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How does Big Data disrupt marketing: the modification of

a marketer’s job

Nicolas Suchaud

Director: Anouk Mukherjee

Today, the improvement of organizations and the information systems in them is not a matter of making more

information available, but of conserving scarce human attention so that it can focus on the information that is

most important and most relevant to the decisions that have to be made.

Herbert A.Simon

University Paris Dauphine

Master 2 - Business Consulting & IT

Year 2013-2014

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Acknowledgments

I thank Mr François-Xavier de Vaujany and Mr Anthony Hussenot, co-directors of the Master's degree in

Business Consulting & IT for their help and dedication throughout the year. They have provided us with the

right theoretical knowledge for our apprenticeship and highly participate into developing my culture and

interests to management science.

I thank Mr Anouk Mukherjee, who, as a thesis supervisor, effectively helped me and guided me with many

useful advices to lead my work.

I thank Mr Ariel Aubry, Consulting Practice Manager and Mr Chen-Do Lu, Head of Microsoft Business

Innovation center and Marketing Manager, for being supportive and providing me with my first apprenticeship

at Microsoft. I thank Ms Morgane Regnier, my manager and apprenticeship supervisor at Microsoft. Her

professionalism and availability have been really appreciated and have contributed a lot to the success of my

apprenticeship. I thank, Sebastien Imbert, Chief Marketing Officer and Damien Cudel Product Marketing

Manager of Big Data products at Microsoft for their precious time they offered to contribute to my thesis.

I thank all my Microsoft colleagues I worked with during my apprenticeship: their advices and methods helped

me build a path during my thesis construction.

I thank all professionals who gave me their time and shared their passion during interviews. My colleagues

from Microsoft, professionals from advertising and media Mr Galisse, Mr Baron and Mr Pere. The employees

from companies who presented me their projects, Mr Hoang from Orange, Mr Lalanne from SNCF. And all

the startups that I’ve met and challenged with passionate discussion, especially on the American market.

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Executive summary

Companies have just started to understand the new approach of Big Data and its application on

strategies. New competitors have grown on the market and they keep on offering new tools and solutions.

Work of marketers has switched to a lot of intents and a lot of expectations for business growth. This thesis

compiles the different fundamental approaches of the digital marketing strategies as well as the marketing

analytics. It will review the data driven marketing changes which have been accelerated by the Big Data trends.

The methodology and results conducted in this study will provide us with instructions on the influencing factors

and the determinism on the marketer job.

Deployed on a sample of marketing departments with the participation of key strategic roles in company, it

lends a better understanding of the new sponsor role of The Chief Data officer across firms’ departments. Some

of the outcomes are frictions initiated by the emergence of the new business scenarios that departments should

quickly deploy. For instance, we will detail how the intuition marketing is directly challenged by the data

driven process and why the two approaches are in conflict. The marketers seem to center their efforts on the

customer centric approach, while trying to integrate the new job titles like “Data Scientist” to their side every

day. The consequences are new paradigms around this topic, such as the notion of long term industrialisation

in projects that meets the needs of quick “test & learn” activation.

This study will give to the reader a better understanding and characterization of projects and key initiatives

when launching this type of project. It will specify key steps to grow businesses quickly. An analysis of the

complex imperceptible links with technical partners will follow. It will review how the different partners

structure feed themselves and improve the scenarios monetisation between themself thanks to the data

exchange. The new external stakeholder involved in this new typology of project will also be analyzed. Indeed,

they now gather important quantities of customer information worldwide, and it could create new challenges

by leading the value chain of relationship marketing. Through this thesis, we will explain the disruption

mechanism of the classical view of marketer job and the impacts on their daily work.

Keywords: Big Data, Marketing strategies, CRM, Data-Driven Marketing, Predictive, Data Science, Data

Driven Marketing, Buying pattern, Data Mining.

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Detailed Table of contents 1 Introduction ........................................................................................................................................................................ 7

2 Theoretical study ................................................................................................................................................................ 9

2.1 Theoretical framework: understanding marketing transformation and Big Data rise ............................................... 9

2.1.1 History and emergence of Big Data terminology – Technology and business case proximity ............................... 9

2.1.2 Influence on marketing jobs – Marketing work evolution ................................................................................... 12

2.2 Marketing Theoretical Framework ........................................................................................................................... 20

2.2.1 Traditional marketing model and marketing department ................................................................................... 20

2.2.2 The paradigm for marketing employees .............................................................................................................. 26

2.1 The relationship between marketing and Big Data technology: .............................................................................. 31

2.1.1 Classic critics of Marketing ................................................................................................................................... 31

2.1.2 Improve efficiency marketing business and Impact on CRM ............................................................................... 34

2.1.3 The cristims causality ........................................................................................................................................... 36

2.2 Marketing and link with Big Data what it makes the advantages ............................................................................ 37

2.2.1 Difference of language and vocabulary ............................................................................................................... 38

2.2.2 Difference of expectations the classic divergence perspective of IT and Marketing ........................................... 41

2.3 Synthesis of the mobilized theories ......................................................................................................................... 42

3 Study field methodology: qualitative study and research field ........................................................................................ 43

3.1 Presentation of the quantitative methodology:....................................................................................................... 44

3.1.1 Hypothesis ............................................................................................................................................................ 45

3.1.2 Content methodology analyzed – Procedure for data collection: Extraction, and interpretation ...................... 49

3.1.3 Procedure for data analysis:................................................................................................................................. 49

3.2 Data collection protocol: .......................................................................................................................................... 50

3.2.1 Construction of the interview guide for the study field ....................................................................................... 53

3.3 Presentation of the field study: ................................................................................................................................ 55

3.3.1 Context of the study: Key stakeholders, historic ................................................................................................. 55

3.3.2 Challenges and key event: .................................................................................................................................... 57

4 Qualitative study: Results ................................................................................................................................................. 58

4.1 Characterization of projects and initiatives clarifications: ....................................................................................... 59

4.2 Complex transversal projects with agile partner structure ...................................................................................... 61

4.3 Contextual evolutions of marketer skills: ................................................................................................................. 64

4.4 The expectations of Data Driven Marketing ............................................................................................................ 66

4.5 Towards the marketing strategy Test & learn .......................................................................................................... 68

4.6 Data science perspective integration: ...................................................................................................................... 70

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5 Discussions ........................................................................................................................................................................ 71

5.1 Personal contribution to the topic: .......................................................................................................................... 71

5.2 Management implication results: ............................................................................................................................ 72

5.3 Limits of my research: .............................................................................................................................................. 73

6 Conclusion ......................................................................................................................................................................... 74

7 Bibliography ...................................................................................................................................................................... 76

8 Appendix ........................................................................................................................................................................... 79

9 Interviews.......................................................................................................................................................................... 83

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1 Introduction

Every two days, we create as much information as we did from total civilization until 2003. With the

arrival of new technologies and facilities services provided by IT department and tech companies, the last few

years have accelerated trends and transformation, which we begin to introduce and discuss about in all

departments in each company. Especially in the marketing department, we constantly hear about topics such

as social media analytics, CRM, mobile marketing, digital strategy… These subjects have now matured and

among of all these trending words has emerged a very hot topic.

Where and how will we go with data and behavior we have collected? Which strategy should we adopt

with it? Here are the questions coming out of this new battlefield.

