs. Digital

Why:

Cluster attributes

How:

Personalized messaging

How Teradata enhances existing

AWS products and services

Looking for additional

capabilities around

analytics models

What can be coded easily

and run in real time against

data pulled from Amazon Redshift

Teradata is the right

complementary platform,

compatible with existing setup,

adds needed functionality

Focus on model building,

execution, and connectivity back

and forth with KORE/AWS

Elevating fan engagement through Teradata

Designed for today

and the future;

environment can

grow to any scale

Maximizes revenue

by optimally

monetizing every

step of the customer/

fan experience

Empowers data-

driven insights for

all aspects of

business operations

AI/ML capabilities

are fully integrated

in platform for future

state opportunities

Q&A

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