Big data opportunities for the Digitally Transforming Football Industry

Francisco Hernandez-Marcos
Francisco Hernandez-MarcosPartner at Digital-Praxis
Big data opportunities for
the Digitally Transforming
Football Industry
Big Data in Sports conference
Francisco Hernández-Marcos
May 25th, 2016
This document has been produced by 11 Goals & Associates. It is not complete unless
supported by the underlying detailed analyses and oral presentation.
About me SHAMELESS
SELF-PROMOTION
Education: Universidad Politécnica de Madrid, UNED,
London Business School, University of Chicago – Fundaciò
“laCaixa” & Fundación Rafael del Pino scholarships.
Firms worked for: Abengoa, McKinsey&Co, ABN AMRO,
Real Madrid C.F.
Entrepreneurship: Crisalia
Social Media & Internet consulting: 11goals.com
Lectures & Speaker in 4 continents: The Wall Street Journal, UP Madrid, London
Business School, Cornell, Politecnico Milano, CEIBS (Shanghai), Kungliga Tekniska
högskolan, The Business Factory, Fulbright Spain, ESCP Europe, UIMP, Harvard,
Moscow SU, IE, and several private companies.
Full profile: linkedin.com/in/franciscohm
@ Real Madrid
• Former Director of Online Strategy, depending directly from the
Club’s chairman.
• Designed integrated Digital Strategy, both in terms of attracting
traffic and monetising it.
• Designed and implemented Social Media model. Real Madrid
climbed from #3 to #1 team worldwide, in a season where FC
Barcelona won all possible titles, and Real Madrid experienced
the largest decrease of fans of any football team (source: Sport
Markt).
• Most active Facebook page worldwide in any category. A record
that has not been broken by any sports team yet.
• First international team to open presence in Chinese Social
Media.
• Several awards and recognitions, including that of the most
valuable Facebook page in terms of economic value for the club.
• Football and Digital advise to football clubs in 4 countries.
33
Views are our own. All information and
insights contained in this presentation are
either public or common knowledge
Disclaimer
Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry
55
Competitive dynamics in the Football Industry
• Monopolistic and regulatory power
• Highly corrupt
Leagues/
Federations
• Oligopolistic, based on revenues and ability to attract key players
and coaches
• Most clubs are systematically making loses, only top clubs make
profits, but not extraordinary compared to other “oligopolistic”
industries
• Heavy externalities (e.g. PR)
Clubs
• Extremely competitive environment based on quality of product,
but not price
• Top players having immense bargaining power
Players
66
Football clubs revenue ranking
Source: Deloitte Football Money League (2016); UEFA (2012)
577
561
520
481
474
464
436
420
392
324
281
258
220
199
187
180
169
165
165
161
Real Madrid
FC Barcelona
Manchester United
Paris Saint-Germain
Bayern Munich
Manchester City
Arsenal
Chelsea
Liverpool
Juventus
Borussia Dortmund
Tottenham Hotspur
Schalke 04
AC Milan
Atlético de Madrid
AS Roma
Newcastle United
Everton
Internazionale
West Ham United
Revenue (14/15)
EUR mill.
First
56%
Second
21%
Third
8%
Other
15%
Revenue matters. Securing a significant amount
of recurring revenue is very likely the most
important factor for succeeding in the pitch
Finishing position of highest-
spending club in players wages
(UEFA domestic leagues)
77
Some research shows that league position is strongly
correlated (R2=89%) with wage expenditure
Source: Footballnomics
BACK-UP
…but same research tells us that correlation with transfer spending is low (R2=16%)
Are wealthier clubs profiting
from the non-existence of
superior options for over-
performing players?