According to IDC, we will store up to 35 zetabytes (i.e. 35 trillion terabytes) of data globally by 2020, a 44-

fold increase since 2009! Most Analysts agree that up to 85% of new data capture is unstructured, which means

that the content aggregated for company are not directly useful and understood by IT department company.

What is underline here is that there is a new opportunity to create and understand value with this data.

Meanwhile the velocity of data captured is also growing rapidly: Social media networks like Facebook loads

over 10 TB of data every day, while the Twitter community generates over 1 TB of tweets per day. In addition,

customers keep on wanting information in real-time and basing their decisions on always more personalized

experiences. During the past few years marketing departments, have developed and increased the Business-

insights and data collected. Meanwhile, a majority of marketers still rely too much on intuition1, a recent study

shows that nearly 800 marketers at Fortune 1000 companies found the vast majority of marketers still rely too

much on intuition. The actors of marketing in a company are aware of the opportunity, according to a recent

survey conduct by market research institute GfK2, “86% of marketers consider that Big Data will change the

function of marketing, and a further 62% say that it has already fundamentally changed their role.”

In this central problematic has emerged the challenge of traditional marketer in the digital era and call for a

new approach. This topic is accelerated by the interest and the opportunity that many marketing departments

and divisions have detected. Companies have just started to understand new approach and to apply Big Data

strategies. The market has seen new competitor’s offering new tools and solutions like in consulting and web

1 Study of nearly 800 marketers at fortune 1000 [ http://blogs.hbr.org/2012/08/marketers-flunk-the-big-data-test/ ] 2 GfK for the Guardian, Big data – a marketer's dream or dilemma? [ http://www.theguardian.com/media/2013/oct/07/big-data-

marketing-dream-or-dilemma ]

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services (Social Media, E-Business and web marketing). The work of marketers has switched to a lot of intent

and a lot of expectation for business growth.

This study will focus on the marketer job on which the impact of using data-driven approach and new

methodology could be significant. My thesis problematic will explore “how does Big Data disrupt Marketing:

the modification of a marketer’s job”. Indeed, classical business models and structures are forced to be more

agile in business to preserve their advantages against new competitors and to reduce the cost in industry and

services. Throughout this thesis, I will provide with an overview to better understand and qualify the Big Data

usage marketing approach on each actors. How they are link in their strategies and the different aspect of a Big

Data marketing strategy. During my literature study I will focus on the emergence of the Big Data strategy in

marketing divisions with the explosion of new technology use. I will then detail what is behind this

terminology, and what are the links with Data Science, Data Mining… After the introduction and definition of

key concepts and structure example strategies, I will analyze profiles and how new challenges created by new

communications technologies could introduce new opportunities to use Big Data for marketing teams.

In the second part of my thesis, a qualitative study conduct by interviews field will define the reality of the

market. On one hand, I will explore the main advantages and differences learned by coding interviews and

relevant points during interviews with professionals in the sector. On the other hand, I will select different

profiles with a comparative analysis of their point of view.

Though Big Data is an opportunity for companies to re-define the role of decisional and classical logic link

model with marketing, a new challenge has risen to respond more efficiently to business and clients needs.

This thesis will analyze how companies experiment Big Data projects. We will focus on how it transformed

marketing and digital strategies decisions. To achieve this I will define the recent changes on the marketer

work, and how new concepts are integrated.

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2 Theoretical study

2.1 Theoretical framework: understanding marketing transformation and Big

Data rise

2.1.1 History and emergence of Big Data terminology – Technology and business

case proximity

One of the first publication using this term was in July 2000 from Francis Diebold of the University of

Pennsylvania. For the first time, the term Big Data was related to modelling information. He named it “Big

Data” phenomenon, and already described it like an opportunity to access to “quality relevant data3”. Besides,

Big Data has greatly gained in popularity, since 2009 Big Data started to show up as a marketing term in many

press releases and stories. At the same time, the technology Hadoop emerged. This technology has accelerated

the business products growth of companies like Facebook, Yahoo or Twitter, from a technologic perspective,

it was a new framework for storage and large-scale processing of data-sets. In 2010 the trends emerged mainly

thanks to IBM and Oracle holding their biggest Information Management conferences and start to present Big

Data as a product asset.

However Big Data is poorly defined by a part of the community of scientists who worked on this topic. Some

of them saw it as an opportunity and just a fad (Abiteboul 2012). Moreover, it simply exists no single unified

definition. One of the most common used definition on the field is the one of Gartner’s. They define Big Data

with the regular 3 V, Volume, Velocity and Variety. (Gartner 2012). In this definition two aspects are

noteworthy. The Big Data is no longer considered as a capacity of storage, first, they introduce Variety of

different data types, unstructured/structured for example. Secondly, the Velocity qualifies the speed at which

data are created, collected and analyzed. An additional dimension is added by the company IBM to address the

uncertainty of the data: Veracity (Schroeck et al., 2012). Veracity refers to the question of the reliability of

ascertain data type. And the last V stands for value, the value has been introduced to qualify pertinent and

useful scenario utilization of Big Data, for example, business scenario for customer sales, enhancing the 360º

View of Customers.

Most often, Big Data is defined by volume of data, in the reference, “Big Data: The next frontier for innovation,

competition, and productivity” a white paper about the business opportunity written by McKinsey , the scientist

who leads research on global economic and technology trends describe more the opportunity has a capacity

3 "Big Data" Dynamic Factor Models for Macroeconomic Measurement and Forecasting [

http://www.ssc.upenn.edu/~fdiebold/papers/paper40/temp-wc.PDF ]

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(Manyika 2011). There is a growing awareness across companies that Big Data addresses more than just

volume of data (Schroeck 2012). Although, each IT editor has developed his own definition4, for example

Oracle contends that Big Data is the derivation of relational database driven business decision making.

What is Big Data - Volume ,Variety,Velocity,Value and Veracity5

Oracle has long been a leader in information management and analytics for structured, mostly enterprise

transaction data, but its introduction of the Oracle Big Data solutions is demonstrating product vision and

commitment to the growing importance and potential value to Oracle customers of incorporating, relating and

analyzing unstructured data for new insights.

On its side, Intel has concretely formalized links for Big Data to organizations “generating a median of 300

terabytes (TB) of data weekly”, especially since Intel communication and product offers were the first partner

to start a company project on Big Data strategy. Historically it’s natural for a hardware constructor, like Intel,

Xerox or Vmware... to have this market value, otherwise clients will go on cloud technology based on

virtualization and specialists like Amazon, Google, And Microsoft.

On its side, Microsoft provides a notably succinct definition: “Big Data is the term increasingly used to

describe the process of applying serious computing power - the latest in machine learning and artificial

intelligence - to seriously massive and often highly complex sets of information”6. Moreover, Microsoft

continues to accelerate the integration of a strategy based on Mobile and Cloud. On the topic of Big Data, they

4 Unified by data a survey of Big Data definition : Jonathan Stuart Ward and Adam Barker - School of Computer Science

University of St Andrews, UK 5 What is Big Data [ http://www.datatechnocrats.com/tag/big-data/ ] 6 The Big Bang: How the Big Data Explosion Is Changing the World – Feb. 2013[ http://www.microsoft.com/en-

us/news/features/2013/feb13/02-11bigdata.aspx ]

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introduce IA breakthrough by using words like machine learning and artificial intelligence in their products,

communications and definition of what is Big Data.

