SPORTS BUSINESS ANALYTICS & TICKETING CASE STUDIES FROM THE PROS

Transcription

1 SPORTS BUSINESS ANALYTICS & TICKETING CASE STUDIES FROM THE PROS MARCH 2013

2 CONSUMER DATA Ticketmaster s unique live event transaction database + 3 rd party data source RESOURCES Industry experts, research and statistics teams + database engineers and strategy consultants INTEGRATION Seamless, accessible data and analytics through existing CRM or Ticketmaster systems 2

3 GLOBAL MONTHLY UNIQUE ONLINE VISITORS EVENTS TICKETED ECOMMERCE SITE ON THE WEB SPORTS TICKETS PROCESSED GLOBAL CUSTOMER DATABASE RECORDS 3

4 SPORTS TICKETS GO UNSOLD UNCAPTURED REVENUE Note: From TM Data analysis Summary of Distressed Inventory 4

5 IN SECONDARY TICKET SALES IN US PER YEAR OF SECONDARY SALES FOR SPORTING EVENTS Source: Forrester Research 5

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7 MLB case study 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 7

8 NFL case study Using attendance, secondary prices, fan demographics to understand relative value of sections, price/value disparities, and scaling opportunities 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. Club Middle Lower End Zone Upper Front Upper Rear 8

9 Ticket Sales Score NBA case study Evaluate Fan Database to determine acts/artists for which fans have greatest affinity to assist in booking and marketing decisions Spurs Season Plan Buyers Drake Dave Matthews Band Muse Pearl Jam Bruce Springsteen and The E Street Band Kings Of Leon Depeche Mode Jimmy Buffett Rihanna Tim McGraw Bob Seger 50 Victoria Justice The Black Keys Big Time Rush Usher Elton John 25 Mumford & Sons Romeo Santos Luke Bryan Tom Petty & The Heartbreakers Brantley Gilbert Sigur Ros Bruno Mars Black Sabbath The Killers Band 0 0% 25% 50% 75% 100% Percentage of Customers with High Affinity to Artist Season Plan Buyers Single Buyers All Venue Buyers Other Venue Events Buyers 9

10 KENNY FARRELL SENIOR DIRECTOR BUSINESS STRATEGY AND OPERATIONS MARCH 2013

11 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 Slow cross-departmental collaboration 11

12 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 12

13 Exec Office Corporate Partnership Analytic Tools Finance Gameday (retail) Ticketing Marketing 13

14 Business Development (BI) Cube & Excel Employee Dashboards & Intranet External Tools for Customization CRM & The Ticket Sales Process 14

15 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 15

16 Spend per Event on Ticketmaster.com 16

17 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 17

18 ANTHONY PEREZ VICE PRESIDENT OF BUSINESS STRATEGY MARCH 2013

19 How we use Live Analytics tools Case Study: Single Game Yield Management How we use Live Analytics data Case Study: Prospect Targeting 19

20 How will the team s personnel changes impact ticket sales? 20

21 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 21

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23 -6.3% Season: W-L Record: Wtd Avg Tier:

24 -3.3% Season: W-L Record: Wtd Avg Tier:

25 How can we better target the right prospects with the right products? 25

26 30+ Acxiom demographic, psychographic, and lifestyle attribute variables were tested in the models: 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 25+ Host transactional behavior variables were tested in the models: 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 Each variable was examined for its strength of relationship with the outcome variable, as well as consistency and trending patterns Ticketmaster LLC 26

27 Overall Full Plan Partial Plan Single Game Male 69% 81% 68% 54% Age: % 16% 22% 32% Age: % 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 Household Income $93,640 $102,906 $94,444 $81,782 Income: $125K+ 20% 25% 20% 14% PersonicX Cluster: Established Elite 6% 9% 4% 3% PersonicX Cluster: Corporate Clout 5% 6% 9% 3% PersonicX Cluster: Jumbo Families 6% 5% 5% 8% PersonicX Group: Mature Wealth 11% 15% 13% 6% PersonicX Group: Golden Years 11% 14% 12% 6% AMEX 30% 34% 31% 22% Purchase Timing: Presale 26% 32% 21% 16% RFM Score RFM: % 20% 19% 8% Ticketmaster LLC

28 Full / Partial Seasons Club Seats Upgrades Family Day Packages

29 KENNY FARRELL JOHN FORESE ANTHONY PEREZ thank you! 29