88
Revenue breakdown and key drivers
Revenue
Matchday
Broadcast
Commercial
Domestic
International
(Champions League,
Europa League)
Drivers
• Stadium ownership
• Stadium size
• Income per capita
• VIP facilities
• Dynamic pricing (when possible)
Actionable by the club
Drivers
• Lobbying & bargaining to the league
• Team performance
• League salesforce skills
Drivers
• Team performance
• League salesforce skills
Drivers
• Historical Team Performance
• Brand positioning
• Fan base
• Big Ticket contracts bargaining
• Long-tail contracts salesforce
• Loyalty card
• Summer tours
+
+
99
Cost breakdown and key drivers
Expenses
Team wages
Amortizations
G&A
Drivers
• Relative bargaining power with players/agents
‐ Hiring (eg. Revenue-sharing model)
‐ Renewing (e.g. wage steps)
Actionable by the club
Drivers
• Several small factors
Drivers
• Accumulated Net Investment
(more later)
• Legal rate of amortization
+
+
1010
Cost drivers
Source: own analysis based on Annual Statements (2013/14); UEFA (2012)
ILLUSTRATIVE
EXAMPLES
Ratios to revenue
44% 48%
74% 69% 69% 69% 61%
51%
65%
17% 12%
Real
Madrid
FC
Barcelona
Average
Turkey
Average
Italy
Average
England
Average
Russia
Average
Spain
Average
Germany
Average
UEFA
Other
Amortization
Wages
•Team wages account for the most part of an average club costs
•There are significant differences in cost management among clubs (e.g. wage steps)
•57% of UEFA member clubs are loss-making
•Cost is the main driver of a Club’s profitability. Most Clubs are loss-making because
they are not enough diligent on the cost base
•UEFA is concern about these issues and is implementing “Financial Fair Game” policies
0%-8%-11% 2% -8%
Net profit to revenue ratio
EBITDA:
164 M.€
EBITDA:
134 M.€
-9%-22%
1111Source: SportYou
EUR mill. - Estimation
Net (after tax) player’s wages (2015/16)
17
11
10
8
6
6
5
4,5
3,8
3
2,8
2,5
2,4
2,4
2
2
2
1,2
1,2
1,2
1,2
1
Cristiano
Bale
Ramos
Benzema
James
Kroos
Marcelo
Modric
Pepe
Casemiro
Arbeloa
Danilo
Kovacic
Varane
Carvajal
Isco
Keylor
Jesé
Nacho
K. Casilla
Chersyshey
L. Vazquez
21,2
10
10
7,5
6,5
6
6
5,8
5,5
4
4
3,5
3,5
3
3
2,5
2,5
2
2
1,5
1,5
1
1
1
Messi
Neymar
Suárez
Iniesta
Rakitic
Busquets
Alves
Piqué
Mascherano
Jordi Alba
Arda Turan
Claudio Bravo
Vermaelen
Ter Stegen
Mathieu
Adriano
Aleix Vidal
Rafinha
Barta
Sergi Roberto
Douglas
Masip
Munir
Sandro
Total: EUR 96,2 mill
Average: EUR 4,4 mill
Wage to Turnover ratio: 41%
Net profit: EUR 42 mill.
Real Madrid CF FC Barcelona
Total: EUR 114,5 mill
Average: EUR 4,8 mill
Wage to Turnover ratio: 47%
(73% with amortizations)
Net profit: EUR 15 mill.
1212
Source:
Revenue-sharing model in the football industry
Revenue-
sharing model
A case of
sustainable
advantage based
on asymmetric
information?
1313
Net Investment breakdown and key drivers
Net Investment
Arrivals
expenditure
Departures
income
Actionable by the club
-
Drivers
• Relative bargaining power with players/agents
• Revenue Sharing Model
• Flexible , performance driven, terms
Drivers
• Relative bargaining power with players/agents
• Alternative departure models (lease, free, etc)
1414
Source: Transfer Markt; own analysis
Note: Some transfers data are estimations
Player transfers of selected clubs
-200
-100
0
100
200
300
Ronaldo;
Kaká;
Alonso;
Benzema
Departures income (GBP mill.)
Arrivals expenditure (GBP mill.)
Net income (GBP mill.)
