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Platforms, big data and marketing Christophe Benavent Université Paris Nanterre

Data ; Algorithmes et marketing

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Page 1: Data ; Algorithmes et marketing

Platforms, big data and marketing

Christophe BenaventUniversité Paris Nanterre

Page 2: Data ; Algorithmes et marketing

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.

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Challenges

● Consumer (brand) to customer (category)● Data integration → DMP ● Marketing automation → customer (multi

scales) journeys● Actions more than insights :

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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

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So simple ! the data scientist workshop

(twitter content topic analysis)

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Hedonometering with social content

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At the beginning a cookie

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Apps interaction

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The rise of Data Management Platforms

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Ranking : far over satisfaction measurement

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The performative biases

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Pay How You Drivebehavioral monitoring with IoT ?

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Iot : a dream of general feed back

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AB testing – the criticized Facebook experiment

Done on a ~= 700 000 indswithout asking for consent.

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Flickr : labelling with deep learning for searchable (and monetization) pics

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Meta data and derived data

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Surge Pricing : smart pricing

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For food

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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.