Data ; Algorithmes et marketing

Post on 21-Jan-2018

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Transcript of Data ; Algorithmes et marketing

Platforms, big data and marketing

Christophe BenaventUniversité Paris Nanterre

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.

Challenges

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

scales) journeys● Actions more than insights :

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

So simple ! the data scientist workshop

(twitter content topic analysis)

Hedonometering with social content

At the beginning a cookie

Apps interaction

The rise of Data Management Platforms

Ranking : far over satisfaction measurement

The performative biases

Pay How You Drivebehavioral monitoring with IoT ?

Iot : a dream of general feed back

AB testing – the criticized Facebook experiment

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

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

Meta data and derived data

Surge Pricing : smart pricing

For food

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.