Real Madrid CF
05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14Season
473
998
-525
Total 05/06 to 13/14
Ramos;
Robinho
Diarra;
Gago
Robben;
Pepe;
Sneijder
Huntelar
DiMaría;
Özil;
Khedira
Coentrão
Modric
Bale;
Isco James;
Kroos
14/15
-20
-10
0
10
20
30
40
Athletic Bilbao
84
49
35
Del Horno
Aduritz
Martínez Herrera
1515
Football: The rules of the Business
1) Football has social value, and business value too
2) The better players the team has (measured by market wage), the better the team does on the
pitch
3) Revenue drives long-term team’s performance
4) Commercial the most important source of revenue: larger and growing faster
5) Matchday revenue drivers: Stadium ownership, VIP
6) Broadcast revenue drivers: domestic league value and team distribution, and success on
international tournaments
7) Commercial: Big-ticket contracts (dependant on TV audience) are key. Stadium naming rights will
bring significant growth soon
8) Main cost driver is team wages. It determines (un)profitability
More info: Football: 10 rules of the Business
FURTHER
READING
Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry
1717
Football industry is heavily intermediated, specially when
trying to reach a global audience
Source: Xxxxx
•TV channels
•Other media
•Retailers
•Small-deal
agents
•Games
•Other
1818
Strategic shift in the football industry: Digital as enabler
of a global, fan-centric organization
Content and
Brand provider
DT
Leading
relationship
with fans
(customers)
E.g.: Nespresso, Apple, Ferrari USA, Tesla Motors, Zara
Customer-centric
organizations always
create value
1919
Customer Value Vs Brand value
Source: Harvard Business Review; Markables
2020
Threat of media content piracy: Use Social Tech when it is
strategic for you to be closer to your end-customers
Low
•The sports industry is about to be seriously threatened by Internet piracy.
•Getting closer to the end-user would help clubs to gain insights and knowledge of the
customer, and to react and change value propositions to fight piracy.
•Also social technologies can create value-added, harder to be copied, services to bundle
with the base product.
Live Sport
Events
HighRequired Broadband
Non-Live
Live
Nature of
content
News
MusicBooks
Movies,
TV Series
2121
Why Digital Transformation in Football?
Global revenue
opportunities
Threat
of piracy
Digital
Transformation
Strategic
problem
2222
Strategic Digital Transformation in Soccer
More info: Strategic Digital Transformation in Soccer
FURTHER
READING
Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry
2424
Big Data opportunities for the Revenue stream
Valuation of
contracts
Revenue
Matchday
Broadcast
Commercial
Domestic
International
(Champions League,
Europa League)
Drivers
• Stadium ownership
• Stadium size
• Income per capita
• VIP facilities
• Dynamic pricing (when possible)
Drivers
• Lobbying & bargaining to the league
• Team performance
• League salesforce skills
Drivers
• Team performance
• League salesforce skills
Drivers
• Historical Team Performance
• Brand positioning
• Fan base
• Big Ticket contracts bargaining
• Long-tail contracts salesforce
• Loyalty card
• Summer tours
+
+
Dynamic
pricing
Brand Management
(own&sponsors)
Where &
when to Tour
Team
Performance
1-to-1
communications
TV Freemium
model
2525
Big Data opportunities for the Expenses stream
Expenses
Team wages
Amortizations
G&A
Drivers
• Relative bargaining power with players/agents
‐ Hiring (eg. Revenue-sharing model)
‐ Renewing (e.g. wage steps)
Drivers
• Several small factors
Drivers
• Accumulated Net Investment
• Legal rate of amortization
+
+
Player valuation
(sport+commercial)
2626
Big Data opportunities for the Net Investment
stream
Net Investment
Arrivals
expenditure
Departures
income
-
Drivers
• Relative bargaining power with players/agents
• Revenue Sharing Model
• Flexible , performance driven, terms
Drivers
• Relative bargaining power with players/agents
• Alternative departure models (lease, free, etc)
Player valuation
(sport+commercial)
Agenda
 Football as a business (summary)
 Digital Transformation in Football (summary)
 Big Data opportunities in the Football industry
•Strategic consulting services in technology and digital marketing for top executives
•We advise companies on digital transformation
Francisco Hernández
•MBA London Business School.
•IEP University of Chicago.
•11 years of digital experience.
•Ex Director Online Strategy Real
Madrid C.F.
•Other companies: ABN Amro,
Abengoa, McKinsey&Company.
•Professor at ESCP Europe.
•Lecturer in Europe, Latam and
Asia
•PWC: 10 e-Business talents in
Spain.
Sonia Fernández
•MBA Stanford.
•15 years of digital experience.
•Ex CEO Vindico Europe.
•Ex CEO Match.com Spain.
•Ex CEO MercadoLibre Spain.
•Other companies: Fon, Grupo
Prisa, 3i, Lehman Brothers.