Version 3.0 of the Big Data Landscape, from Matt Turck, now at FirstMark

For each IT editors the discussion is oriented on different topic that are matching with product solution, every

definitions introduces new concepts and new IT technologies. We will details the different expectations and

opportunity on marketing by using this technology.

It is also very important to take the new technological pure players challengers into consideration. First Google,

Amazon and now Facebook, are creating and mastering the data from the Web, Online searches, posts, and

customer behavior. They are platforms that capture aggregate consumer and provide services, data to marketing

IT department. They are new competitors and partners for classic editors. And this new companies are

redefining the marketing, especially some industry like the advertising market and e-business strategies.

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The Lines between Software and Hardware Continue to Blur7

2.1.2 Influence on marketing jobs – Marketing work evolution

The last few years have brought many evolutions to the job of marketer, the automation of the media market

place for activation is currently changing the media targeting and e-commerce on the internet. The next

transformation will be the automation based on comportments on television; it will have an important impact

on the advertising investment and marketing department. The mindset and the way to build strategies and

customer relationship are now changing. We saw more and more marketer based their decision on data driven

model and consider digital as a strategic opportunity.

Across different academic research, there is a lot of questioning about what is Big Data and its application to

marketing. Matt Ariker8 from McKinsey suggest marketers to start their Big Data projects by thinking of the

end goal and then working through all the details. There is a paradox, because “many Big Data marketing

projects where deliverable of the projects become end goal itself instead of the business value imagined at the

outset.”

What is pertinent through the research review, is that it enlighten us with key elements on the world of Big

Data marketing with 4 critical asset for marketing departments

Setting up a cross-functional marketing and IT team

Prioritization of the marketing goals Big Data can help you accomplish

Mapping the data sources to obtain reporting on key metrics supporting the main objectives (KPIs)

7 The Lines Between Software and Hardware Continue to Blur – The Wall Street Journal – Dec. 2012 [

http://online.wsj.com/news/articles/SB10001424127887324677204578188073738910956 ] 8 Matt Ariker is the Chief Operation Officer of the Consumer Marketing Analytics Center (CMAC) - McKinsey

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Creating an “agile marketing” implementation roadmap which develops against the highest priority

areas to generate the quickest value.

For Scott Brinker, pioneer of IT technology “the marketing needs to take advantage of both new technology

and new talent to start creating hypotheses. Then to use Big Testing to prove them out – right or wrong.” “The

key to scientific marketing is actually the embrace of marketing experimentation as a driver of continuous

innovation.” (Interview for Forbes magazine - 2014)

Beyond its technical aspect, the Big Data opportunity has brought back the topic of Machine Learning and

predictive analysis on the field. Back in the 1980s there was a popular field called Artificial Intelligence, the

main idea of which was to figure out how experts were working and how to reproduce the tasks and rules, to

program computers with this information for replacing the experts. One of the examples of the last research on

this topic is the launch by IBM of Watson a super-computer which objective is answering questions posed in

natural language. Machine Learning is the continuity of the first studies about Intelligence Artificial, Machine

learning is a subfield of computer science (CS) and artificial intelligence (AI). Today the topic areas of

exploration are (1) ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3)

reinforcement learning, and (4) the learning of complex stochastic models9.

Predictive analytics, is a statistical modelling and predictive models. The model exploits patterns found in

historical and transactional data to identify risks and opportunities. It also capture relationship with many

factors and define risk or potential associated. The predictive analytic could be consider as the extension of the

decision making.

For marketing department, it represents an opportunity to capitalize on all customers data accumulated so far.

Marketing department needs to get along with IT department to make Big Data project work. To conduct this

success four points are relevant according to Matt Ariker. 1) Build the right teams. The two executives must

lead a common definition of capabilities, skills and tools to integrate. 2) Hire or nominate an IT or marketing

translator, install bridge process to develop flexibility across technical team and Marketing department. 3)

Enter in the era of test & learn, instead of identifying or programming large projects of acquisition for

customers, prefer to rather focus more on a few pilots or prototype programs to test collaboration and

performance concepts, discard what does not work and don’t be afraid to fail. 4) Establish a transparency

between CIO-CMO and CDO. Create a common strategy and develop a single scorecard. To better understand

the barrier around Big Data for marketing departments, I’ve compiled publications by researchers whose work

9 Machine-Learning Research Four Current Directions - Thomas G. Dietterich

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focuses around the topic of the integration of new competences like data scientist and specialized articles by

institutes on the topic.

A reality in Digital Marketing department:

The insights for marketers coming from Big Data have to drive the future of decision. It must deliver the right

message to right person at the right time as well as at the right price. In other words we need to characterize

what are the challenges and objective of this insight:

Consumer behavior and patterns evolutions: Started by a question of evolution of Media: Companies

have to measure, quantify, engage and understand the behavior and pattern of consumers on new

channels, like Mobile and new devices (IOT). The consumer panel is now accessible with crowd-

sourced solutions. It could be analyze with analytics tracking tools.

Different stages where the modern marketer can collect data.

IBM - Moving Up the Digital Marketing Maturity with Big Data Analytics

Perception and adoption of new methodology to recognize data as a strategic asset: As presented in

introduction, most of marketers are more implicated upon their intuition into making decisions, rather

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than data driven decisions. To better illustrate this statement, Razorfish10 recently conducted a study

showing that “Seventy-six percent of marketers do not utilize behavioral data in either segmentation

analysis or targeting”. Moreover, only “13 percent of businesses can target a segment and measure

results”. Two primary factors can explain why most companies are not using data:

- The lack of ability to tie together the various elements of their information-system and marketing

business tool required to take action. One of the explain is the fact that today, marketers continue to use

the technology, processes and tools developed twenty years ago or more to drive their strategy of

customer segmentation. Indeed, a vast majority of marketing executives are only using CRM,

demographic and historic sales data.

- Secondly, the study extracts that a majority of marketing executives consider that they have a strong

targeting experiences to segmented groups and adopted marketing strategies. But there is only “13

percent are delivering segmented experiences and measuring the results”. So, even those who believe

they have strong targeting capabilities (58 percent) may not be able to quantify that perceived value.

Skills and competences evolution: On this point, two factors can explain why marketers are reluctant

to conducting Big Data projects. First, it’s mandatory for marketing teams to have staff talented in

marketing analytics techniques, such as Data Mining and data science. Secondly, there is a lack of

comprehension of the skills needed to extract the value of Big Data. In the book Data Science for

Business (F. Provost, T.Fawcett; 2014) written by a Professor at NEC Faculty Fellow and the NYU

Stern School of Business and Doctor in Machine learning, “there is confusion about what exactly data

science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless

buzz”. A part of this confusion is due to the fact that the company departments have a lack of visibility

of what are the skills and profile of a good data scientist – because there are simply not used to work

on Big Data projects; also, the maturity and education doesn’t really exist on the market at the moment.

Before going further on this topic, it is important to define what are data mining and data science.

Data Miner: Data Mining is historically the first practicing in discovering patterns in large data sets. The Data

miner refers more to a computer science and to artificial intelligence. In the business, a Data miner analyzes

the historical business activities, mostly using BI tools and is asked to be able to determine metrics efficiently.

Data mining is more a disciplinary rather than an isolated work. (O'Brien, J. A., & Marakas, G. M. (2011)).

