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Data ; Algorithmes et marketing

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comprendre le rôle des données et des algorithme dans le marketing de plateformes.

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Data ; Algorithmes et marketing

  1. 1. Platforms, big data and marketing Christophe Benavent Université Paris Nanterre
  2. 2. foreword ● A deep dive in the platform world ● A threefold lesson : – Competitive advantage come from capability to coordinate a very large number of people coming from different market sides – Datas and algorithms are the core tools – It's not a question of knowledge, it's about how to drive behaviors to benefit more from market externalities.
  3. 3. Challenges ● Consumer (brand) to customer (category) ● Data integration → DMP ● Marketing automation → customer (multi scales) journeys ● Actions more than insights :
  4. 4. Data is a (multi) process and one thousand of them could make noise Acquisition Preparation Modeling Diffusion ● CRM and accounts ● Social networks ● Tracking (web, retargetting...) ● Buttons ● Beacons ● Apps (ie shopping list, loyalty apps) ● IoT ( balance, fridge, fitbit) ● Domestic assistant ● Cars and computer ● API ● ... ● Matching/fusion ● Quality control ● Big data - nosql ● Numerical ● Text ● Pictures ● Signals ● Surveys ● Scoring ● Dashboards ● Ranking ● Electronic labels ● ... ● Traditionnal marketing survey (CA,MDS, cluster...) ● Avanced econometrics ● Network analysis ● First generation of ML ● Deeplearning A need for data architecture
  5. 5. So simple ! the data scientist workshop (twitter content topic analysis)
  6. 6. Hedonometering with social content
  7. 7. At the beginning a cookie
  8. 8. Apps interaction
  9. 9. The rise of Data Management Platforms
  10. 10. Ranking : far over satisfaction measurement
  11. 11. The performative biases
  12. 12. Pay How You Drive behavioral monitoring with IoT ?
  13. 13. Iot : a dream of general feed back
  14. 14. AB testing – the criticized Facebook experiment Done on a ~= 700 000 inds without asking for consent.
  15. 15. Flickr : labelling with deep learning for searchable (and monetization) pics
  16. 16. Meta data and derived data
  17. 17. Surge Pricing : smart pricing
  18. 18. For food
  19. 19. Perspectives ● Retailers are cornerstone of data strategy. ● How to be embedded in the data plaforms network ? ● RGDP : data privacy, portability, how much data are personal ? How to be “data loyal”. ● Health, fitness, hedonism and food ethics : different brand/segment models.

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