•Professor at OBS-UB, EOI and MIB
•Lecturer at universities and in-
company training
•Author of two books on
networking and social networks
published in 2004 and 2001 franciscohm
francisco.hernandez@11goals.com | (+34) 605 58 66 55
soniafernandez
sonia.fernandez@11goals.com | (+34) 619 721 781
Thanks very much for your
attention and interaction
Francisco Hernández
francisco_hernandez@11goals.com
(+34) 605 58 66 55
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Big data opportunities for the Digitally Transforming Football Industry

  • 1. Big data opportunities for the Digitally Transforming Football Industry Big Data in Sports conference Francisco Hernández-Marcos May 25th, 2016 This document has been produced by 11 Goals & Associates. It is not complete unless supported by the underlying detailed analyses and oral presentation.
  • 2. About me SHAMELESS SELF-PROMOTION Education: Universidad Politécnica de Madrid, UNED, London Business School, University of Chicago – Fundaciò “laCaixa” & Fundación Rafael del Pino scholarships. Firms worked for: Abengoa, McKinsey&Co, ABN AMRO, Real Madrid C.F. Entrepreneurship: Crisalia Social Media & Internet consulting: 11goals.com Lectures & Speaker in 4 continents: The Wall Street Journal, UP Madrid, London Business School, Cornell, Politecnico Milano, CEIBS (Shanghai), Kungliga Tekniska högskolan, The Business Factory, Fulbright Spain, ESCP Europe, UIMP, Harvard, Moscow SU, IE, and several private companies. Full profile: linkedin.com/in/franciscohm
  • 3. @ Real Madrid • Former Director of Online Strategy, depending directly from the Club’s chairman. • Designed integrated Digital Strategy, both in terms of attracting traffic and monetising it. • Designed and implemented Social Media model. Real Madrid climbed from #3 to #1 team worldwide, in a season where FC Barcelona won all possible titles, and Real Madrid experienced the largest decrease of fans of any football team (source: Sport Markt). • Most active Facebook page worldwide in any category. A record that has not been broken by any sports team yet. • First international team to open presence in Chinese Social Media. • Several awards and recognitions, including that of the most valuable Facebook page in terms of economic value for the club. • Football and Digital advise to football clubs in 4 countries.
  • 4. 33 Views are our own. All information and insights contained in this presentation are either public or common knowledge Disclaimer
  • 5. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  • 6. 55 Competitive dynamics in the Football Industry • Monopolistic and regulatory power • Highly corrupt Leagues/ Federations • Oligopolistic, based on revenues and ability to attract key players and coaches • Most clubs are systematically making loses, only top clubs make profits, but not extraordinary compared to other “oligopolistic” industries • Heavy externalities (e.g. PR) Clubs • Extremely competitive environment based on quality of product, but not price • Top players having immense bargaining power Players
  • 7. 66 Football clubs revenue ranking Source: Deloitte Football Money League (2016); UEFA (2012) 577 561 520 481 474 464 436 420 392 324 281 258 220 199 187 180 169 165 165 161 Real Madrid FC Barcelona Manchester United Paris Saint-Germain Bayern Munich Manchester City Arsenal Chelsea Liverpool Juventus Borussia Dortmund Tottenham Hotspur Schalke 04 AC Milan Atlético de Madrid AS Roma Newcastle United Everton Internazionale West Ham United Revenue (14/15) EUR mill. First 56% Second 21% Third 8% Other 15% Revenue matters. Securing a significant amount of recurring revenue is very likely the most important factor for succeeding in the pitch Finishing position of highest- spending club in players wages (UEFA domestic leagues)
  • 8. 77 Some research shows that league position is strongly correlated (R2=89%) with wage expenditure Source: Footballnomics BACK-UP …but same research tells us that correlation with transfer spending is low (R2=16%) Are wealthier clubs profiting from the non-existence of superior options for over- performing players?