Management Information Systems. New York, NY: McGraw-Hill/Irwin)

10 The state of Always-on marketing Study – Razorfish/Adobe - 2014

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Data Scientist: According to IBM, a data scientist represents an evolution from the business or data analyst

role. As explained by F. Provost the Data Scientist is not already well defined on the market. "The industry

hasn't reached a consensus on what data scientists should know". The convergence between research and

market definition is that the data Scientist have to possess strong business acumen.

The technical mindset aspect: Through the time, marketers have drastically changed their mindset. For

example, while back in the 1980s the investment to equip typical supermarkets was not the value of the

data they would obtain but rather the cost savings. The massive investment on these projects and

change of mindset are partly increased by the technology availability (Like NoSQL and Hadoop

technology that propose easily business Data opportunities). Standardized hardware and service-

software architecture are enabling to be analyzed and massively distributed. Because they are structured

differently to classical information on websites databases, it permits to have more scalability and to

analyze the information more quickly. For example, services and technologies based on this principle

like Open Source solutions Cassandra, mango DB or Hadoop provide solutions and keep services online

like Facebook or amazon. The emergence of new technology and the internet have change the daily

work of marketing teams.

Starting from this overview of changes for the marketing department and opportunities, we need to engage and

transform the objective of a classic marketing department. Extract from theoretical study for critical

optimizations are actable. The first that can be defined is the audience optimization. Thanks to the emergence

of the technology of communication, more and more media are now connected and allow a better audience

measurement and a comprehension of customers.

The increase of mobile devices ownership and digital connectivity has turned human communications into a

rush of information. The advent of digital distribution for content and products has facilitated a fragmentation

of choices and channels. For Erik Brynjolfsson researcher at the MIT, the digital has made “The long tail of

consumer”11 emerged. By analyzing sales patterns on the internet, they found that 30-40% of sales wouldn't

normally be found in a physical store. This granularity measurement and analyze of different “cluster” provide

to marketers new tools to confront targeting strategies and customers insights.

The segmentation of prospect potential, to overlay with conversation, exposure, and third part data (From

external sources) that is correspond to targeting, permits to address the best message or products.

The impact on marketing business areas:

11 From Niches to Riches: Anatomy of the Long Tail - Erik Brynjolfsson, Yu "Jeffrey" Hu and Michael D. Smith – MIT Sloan

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Ultimately, the Big Data challenge surrounding audience optimization will revolve around the integration of

huge volumes of disparate data from many potential channels, and running complex segmentation models with

speed and great accuracy. For marketers and advertisers the impact is on three relevant tools of marketing

activation.

- Channel optimization

Evolution of media has consequently changed the approaches of advertising and customer relationship

management. Consumer behavior can now be analyzed across multi channels thanks to tracking technology

like cookies and the Internet of things. The potential for marketers is gradually more important and the

difficulty to attribute value to media channel that impacts customers is becoming complex. The difficulty now

lays on how to cover all channels and how to choose the best one to deliver a message efficiently. The ability

to retain message consistency to know audiences regardless of the channel (Named OmniChannel) has became

a priority for marketers.

According to the IAB12 annual study on Emerging Marketing Data Use Cases, “more so than any other use

case, the ability to define high-potential audiences from disparate indicators—and then communicate with them

across a range of media—represents a fundamentally new approach to managing addressable customer

markets”. The customers’ behaviors across multiple channels must be tracked and qualified, that’s why a

neologism has appeared to qualify the deep quality knowledge about customers “Smart Data”. Behind this new

terminology companies are looking for different assets for Data, as well as to be more relevant and pertinent

for their marketing department.

- Advertising yield optimization

One of the major trends of the last few years in marketing is the evolution of media-planning advertising to

programmatic and real time advertising. The industry is changing very quickly, advertisers can now purchase

ad placements through spot markets of online ad slots in real time, and we call it Ad Exchanges (Muthukrishnan

2009). An Ad Exchange works like a market place, publishers (Website, apps…) post inventory of ad slots

with a reservation price, and advertiser bid: an auction is run. We have seen an ecosystem of Big Data

specialists’ proposing new tools for marketers on the display advertising. Display advertising, has been recently

studied by researchers, two relevant works retain our attention, “the first one is an utility model that accounts

for two types of advertisers: one oriented towards campaigns and seeking to create brand equity, and the other

12 The Interactive Advertising Bureau (IAB) is an advertising business organization that develops industry standards, conducts

research, and provides legal support for the online advertising industry.

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oriented towards the spot market and seeking to transform impressions to sales13“. The second paper studied

the scheduling limits and problem in display advertising. (Roels and Fridgersdottir 2009). Their research was

focused on the scheduling problem in display advertising in the case without the exchange. Media Agencies

must adapt their strategy by adopting the technology. Programmatic refers to automated buying at a large scale

based on machine learning, Data and algorithms. In the programmatic buying Real Time Bidding refers more

about a feature of programmatic. Real time provide access to buyer at an Ad-Exchange at a price those buyers

want to pay.

Difference between programmatic and RTB:

Framework of programmatic buying ecosystem – IAB Europe

- Content optimization

Creative Driven marketing14 will become fully integrated into data-driven companies. Data-driven company

qualifies companies who are compelled by data, rather than by intuition or personal experience. New digital

tools now allows to create campaigns and test on customers differences, we call it the "test and learn"

methodology. In the test and learn we have a specific model of customers audit. It’s a totally different use case

from the classical view with data.

13 Yield Optimization of Display Advertising with Ad Exchange - S. Balseiro, J. Feldman, V. Mirrokni, S. Muthukrishnan -

Google Research - 2011 14 Internet Company using data for Design – Inc [ http://www.inc.com/magazine/201312/ryan-underwood/internet-companies-

using-data-for-design.html ]

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One of the popular examples is the Criteo Company. The company has been really disruptive on its market by

proposing platform aggregation of third part data with tools to target online segment with principle of

retargeting. Additionally, Criteo proposes strategic business revenue opportunity growth to advertise like many

companies on the Internet but have shown to succeed to position their platform as the leader of retargeting. As

research marketer McKenna introduces the concept that marketing is close to technology "marketing evolves

as technology evolves." Programmable technology means that companies can promise customers "anything,

anywhere, anytime." Which leads to think that “Marketing is everything and everything is marketing”15. This

concept appears as very fundamental to understand how Big Data technology have accelerated the integration

in marketing departments.

What is really interesting to understand is that there is also a question of perception and comprehension of what

Criteo has built. They have driven changes of perception of Media’s agencies. Where the recommendations

were based on study Institute, marketers are now more driven and guided by technical specialists. The IT

editors were able to build a similar solution, but we never saw it exported on the marketing field. The difference

programmatic aims to connect the publisher’s direct systems to buyers’ systems and provide fluent experiences

for marketer users.

3 relevant points are worth being pointed out:

- Disintermediation of brick & mortar actors

- Mastery of the strategic data market by few actors

- Fervency on Big Data tools and transformation project

15 Marketing is everything R. McKenna – Harvard business review – 1991

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The second important trend is the fast spread of the Internet and new communication technologies, which has

mainly contribute to increase interactions between people and consequently the production of information’s by

informatics and people.

2.2 Marketing Theoretical Framework

2.2.1 Traditional marketing model and marketing department

To begin, we have to define what is marketing, its scope on the business of a company and on its strategy.

