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Big data opportunities for the Digitally Transforming Football Industry

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Big data opportunities for the Digitally Transforming Football Industry

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Big data opportunities for the Digitally Transforming Football Industry

  1. 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. 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. 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. 4. 33 Views are our own. All information and insights contained in this presentation are either public or common knowledge Disclaimer
  5. 5. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  6. 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. 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. 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. 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. 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. 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. 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. 13. 1212 Source: Revenue-sharing model in the football industry Revenue- sharing model A case of sustainable advantage based on asymmetric information?
  14. 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. 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. 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. 17. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  18. 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. 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. 20. 1919 Customer Value Vs Brand value Source: Harvard Business Review; Markables
  21. 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. 22. 2121 Why Digital Transformation in Football? Global revenue opportunities Threat of piracy Digital Transformation Strategic problem
  23. 23. 2222 Strategic Digital Transformation in Soccer More info: Strategic Digital Transformation in Soccer FURTHER READING
  24. 24. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  25. 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. 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. 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. 28. Agenda  Football as a business (summary)  Digital Transformation in Football (summary)  Big Data opportunities in the Football industry
  29. 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. 30. Thanks very much for your attention and interaction Francisco Hernández francisco_hernandez@11goals.com (+34) 605 58 66 55

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