  • 9. 88 Revenue breakdown and key drivers Revenue Matchday Broadcast Commercial Domestic International (Champions League, Europa League) Drivers • Stadium ownership • Stadium size • Income per capita • VIP facilities • Dynamic pricing (when possible) Actionable by the club Drivers • Lobbying & bargaining to the league • Team performance • League salesforce skills Drivers • Team performance • League salesforce skills Drivers • Historical Team Performance • Brand positioning • Fan base • Big Ticket contracts bargaining • Long-tail contracts salesforce • Loyalty card • Summer tours + +
  • 10. 99 Cost breakdown and key drivers Expenses Team wages Amortizations G&A Drivers • Relative bargaining power with players/agents ‐ Hiring (eg. Revenue-sharing model) ‐ Renewing (e.g. wage steps) Actionable by the club Drivers • Several small factors Drivers • Accumulated Net Investment (more later) • Legal rate of amortization + +
  • 11. 1010 Cost drivers Source: own analysis based on Annual Statements (2013/14); UEFA (2012) ILLUSTRATIVE EXAMPLES Ratios to revenue 44% 48% 74% 69% 69% 69% 61% 51% 65% 17% 12% Real Madrid FC Barcelona Average Turkey Average Italy Average England Average Russia Average Spain Average Germany Average UEFA Other Amortization Wages •Team wages account for the most part of an average club costs •There are significant differences in cost management among clubs (e.g. wage steps) •57% of UEFA member clubs are loss-making •Cost is the main driver of a Club’s profitability. Most Clubs are loss-making because they are not enough diligent on the cost base •UEFA is concern about these issues and is implementing “Financial Fair Game” policies 0%-8%-11% 2% -8% Net profit to revenue ratio EBITDA: 164 M.€ EBITDA: 134 M.€ -9%-22%
  • 12. 1111Source: SportYou EUR mill. - Estimation Net (after tax) player’s wages (2015/16) 17 11 10 8 6 6 5 4,5 3,8 3 2,8 2,5 2,4 2,4 2 2 2 1,2 1,2 1,2 1,2 1 Cristiano Bale Ramos Benzema James Kroos Marcelo Modric Pepe Casemiro Arbeloa Danilo Kovacic Varane Carvajal Isco Keylor Jesé Nacho K. Casilla Chersyshey L. Vazquez 21,2 10 10 7,5 6,5 6 6 5,8 5,5 4 4 3,5 3,5 3 3 2,5 2,5 2 2 1,5 1,5 1 1 1 Messi Neymar Suárez Iniesta Rakitic Busquets Alves Piqué Mascherano Jordi Alba Arda Turan Claudio Bravo Vermaelen Ter Stegen Mathieu Adriano Aleix Vidal Rafinha Barta Sergi Roberto Douglas Masip Munir Sandro Total: EUR 96,2 mill Average: EUR 4,4 mill Wage to Turnover ratio: 41% Net profit: EUR 42 mill. Real Madrid CF FC Barcelona Total: EUR 114,5 mill Average: EUR 4,8 mill Wage to Turnover ratio: 47% (73% with amortizations) Net profit: EUR 15 mill.
  • 13. 1212 Source: Revenue-sharing model in the football industry Revenue- sharing model A case of sustainable advantage based on asymmetric information?
  • 14. 1313 Net Investment breakdown and key drivers Net Investment Arrivals expenditure Departures income Actionable by the club - Drivers • Relative bargaining power with players/agents • Revenue Sharing Model • Flexible , performance driven, terms Drivers • Relative bargaining power with players/agents • Alternative departure models (lease, free, etc)
  • 15. 1414 Source: Transfer Markt; own analysis Note: Some transfers data are estimations Player transfers of selected clubs -200 -100 0 100 200 300 Ronaldo; Kaká; Alonso; Benzema Departures income (GBP mill.) Arrivals expenditure (GBP mill.) Net income (GBP mill.) Real Madrid CF 05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14Season 473 998 -525 Total 05/06 to 13/14 Ramos; Robinho Diarra; Gago Robben; Pepe; Sneijder Huntelar DiMaría; Özil; Khedira Coentrão Modric Bale; Isco James; Kroos 14/15 -20 -10 0 10 20 30 40 Athletic Bilbao 84 49 35 Del Horno Aduritz Martínez Herrera
  • 16. 1515 Football: The rules of the Business 1) Football has social value, and business value too 2) The better players the team has (measured by market wage), the better the team does on the pitch 3) Revenue drives long-term team’s performance 4) Commercial the most important source of revenue: larger and growing faster 5) Matchday revenue drivers: Stadium ownership, VIP 6) Broadcast revenue drivers: domestic league value and team distribution, and success on international tournaments 7) Commercial: Big-ticket contracts (dependant on TV audience) are key. Stadium naming rights will bring significant growth soon 8) Main cost driver is team wages. It determines (un)profitability More info: Football: 10 rules of the Business FURTHER READING