Within a company, marketing is often considered as a support team for the sales department. The research

definition is, however, based on American conditions and, moreover, mainly geared to the customer

relationships of manufacturers of consumer goods and services. The definition of marketing is in majority

developed by Philip Kotler, in his eponym book Kotler & Dubois. In his first research he defined it more like

a process of relationship with customers “satisfying needs and wants through an exchange process” (Kotler,

1980). Since the 1980s the definition has developed “Marketing is a social and managerial process by which

individuals and groups obtain what they need and want through creating, offering, and exchanging products of

value with others.” (Kotler et al., 1999). Four aspects are used today to qualify marketing: as an organizational

function, as a management function, as a business concept and a business philosophy. We will, through this

study, focus more on both the management and the business concepts.

A very fundamental model in the “science” of marketing is the marketing mix. The marketing mix is a business

tool used by marketers to build an offer on the market. It exists numerous versions of the marketing mix named

4C or 4P. One of them is particularly interesting, introducing technology as a Marketspace model, developed

by the INSEAD (de Meyer et al 2001, Amoni et al 2002). The model adds three key features, and integrates

technology in a central role of the market mix. The customer relationship became central for building an offer

on the market. Indeed, with the emergence of new technologies, new companies started to base their customer

relationship at the core of the marketing mix model. Two major features are also introduced: the interactivity;

which corresponds to the way of exchange of information with customers, and the connectivity; which comes

from the open and global nature of the Internet and new business. The connectivity can be associated the co-

construction, and co-ordination mechanisms across organization and customers.

21

E-business marketing - Marketing-led business - The marketspace Model - INSEAD

The information technologies have accelerated the integration of new business departments. For a long time,

digital was considered as an additional channel of communication, it has now become a real business in itself

within companies. Introduced by the former CEO of IBM, during the 1990s, e-business has matured as a major

part of distribution of products and services. E-business is the application of information and communication

technologies (ICT) in support of all the activities of business. It could correspond to marketing applied to the

digital channel. But the transformation is more radical. We just started to define the new company as a Software

Company, based even more on its capacity to deliver the good product at the right time with the best price. The

revolution of computers already started seven decades ago, we now have new opportunities thanks to

technology development and spread. Having Software Industries directly connected to customers' needs is one

of them. “All of the technology required to transform industries through software finally works and can be

widely delivered at global scale”16 (Marc Andreessen). The marketing is also greatly impacted by this, as we

previously explained, the IT became more central and companies are becoming Software-Centric, the strategic

asset has now became the capacity of targeting customers. On one side we will have the strategic efficiency of

Software Company, and on the other side the Data Driven pertinence.

One of the examples given by Marc Andreessen is the phenomenon of software absorbing a traditional

business. For example, the decline of Borders corresponds exactly to the rise of Amazon. In 2001, Borders

agreed to hand over its online business to Amazon under convinced that online book sales were non-strategic

and unimportant.

16 Why Software Is Eating The World – Marc Andreessen – Wall Street Journal [

http://online.wsj.com/news/articles/SB10001424053111903480904576512250915629460 ]

22

A framework of Data Driven Marketing department

Furthermore, many frameworks models exist to help companies to build an effective marketing department

and to choose what is involved in it. As we explained briefly through the introduction, one of the marketing

segments that is the most impacted by Big Data is the Marketing Activation one. CRM, Advertising and lead

generation are what lay under what we call Activation. A recent qualitative study17 conducted by a team of

doctors specialized in marketing has shown that there is a key relationship between the environment, the culture

of company and the marketing (John P. Workman). They have conducted a compilation work of theoretical

background on the coordination mechanism between marketing organization and sales organization18. In one

of these articles, Anderson argues in favor for a "constituency-based theory of the firm" and says that "the chief

responsibility of the marketing area is to satisfy the long-term needs of its customer coalition". He also states

that marketing's role in strategic planning must be that of a strong advocate for the marketing concept". Based

on their work they identified a difficulty of conceptual work on marketing organization due to different

dependent variables. This is, in part, the consequence of many dimensions such as structure (Weitz and

anderson 1981), power (Hinings et Al 1974) interactions with other groups (Walker and Ruekert 1987) and

bureaucratic dimensions, such as formality centralization, standardization and optimization. (Ruekert Walker

and Roering 1985)

In addition, there is a definition of marketing for each organization. For example each company design their

respective marketing groups with the assignment of activities to functional group, the “locus of decision

making” (corporate versus divisional) which correspond to different types of possible participations for each

step or process. It corresponds to the criterias of the various decisions in the company (Varadarajan and Clark

1994). This notion fits with the complementary decision-making between performing internally versus

externally (Achrol 1991).

Example of IT marketing department structure:

Many different models of marketing department structuration are available; various factors could be taken into

consideration prior the launch of this structure in a company. Historically some companies are oriented in their

marketing development (For example: Identity Culture, Design Centric, branding positionnement ...). One

model that appears as very pertinent for this study, is the model of marketing department for an IT company.

17 Marketing Organization: An integrative framework of Dimensions and Determinants - J.P. Workman, Christian Homburg, Kjell

Gruner – Journal of marketing Vol. 62 - 1998 18 Appendix – Typology of reporting relationship of marketing

23

The "Pragmatic Marketing Framework" is one of them. It is more adapted for marketing department of an IT

editor, the framework illustration shows their different parts and main missions. There are two level in this

framework model, on the top we have the strategy marketing decision and under the line we have the execution

of different activities. Between the two of them we have an example of strategy marketing continuum. By

considering what we have previously explained alongside this study, we can here see which part of the

marketing department could be more influence by the Big Data strategy.

This model is just an example of what is recommended for a marketing department. The structural approach

has been useful for classifying the innumerable arrangements firms used to organize their marketing activities.

This perspective suffers from several crucial weaknesses. In an analysis of marketing organization conducted

on the different structures for marketing activities, few researchers have given a critical view of the traditional

approach of Organizational Structure in marketing. Four critics are addressed on these frameworks. First is the

focus on “Macro-Organizational”. As presented in the example, a product management organization within a

large consumer package goods firm often varies markedly from the same form used by an industrial goods firm

or a service organization. In fact, it depends of the responsibilities and influence of the product manager. The

nature of the interactions between the product manager and other company departments influences the impact

on the marketing performances. A second limitation of the traditional framework is that it ignores the

relationship with external stakeholders like advertising agencies, research firms… The organizational form

approach fails to consider a significant portion of the activities within a total marketing program. The third

24

limit, which is to be attenuated, is the lack of explanations in the traditional view of the linkages between the

structure of marketing activities and subsequent performance of the company in enough details. Marketing

performance can be measured on a number of different dimensions, and no single structural form is likely to

produce equally good performances on all those dimensions. The last critique about the traditional structural

forms approach, which has described deeper the control of marketing programs rather than the structure of

specific tasks and processes within those programs.

It is interesting to compare the reality of a marketing department today. The digital has transformed and

industrialized marketing departments. But as we previously explained, have we already defined the tasks and

the objectives? Three points are given by the author and seem to be relevant to identify the best investment and

the best task to deliver : 1) understand the diversity of structure available for implementing marketing activities

2) cartography and identify the likely impacts of organizational on the performance dimensions 3) examine a

set of contingent environmental factors which moderates the effects of structure on performance, considering

the functions of different specialist (market research manager, sales manager, advertising manager, etc.)

