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Platform big data 2015 sept
Globalisation: The Platform Era 1) The internet has stratified over time from databases in the 1990s, the encyclopedic web in 1995, e-commerce in 2000, social media in 2005, apps and mobile in 2010, and big data/IoT currently. 2) Platforms govern through algorithms that influence user behavior via recommendation engines, notifications, and dashboards to encourage desired collective behaviors. 3) Platforms employ various techniques of "governmentality" including policies, marketing nudges, reputation systems, and gamification to manage networks with externalities, long tails, multisided markets, and crowdsourcing.
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Platform big data 2015 sept
- 1. Globalisation : the PlatformEra Christophe @Benavent Professor at niversity Paris Ouest
- 2. The internet Stratification ●1990 : Databased Marketing ● 19 95 : The encyclopedic Web ● 2000 : e-commerce blossom ● 2005 : Social Media turn ● 2010 : Apps and Mobile ● 2015 : Big data era and IOT
- 5. 1850 : Navier-stokes WAVEWATCHIII™ (Tolman 1997, 1999a, 2009) Based on of the model WAVEWATCH, as developed at Delft University of Technology (Tolman 1989, 1991a)
- 6. A question ofCoordination Locus principal de la décision individu Composant Collectif décentralisé Marché : contrôle par les prix résultats et incitations monétaire convient aux environnement dynamique et homogène Cluster Clan : contrôle par la culture et l'initiation nécessité par l'absence de critère de performance. Environnement héterogène et stable. Fédératif Classements Réseaux collège Centralisé Plateforme : contrôle par l'information et haut degré d'auto-contrôle par des mécanismes de réputation. Environnement dynamique et hétérogène. Compétence Bureaucratie : contrôle par la règle et les systèmes de délégation qui convient à un environnement stable et homogène. Niveau de coordination
- 7. A world ofdata BIG DATA OPEN DATA SELF DATA SMALL DATA OBJECT DATA API DEVICE TEXT DATA WEB Camera GIT HUB r VISUAL DATA TAL Dashboard
- 10. 4 Big DataBusiness Model ● The learning Model ● The Empowerment Model ● The Automation Model ● Data As Service Model
- 11. Modèle Compétence EnjeuExemple Automation Machine learning et real time Infrastructure Pricing Plateform Empowerment Dash board et visualisation Sense-making / Motivation Burberry's /La Poste Data-based Services Nudges et Choice architecture Policies Google Now Knowledge Data-mining et GIS None Cartographie (fine)
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- 13. Learning machine : exModèles MAB
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- 15. How the worldreact to Tokyo Games 2024
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- 19. A journey withGoogle Now
- 21. Relationship, interactions etprivacy Brand Relationship (Trust and Commitment) Touch Points (Customer Experience Management) Sociale Prsence Légitimacy Satisfaction Appropriation Data Personnalization
- 22. Need Recognition Information search Evaluation Decision Purchase Post Purchase reaction Consumer Experience Relationship :Commitment, Trust and self efficacy Environment : Cultural and material condition 48% search on mobile several time aday 82% Use their phone is the street 40% have made some consumer reviews 45% use comparaison engine
- 23. Usage and customer Different Channelsfor Different Usages Different channels for Different customers ? Cross channel Business integration Multi channel Inter competition Unique channel Specialization Transchannel Organic integration
- 24. Logic of Platforms ●Network Externalities ● Long Tail ● Multiside Markets ● Matching Markets ● Crowdsourcing ● Reputation systems => How to manage ? ● Influencing behaviors to favour a global response : gouvermentality ● Marketing : Nudges and Policies
- 25. Governementality ● Means – Algorithm :search and recommendation engine, notification and dashboard, recognition engine,.. ● Policies : rules and enforcement ● Motivational devices : – Gamification – Reputation device – Nudges
- 26. Governementality ● Michel Foucault :the Pastor figure : – influence conduct to produce a desired collective behavior
- 27. The Clubbing Issue AsDating Platform: IRC, Meetic, AdopteunMec, Tinder, Cam4
- 28. Every thing isa Screen
- 29. Feed back andnudges devices ● Recommendation system ● Feed back and notification ● Nudge
- 34. Algorithm is aboutFeed Back and IoT
- 35. Nudging ● Default (pre-filledformulas) ● Simplification ● Use of social norms ● Increase in ease and convenience ● Disclosure ( component of a price) ● Warning and graphics ● Precommitment strategies ● Reminder ● Elicit intention ● Feed back
- 37. Policies ● The behavioralrules, distribution of rights and roles : ● Surveillance and discipline
- 38. Motivational devices ● Rankingsystems and social recognition ● Perks and status ● Gamification
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- 41. Politics of algorihm Uneseule fois Occasionellement Plutôt fréquemment Très souvent -1,5 -1,0 -,5 ,0 ,5 1,0 1,5 ,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 50,0 Motivation par fréquence de publication Proportion Egoisme Vengeance vs encouragement Espérance de Réciprocité Alternative à une autre solution A l'issue d'un de vos voyage avez-vous publié un commentaire en ligne? degrédemotivation Proportiondel'échantillon
- 42. 22$ of difference(average : 140$)
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- 44. The question ofdigital sovereignty
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