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Globalisation : the Platform Era
Christophe @Benavent
Professor at niversity Paris Ouest
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
1850 : Navier-stokes
WAVEWATCH III™ (Tolman
1997, 1999a, 2009)
Based on
of the model WAVEWATCH,
as developed at Delft
University of Technology
(Tolman 1989, 1991a)
A question of Coordination
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
A world of data
BIG DATA
OPEN DATA
SELF DATA
SMALL DATA
OBJECT DATA
API
DEVICE
TEXT DATA
WEB
Camera
GIT HUB
r
VISUAL DATA
TAL
Dashboard
4 Big Data Business Model
● The learning Model
● The Empowerment Model
● The Automation Model
● Data As Service Model
Modèle Compétence Enjeu Exemple
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)
Automation
Learning machine : ex Modèles MAB
Knowledge
How the world react to Tokyo Games 2024
Empowerment
Data as services
The Uber Case
A journey with Google Now
Relationship, interactions et privacy
Brand Relationship
(Trust and Commitment)
Touch Points
(Customer Experience
Management)
Sociale Prsence
Légitimacy
Satisfaction
Appropriation
Data
Personnalization
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
Usage and customer
Different
Channels for
Different
Usages
Different channels for
Different customers
?
Cross channel
Business integration
Multi channel
Inter competition
Unique channel
Specialization
Transchannel
Organic integration
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
Governementality
● Means
– Algorithm : search and recommendation engine,
notification and dashboard, recognition engine,..
● Policies : rules and enforcement
● Motivational devices :
– Gamification
– Reputation device
– Nudges
Governementality
● Michel Foucault : the Pastor figure :
– influence conduct to produce a desired collective
behavior
The Clubbing Issue
As Dating Platform: IRC, Meetic, AdopteunMec, Tinder,
Cam4
Every thing is a Screen
Feed back and nudges devices
● Recommendation system
● Feed back and notification
● Nudge
Algorithm is about Feed Back and
IoT
Nudging
● Default (pre-filled formulas)
● 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
Policies
● The behavioral rules, distribution of rights and
roles :
● Surveillance and discipline
Motivational devices
● Ranking systems and social recognition
● Perks and status
● Gamification
Digital labor
Politics of algorihm
Une seule 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
22$ of difference (average : 140$)
Privacy paradox
Benéfices
Influence
sociale
Self efficacy
Awareness
Collection
Privacy
concern
Perceived Risk
Control
Protection
The question of digital sovereignty
Digital education

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Platform big data 2015 sept

  • 1. Globalisation : the Platform Era 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
  • 3.
  • 4.
  • 5. 1850 : Navier-stokes WAVEWATCH III™ (Tolman 1997, 1999a, 2009) Based on of the model WAVEWATCH, as developed at Delft University of Technology (Tolman 1989, 1991a)
  • 6. A question of Coordination 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 of data BIG DATA OPEN DATA SELF DATA SMALL DATA OBJECT DATA API DEVICE TEXT DATA WEB Camera GIT HUB r VISUAL DATA TAL Dashboard
  • 8.
  • 9.
  • 10. 4 Big Data Business Model ● The learning Model ● The Empowerment Model ● The Automation Model ● Data As Service Model
  • 11. Modèle Compétence Enjeu Exemple 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)
  • 13. Learning machine : ex Modèles MAB
  • 15. How the world react to Tokyo Games 2024
  • 19. A journey with Google Now
  • 20.
  • 21. Relationship, interactions et privacy 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 Channels for 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 As Dating Platform: IRC, Meetic, AdopteunMec, Tinder, Cam4
  • 28. Every thing is a Screen
  • 29. Feed back and nudges devices ● Recommendation system ● Feed back and notification ● Nudge
  • 30.
  • 31.
  • 32.
  • 33.
  • 34. Algorithm is about Feed Back and IoT
  • 35. Nudging ● Default (pre-filled formulas) ● 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
  • 36.
  • 37. Policies ● The behavioral rules, distribution of rights and roles : ● Surveillance and discipline
  • 38. Motivational devices ● Ranking systems and social recognition ● Perks and status ● Gamification
  • 40.
  • 41. Politics of algorihm Une seule 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$)
  • 44. The question of digital sovereignty