25

The Organization of Marketing Activities: A Contingency Theory of Structure and Performance – Journal of marketing – Table 3s

This different form of marketing organization are influenced by the environment and the typology of the

company. We also have to consider the technologic rupture which changes the task themselves. It creates a

new challenge for marketers inside the organization, wondering now how they can adapt their work and

activities with the complexity of different technology opportunity…

26

Real-Time Personalization: Top 5 Use Cases to Boost Conversions19

2.2.2 The paradigm for marketing employees

The major swift in the marketing department in the daily life of marketers is the evolution of an intuition driven

marketing approach, towards data driven process decisions. It’s a process and culture gap in a majority of

companies between the former practices and the new ones. But before a change of mindset for marketing work

units, we need to understand more precisely the difference between the two of them. We will detail them in the

following paragraphs.

Intuition driven marketing decision:

In the two last parts we have introduced quantitative studies that showed that the Big Data is not a common

practice in companies. The most common practice in marketing seems to be driven by personal experiences.

Nonetheless, there is a market growth of 8 percent this year for Business Intelligence and analytics tool

according to Gartner, a major part of this investment are led by IT and finance, and less than 5 percent are

dedicated to marketing. We have to stand back on this notion of intuition in marketing. It is possible to

sometimes find decisional tools within companies. It tends to be up to the company's culture, which has a key

role into deciding to use these new tools and also if a culture of data driven decision must be spread among

employees. In the intuition decision there 4 influences according to the practitioners 20 (Lisa A Burke and

Monica K. Miller 1999).

- Experience-based decisions.

By experience-based marketing, it means based on the past experiences

- Affect-initiated decisions.

For example, the sensations or the feeling that something is not understood

- Cognitive-based decisions.

Cognitive decisions are the sum of total experiences, skills, knowledge and training

- Subconscious mental processing.

Decisions based for example information which create a path processing

- Value-based decisions.

Decisions more based on personal or company values and ethics

19 Marketo what is personalization marketing – 2014 20 Taking the mystery out of intuitive decision making - Academy al Management Executive - 1999

27

Most of the research on intuitive decision-making is conceptual, and little quantitative or qualitative research

has been done in the field settings to support generalizations. For Andrew McAfee it takes a long time to build

good intuition. Chess players, for example, need ten years of dedicated study and competition to assemble a

sufficient mental repertoire of board patterns21. In the intuitive decision-making, there are differences between

two approaches to intuition and expertise that are often viewed as conflicting: heuristics and biases (HB) and

naturalistic decision making (NDM) (By Kahneman, Daniel; Klein, Gary - Conditions for intuitive expertise).

The first model based on heuristics and biases is a mental shortcuts that people use to solve problems. The

(NDM) is a framework that considers and includes situations marked by limited time, uncertainty, team and

organizational constraints, unstable conditions, and varying amounts of experience. The difference between

the two approaches are that one consider that decisions are more influenced by our process decision from

personal judgments, and the other one focus more onto taking a context and multi-influence factors into

consideration.

A statistic extract from the qualitative study of Burke and Miller, states that for Fifty-six percent of the

interviewees, the intuitive decisions were based on past experience. McAfee adds also two conditions that are

more specific in Business Companies, which are the environments and the team experiences. That is why

many economists consider that "thinking-by-numbers" is the new way to think smart. There are a lot of

business stories in the classic industry where intuition and experiences are important to succeed. An example

given by Ian Ayres in his book22 confronts the two concepts, the value starts to rely more in the capacity of

collecting and treatment of data than in to applying the experience and intuition background.

Data-driven decision:

When we started to talk about the Data Driven Decision, we have to consider decision making tools as a central

piece in the marketing strategy. Over the past few years, as we said, we were more focus on cost structure

reduction rather than to activate customer loyalty. Furthermore the dominance of Operations-centric marketing

over Customer-centric approaches have more formatted marketing departments on performance than to attract,

retain and grow the marketing KPI (Value per customers, GRP). The ability to draw deep customer insights

and bring them rapidly into operational decision making is transforming the discipline of marketing.

The lexical and technical vocabulary scope expands as we introduce the Data Driven Marketing. The first point

that is leading a Data-Driven is “understand”. For example, in e-business and on sales topic, we talk about

“Buying Patterns”. A pattern, in marketing, qualifies a customer habit. It corresponds to a segment of people

21 The Future of Decision Making: Less Intuition, More Evidence – Harvard business review – Andrew McAfee – 2010 22 Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart – 2007 – Ian Ayres

28

who operates a task or consumption in the same way. As we defined what the difference between predictive

analytic and machine learning was, we now have a clear idea of what is behind the “Buying pattern”. It is

indeed a key topic of Predictive analytics for marketer. In a marketing department, we consider all indicators

on customer behavior as critical for predictive analytics. All these information could increase new business

opportunities. A recent topic, which is rising at the moment, is the storage unanalyzed by the Social-Medias

platforms sometimes qualifies as “Oil” for marketers. A new technique named Deep Learning, which is an

extension of machine learning, could be able as an artificial intelligence technology to recognize patterns

machines that perform human activities like seeing, listening and thinking. This technology is based on three

key capabilities: 1) Helping a computer to learn, instead of helping a human to interpret 2) Specifically focus

on predicting the future or the unknown 3) Improving performance as more data is analyzed. Shortly, in few

years perhaps; we may be able to automate the detection, analyze and thinking of this models thanks to

algorithmic, intelligence artificial and Social Media. We will therefore be able to launch new Data Driven

Marketing Model based on deep learning.

Big Data accelerates the discovery of the customer behavioral patterns. In a classic business context, it may

not be so evident with Data-Context and smaller homogenous data to find these profiles. A potential source of

competitive advantage becomes Predictive Analytics, this technology learns from experience and analyzes

current and historical facts to make predictions about the future, or otherwise unknown activities. Today the

predictive techniques based on big data includes two steps23 : First, a training phase which consists in learning

from a model based on training data; and a predicting phase, which consists into using the model to predict the

unknown or upcoming outcomes. From a study conducted by Accenture24, the use of predictive analytics

tripled a third on surveyed businesses. It mostly depends of the industry or the company, but it could be a

noticeable advantage for many businesses. For example, the capacity to propose price based on historical

customers information have a big impact on the Internet. E-business predictive model can be used to

automatically vary price based on purchase trends, and optimize the search results, for cross selling or to

analyze the pattern of buying. This technique is included for instance in strategy of Yield Management, for a

variable pricing strategy, particularly in the Airline industry.

Two factors are constantly involved in the deployment of this technology and in the adoption of the Data

Driven Marketing. The first factor is to consider engaging customer in multi-channel relationship and to

consider that all data generated have a value and must be stocked and managed by a "master data management

strategy". In a short time, they should not be important, but later in a specific context; with Border Crossing

23 Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die - Eric Siegel - 2013 24 Analytics in Action: Breakthroughs and Barriers on the Journey to ROI - Accenture - 2013

29

Data this could become important. As we explained, to understand and to propose the best experiences without

being intrusive in the customer experiences is a key success factor in the next customer experiences. There are

many examples available that show that Data Driven experimentations are conducted on users without their

authorization. A well-known example is user testing on website. Called AB-Testing25, this strategy allows

marketers to collect and analyze the behavior of internet users exposed to different campaigns for example. It

is a form of statistical hypothesis testing that can be deployed at a large scale. In Facebook company advertising

department, a Data Scientist employee stated in a chat conversation that every Facebook user is a part of an

experiment at some point. Although we have accepted terms of service, some ethical questions emerge for

companies, wondering: “What data can we use to predict or use for our marketing model?” “Should we

communicate about our study related to our product based on customer analytics?”