  • 17. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  • 18. 1717 Football industry is heavily intermediated, specially when trying to reach a global audience Source: Xxxxx •TV channels •Other media •Retailers •Small-deal agents •Games •Other
  • 19. 1818 Strategic shift in the football industry: Digital as enabler of a global, fan-centric organization Content and Brand provider DT Leading relationship with fans (customers) E.g.: Nespresso, Apple, Ferrari USA, Tesla Motors, Zara Customer-centric organizations always create value
  • 20. 1919 Customer Value Vs Brand value Source: Harvard Business Review; Markables
  • 21. 2020 Threat of media content piracy: Use Social Tech when it is strategic for you to be closer to your end-customers Low •The sports industry is about to be seriously threatened by Internet piracy. •Getting closer to the end-user would help clubs to gain insights and knowledge of the customer, and to react and change value propositions to fight piracy. •Also social technologies can create value-added, harder to be copied, services to bundle with the base product. Live Sport Events HighRequired Broadband Non-Live Live Nature of content News MusicBooks Movies, TV Series
  • 22. 2121 Why Digital Transformation in Football? Global revenue opportunities Threat of piracy Digital Transformation Strategic problem
  • 23. 2222 Strategic Digital Transformation in Soccer More info: Strategic Digital Transformation in Soccer FURTHER READING
  • 24. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  • 25. 2424 Big Data opportunities for the Revenue stream Valuation of contracts Revenue Matchday Broadcast Commercial Domestic International (Champions League, Europa League) Drivers • Stadium ownership • Stadium size • Income per capita • VIP facilities • Dynamic pricing (when possible) Drivers • Lobbying & bargaining to the league • Team performance • League salesforce skills Drivers • Team performance • League salesforce skills Drivers • Historical Team Performance • Brand positioning • Fan base • Big Ticket contracts bargaining • Long-tail contracts salesforce • Loyalty card • Summer tours + + Dynamic pricing Brand Management (own&sponsors) Where & when to Tour Team Performance 1-to-1 communications TV Freemium model
  • 26. 2525 Big Data opportunities for the Expenses stream Expenses Team wages Amortizations G&A Drivers • Relative bargaining power with players/agents ‐ Hiring (eg. Revenue-sharing model) ‐ Renewing (e.g. wage steps) Drivers • Several small factors Drivers • Accumulated Net Investment • Legal rate of amortization + + Player valuation (sport+commercial)
  • 27. 2626 Big Data opportunities for the Net Investment stream Net Investment Arrivals expenditure Departures income - Drivers • Relative bargaining power with players/agents • Revenue Sharing Model • Flexible , performance driven, terms Drivers • Relative bargaining power with players/agents • Alternative departure models (lease, free, etc) Player valuation (sport+commercial)
  • 28. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  • 29. •Strategic consulting services in technology and digital marketing for top executives •We advise companies on digital transformation Francisco Hernández •MBA London Business School. •IEP University of Chicago. •11 years of digital experience. •Ex Director Online Strategy Real Madrid C.F. •Other companies: ABN Amro, Abengoa, McKinsey&Company. •Professor at ESCP Europe. •Lecturer in Europe, Latam and Asia •PWC: 10 e-Business talents in Spain. Sonia Fernández •MBA Stanford. •15 years of digital experience. •Ex CEO Vindico Europe. •Ex CEO Match.com Spain. •Ex CEO MercadoLibre Spain. •Other companies: Fon, Grupo Prisa, 3i, Lehman Brothers. •Professor at OBS-UB, EOI and MIB •Lecturer at universities and in- company training •Author of two books on networking and social networks published in 2004 and 2001 franciscohm francisco.hernandez@11goals.com | (+34) 605 58 66 55 soniafernandez sonia.fernandez@11goals.com | (+34) 619 721 781
  • 30. Thanks very much for your attention and interaction Francisco Hernández francisco_hernandez@11goals.com (+34) 605 58 66 55