SPORTS BUSINESS ANALYTICS & TICKETING CASE STUDIES FROM THE PROS

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SPORTS BUSINESS ANALYTICS & TICKETING

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Industry experts, research and statistics teams + database engineers and

strategy consultants

Seamless, accessible data and analytics through

existing CRM or Ticketmaster systems

RESOURCES

INTEGRATION

Ticketmaster’s unique live event transaction database + 3rd_{party data}

source

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GLOBAL MONTHLY UNIQUE ONLINE VISITORS EVENTS TICKETED SPORTS TICKETS PROCESSED ECOMMERCE SITE ON THE WEB GLOBAL CUSTOMER DATABASE RECORDS

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Note: From TM Data analysis ‘Summary of Distressed Inventory’

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IN SECONDARY TICKET SALES IN US PER YEAR OF SECONDARY SALES FOR SPORTING EVENTS

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Network analysis and visualization tools to identify broker ‘rings’ – allowing client to consolidate and more efficiently manage accounts,

offer more accurate incentives and combat scalping

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NFL case study

Using attendance, secondary prices, fan demographics to understand relative value of sections, price/value disparities, and scaling opportunities

Middle Lower End Zone Upper Front Upper Rear Club

1.Establish relative value of section using secondary resale price.

2.Estimate the primary market value (i.e., building algorithm to convert secondary resale price into primary market price).

3.Compare primary market price with estimated market value for pricing disparities.

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Evaluate Fan Database to determine acts/artists for which fans have greatest affinity to assist in booking and marketing decisions

NBA case study

Big Time Rush

Black Sabbath

Bob Seger

Brantley Gilbert

Bruce Springsteen and The E Street Band

Bruno Mars Dave Matthews Band

Depeche Mode Drake Elton John Jimmy Buffett Kings Of Leon Luke Bryan

Mumford & Sons Muse

Pearl Jam

Rihanna

Romeo Santos

Sigur Ros

The Black Keys

The Killers Tim McGraw

Tom Petty & The Heartbreakers Usher Victoria Justice 0 25 50 75 100 0% 25% 50% 75% 100% T ic k e t Sa le s Sc o re

Percentage of Customers with High Affinity to Artist

Spurs Season Plan Buyers

Season Plan Buyers Single Buyers All Venue Buyers

Other Venue Events Buyers

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SENIOR DIRECTOR BUSINESS STRATEGY AND

OPERATIONS

MARCH 2013

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► Need to Create Effective Tools for Analysis

► Determine what data is relevant ► Access data in usable format ► Platform to bring it all together

► Solution works within framework for all organizational

systems

► Large amounts of data across various sources

► Ticketmaster, CRM, Concessions, Fan Loyalty

► Lack of integration of many disparate systems

► Too many Excel spreadsheets

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► Comprehensive Ticket Sales Strategy

► Ticket Buyer Life Cycle

► Utilize Consumer Prospect Model ► Lead Management Strategy

► Meaning in Data

► Define raw data

► Focus on immediate and long term usefulness

► Utilize technology platforms to maximize efficiency

► Data Warehouse & Business Intelligence (BI) ► Existing CRM structure

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Ticketing Finance Corporate Partnership Marketing Exec Office Analytic Tools Gameday (retail)

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► Business

Development (BI) Cube & Excel

► Employee Dashboards

& Intranet

► External Tools for

Customization

► CRM & The Ticket

Sales Process

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► Integrate Data Warehouse & CRM

► Create Customer Life Cycle ► Focus on Meaning & Utility

► Integrate Additional Systems

► MLBAM; Loaded Tickets; Fan Loyalty

► Utilize Analysts

► Analysts hired to manage processes

► Integrate Live Analytics & CRM

► Utilize TM’s Live Analytics Prospect Model ► Demographics and Predictive Purchases ► Creation of Outbound Lead strategy

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Spend per Event on Ticketmaster.com

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► CRM remains primary consumer based tool

► Improved understanding of our market and

opportunities for growth

► Direct links between LiveAnalytics & Strategy

► Foundation for Ticket Sales Marketing Strategy

► Creation of a 12 month plan for outbound ticket sales

and lead management

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ANTHONY PEREZ

VICE PRESIDENT OF BUSINESS STRATEGY

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►

How we use Live Analytics tools

► Case Study: Single Game Yield Management

►

How we use Live Analytics data

► Case Study: Prospect Targeting

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How will the team’s personnel

changes impact ticket sales?

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► 22 Price levels (excluding premium)

► Manifest scaling using

regression / secondary market data

► 7 Variable pricing tiers

► Estimate demand using

regression / secondary market data

► Variably price season tickets

► Dynamic pricing utilized throughout season

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2010-11 2012-13 34 - 21 15 – 38 Season: W-L Record: Wtd Avg Tier: 5.3 5.4 -6.3%

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How can we better target

the right prospects

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30+ Acxiom demographic, psychographic, and lifestyle attribute variables were tested in the models:

25+ Host transactional behavior variables were tested in the models:

Each variable was examined for its strength of relationship with the outcome variable, as well as consistency and trending patterns.

Age Child Present in HH Income

Gender Working Woman in HH Discretionary Income Index Education PersonicX Group Distance to Venue

Marital Status Life style Interests Occupation Sports Interests

Major Category Purchases RFM Score Host Client Transaction History Transaction Purchase Timing Frequency/Monetary Grade Recency of Host Transaction Transaction Ticket Price/Type TM Live Event Segment Payment Method

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Overall Full Plan Partial Plan Single Game

Male 69% 81% 68% 54%

Age: 25 - 34 23% 16% 22% 32%

Age: 55 - 64 17% 23% 20% 9%

Age: 65+ 9% 14% 6% 4%

Education: Graduate School 21% 26% 21% 15%

Married 65% 68% 66% 62%

Working Women 42% 43% 41% 40%

Children Present 42% 35% 40% 52%

Discretionary Income Index 88 96 93 78

Household Income $93,640 $102,906 $94,444 $81,782