Secondly, it’s considered that Decisional marketing is not based only on historical or predictive model. It

responds sometimes to the needs to adopt and change behaviors or market conditions. This is why a predictive

and Data Driven approach needs to be updated to variable factors. To quickly and efficiently measure, as well

as to adjust decision, analytic tools have to be accessible and easy to use. If marketers have to handle complex

algorithms it should be adjustable even while marketing campaigns would be online. At last variances between

simulations and results may be the opportunity to develop test & learn models. Thanks to a large-scale

experimentation as explained by McKinsey Global Institute research (p. 97 -2011)26, the opportunity is to

increase the efficiency of marketing functions and company. It also aims to better understand its roots and

causes, and can enable leaders to manage performance to higher levels. That is why Data Driven Marketing

requires that CMOs take responsibility for a lot of specialists. A diagram summarizes the points we have just

described. It highlights and illustrate that two points are important when Big Data enables opportunity: to focus

on personalized experiences and the immense scalability.

25 Ethics in a data driven world – [ http://techcrunch.com/2014/06/29/ethics-in-a-data-driven-world/ ] 26 Big data: The next frontier for innovation, competition, and productivity– McKinsey Research - June 2011

30

From Big Data to Big Marketing: Seven Essentials27

We have to consider that there is some limit to Data Driven decisions; there are many areas where human

beings are more efficient than algorithms. For example a qualitative study conducted in 2000 by a scientist

specialized in medicine, found that Sixty-five of analyzed studies on 136, were no better and not so different

between human intuition and algorithm28.

With the opportunity to take an expanding range of data, predictions into account, process can easily became

quite complex. It is obvious that it could be very difficult to manage for a small company. Instead of being

agile, and make better decision the model of data driven poorly deployed could lead to opposite results. With

many complex questions come many different factors. The easy-to-use tools, that enable marketers to modify

constraints to see what the optimal decision strategy would be the best suited. Decision modelling and

optimization can be used as well as to answer difficult questions. The “customer intelligence”, information

compete seems to be the more critical information that can run their business better. For that, consulting

companies or editors help their clients and companies to ask the good question. “If I offer a temporary 30

percent discount will it increase the sales on this clients segment?” “In light of our portfolio goal and this

27 Best practices for performing data-driven personalized marketing at an immense scale - Fico - Nov. 2012 28 The Future of Decision Making: Less Intuition, More Evidence - Andrew McAfee – 2010 [ http://blogs.hbr.org/2010/01/the-

future-of-decision-making/ ]

31

customer’s current and future value to the bank risk profile and expected reaction, what is the most profitable

offer?”

The vast and complicated tools available for marketer29

All the processes of decision strategy marketing can be imported into rules-driven operational systems. In this

context, we have to consider enterprise elements, such as personal daily influences (experiences, intuition...)

and decisions tools powered by Big Data and customer relationship. All data gathered became an opportunity

where new specialist, like Data Scientist, emerge and need to be integrated in this opportunity process to deliver

the best of the Data Driven possibilities.

2.1 The relationship between marketing and Big Data technology:

2.1.1 Classic critics of Marketing

29 Marketing Technology Landscape Supergraphic - Scott Brinker - 2014

32

Some studies estimate an evolution and increase of data around 1,200 exabytes in 2010 to 40,000 exabytes in

2020 (Gantz and Reinsel, 2012). In some industries such as financial services, Big Data has rapidly spurred

new business models. Algorithmic trading now analyses massive amounts of market data to identify

opportunities and to capture value instantly. The transformation of the trading is call the High-frequency

trading or Fast Trading. To model the transformation HFT represents only 2 percent of 20,000 firms operating

in US equity market but 73 percent of trading volume30. In the retail sector, Big Data provides insight into

demand shifts; stores can adjust merchandise, stock levels, and prices to maximize sales (Hagen et al., 2013)31.

The Big Data opportunity is also to be more focused on the buying pattern as we previously explained.

According to a report from Gartner, less than 5 percent of e-commerce use Big Data or predictive analytic

software. A signal important on the retail sector is that Amazon, Alibaba and Rakuten are putting project on it.

Amazon can glean from the trillions of data points generated as people browse its site.

A marketing department impacted by the transformation of market – example:

The world in which marketing departments operates has radically changed. Thomas Friedman has sketched the

new realities of the world of marketing. A majority of the marketing concepts and models were developed in

the last century and are no longer relevant today. Jerry Wind academic Director of Wharton University develop

this theory in a publication on a MIT website32. In my opinion we can keep three ideas on the fact than the

marketing have radically change.

First, a more consumer centric in the marketing strategies

The empowered hybrid consumer who expects customized products and services, messages and

distribution channels

The reluctant consumer — with declining response rates, TiVo and increasingly negative attitudes

toward marketing and advertising

Decreased consumer and employee loyalty

Secondly the border between intermediation and automatisation

The vanishing mass market and increased fragmentation of all markets

30 High Frequency algorithmic definition [

http://topics.nytimes.com/top/reference/timestopics/subjects/h/high_frequency_algorithmic_trading/index.html ] 31 Big Data and the Creative Destruction of Today's Business Models – [ http://www.atkearney.com/strategic-it/ideas-

insights/article/-/asset_publisher/LCcgOeS4t85g/content/big-data-and-the-creative-destruction-of-today-s-business-

models/10192#sthash.HbCKB1QU.dpuf ] 32 A Plan to Invent the Marketing We Need Today – MIT Sloan Management Review – 2008 http://sloanreview.mit.edu/article/a-

plan-to-invent-the-marketing-we-need-today/?use_credit=d1c373ab1570cfb9a7dbb53c186b37a2

33

A blurring of the line between B2B and B2C

The globalization and efficiency of IT technologies

The rising importance of the developing world

Opportunities for outsourcing and digital outsourcing/offshoring of marketing services (beyond call

centers)

Increased focus on public/private cooperation (nongovernmental organizations and others)

A Data Mining example overview:

To see the difference between classical and new models of analyze, I’ve chosen to interpret a classical method33

that marketers can use in their daily work. Basically this method is a classic Data Mining example.

Crossing data model and marketing – Example RFM:

Generally, the marketing strategy was mainly built on the customer’s experience with the 4C (Consumer, Cost,

Convenience, and Communication) model (McCarthy, Jerome E. 1964. Basic Marketing. A Managerial

Approach. Homewood, IL: Irwin.). The marketing framework is in fact to improve with technologies, feedback

data circulating and that are constantly updated, they could be used in the promotion of products or services

innovation.

To have an opinion and a critic about the marketing framework of 4C, there is complemented model driven by

Data with 3 stands.

- Recency: R represents client time span from the last purchase

- Frequency: F the customer’s purchase frequency, the higher the customer

- Monetary Value: the amount of consumption in a period time

This RFM Model, is use in database marketing and direct marketing, especially in the retail industry. Here it

represents the segmentation which directly impacts the accuracy of data Mining technology (M. Maia &

Almeida 2008). There is a Data Crunching, which corresponds to the step of Data Research where the

marketers need to call a Data specialist to retrieve the segment, the period and the specific value.

This model is applicable to the traditional retail industries which provide a variety of products. The idea of this

is to determine the customer value with three behavioral indicators. (Hugues, MA., 1996). This method used

33Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression

http://www.sciencedirect.com/science/article/pii/S0148296306002323

34

for analyzing customer value. That is an interesting asset, but what request lot of time and sometimes the

intervention of a Data Miner resources.

Critics are possible to address on some points, the method is only descriptive and do not provide mechanism

behavior. The first point is that this model does not include any predictive aspect. The second is to consider

that customers will keep the same behavior. It also does not take the lifetime of customer into account nor their

potential value. And finally if we compare it with new methodologies driven by algorism and optimization,

this demonstration is more focused on the capacity to analyze a period.

2.1.2 Improve efficiency marketing business and Impact on CRM

A very controversial part of the use cases of Big Data is the impacts on customers, especially in the perception

of usages by people. Companies specialized on the market like Google or Facebook have been recently facing

the media about the management of privacy information of users and the many concerns around to who they

sell these information. The Internet neutrality has been a hot topic for years now, especially for the key players

of the digital companies. There are many question regarding the principle that the Internet service providers

and governments should treat all data on the Internet equally, not discriminating or charging differentially by

user, content, site, platform, application, type of attached equipment, and modes of communication. The

challenges for companies are about how to engage a customer relationship management safely for marketing

departments.

Reduce of the silos between CRM and marketing:

35

The customer relationship management which is more global than Marketing is a strategic part of all

companies. In the academic community, the terms "relationship marketing" and CRM are often used

interchangeably (Parvatiyar and Sheth 2001).

Three different approach of the CRM continuum34

The Vendor-Relationship Management emergence thanks to the privacy involved by Big Data:

A last, experimentation has emerged in the United-Kingdom, called project MyData it’s a complete opposite

of the perspective scenario of Big Data featuring Marketing. As we explained, Big Data has changed the

paradigm and aims that marketers explore and use data collected to increase sales. A new point of view

proposed by researchers is that many market problems can only be solved from the customer side. For example,

rather than to focus on the choice of the company offers side, it will be led, decided and customized by the

criteria of the customers. More than 20 major companies have agreed to contribute to this project and to share

with their clients the data they have on them: BarclayCard, MasterCard, HSBC, Everything Everywhere (the

operator that brings the UK Orange and T-Mobile brands), Google, and many other companies in the energy

sector and in distribution... This concept is actually very simple, it is the result of the opposite conventional

approaches applied in the CRM. It is based on a simple value "a free customer is more valuable than a captive

customer". The free customers have a level of information and tools comparable to the organizations with

which they are related, it becomes difficult to propose an offer and convince through communication channels.

By cons, where we can improve the customer relationship, is letting his own data space and choice with the

ability to share (or not) information with companies to negotiate terms of interactions to better learn from these

34 A Strategic Framework for Customer Relationship Management A. Payne & P. Frow – Journal of marketing - 1995

36

actions. This trend is named VRM, (Vendor Relationship Management) it has been developed by Doc Searls35

a notorious pioneer of the topics such as intention economy and the open source topic.

In a harvard business review insight, Erik Brynjolfsson and Andrew McAfee expose a new perception of the

Big Data business opportunity. This opportunity “Simply put, because of Big Data, managers can measure,

and hence know, radically more about their businesses, and directly translate that knowledge into improved

decision making and performance”

Example of three customer’s opportunity with Customer Centric scope – ChiefMarctec.com

The solutions to Data Challenges to drive opportunities will come from more customers centric solutions. For

example the crowdsourcing services that provide better quality for customers' survey. Effectively, Big Data is

accelerating 3 main things: 1) the customer knowledge 2) the pertinence of tool to personalized customers

experiences 3) the analytics and feedbacks for marketing. The evidence is clear that Data-driven decisions

tend to be a major opportunity for companies. In a detailed survey data on the business practices and

information technology investments of 179 large publicly traded firms, a recent study36 find that firms that

adopt Data Driven Decisional have output and productivity that is 5-6 percent higher than what would be

expected. It shows that Big Data could be.

2.1.3 The critical causality

35 Project VRM Harvard University – Wiki [ http://cyber.law.harvard.edu/projectvrm/Main_Page ] 36 Strength in Numbers: How Does Data-Driven Decision making Affect Firm Performance? - Erik Brynjolfsson

37

Our period seems to focus on 'neuromarketing' and 'Big Data' marketing and many consider these topics as a

modern science of the customers’ relationship. It seems to sound rather like the advent of pure "causation" in

favor of a science correlation and data decision making. For Viktor Mayer-Schönberger professor at the Oxford

Internet Institute, and Kenneth Cukier, data manager for The Economist37. In their book the authors explain

that Big Data allows to find correlations that we have not seen prior, also called weak signals. This analysis is

not only more powerful than causation, but is most likely to supplant entirely. This new understanding of our

environment completely revolutionizes our assets and our certainties.

Ted Cuzzillo, researcher specialized in Business Intelligence, is joined by a growing chorus of critics that

challenge some of the breathless pronouncements of big data enthusiasts. Specifically, it looks like the backlash

theme-of-the-month is correlation vs. causation38. Correlation does not imply causation is in statistics that

emphasizes that a correlation between two variables does not necessarily imply that one causes the other. A

part of the researchers are critics about the data enthusiasm, Dr. Gary Marcus develop in his book39 that not

every problems are solvable through Big Data, and Big Data “can be helpful in system that are well-

characterized properties, with little unpredictable variable”, but not every problem, especially in company fits

with those criteria: unpredictability and complexity of contexts”. Big Data is a powerful tool for inferring

correlations, not a magic wand for inferring causality, and for the moment the place of human stay center in

the modification of the algorithm and the interpretation of results.

2.2 Marketing and link with Big Data what it makes the advantages

Before going further with this study, it seems important to state that Big Data is often considered a “BuzzWord”

for many professionals. It is often a term used to qualify all new technologies and opportunity to create value

with data unstructured. Unstructured means that this is the “third part” Data, outside of the company, which is

provided in major part by stakeholders or social media platforms outside the company.

As we explained, there are many critics about it and what are the businesses scenarios. But when we start to

explore in depth Big Data, there is a crucial opportunity around Data Science. For instance in web marketing

e-business, Data patterns are identified by crunching information on – among other things – corrections made

by users to searches, acronyms contracted , expanded acronyms and words that are in different languages.

37 "Big Data: a revolution that will transform the way we live, work and think"

38 Understanding why correlation does not imply causality https://www.khanacademy.org/math/probability/statistical-

studies/types-of-studies/v/correlation-and-causality

39 Steamrolled by Big Data - Gary Marcus

38

Understanding the user intent is a data rather than an algorithm task. At eBay, the technology Hadoop is used

by the search data science team. Rather than to center all their activities on technology, they have developed a

team able to understand the customers’ experiences. eBay claims nearly 100 million active users with global

search in 41 markets; more than half of all purchases made on eBay by users in those 41 markets, start with

the same simple act of the customer conducting a search query

The consulting strategy firm McKinsey proposed a cartography of opportunity of Data Science by sector. This

cartography “heat map” shows that not all the sectors have an opportunity on the topic of Big Data, as we

should attend, the main opportunity with data are in the utilities, transportation and manufacturing.

This table is divided in five categories, the way that McKinsey divides it is striking because it does not only

focuses on the Data capacity, but also organize the categories by talent and also data driven mind-set. This

proves that there are real opportunities with the right people and the right frame of mind.

2.2.1 Difference of language and vocabulary Lack of profiles and time consideration for Data Science as a strategic marketing asset:

As we explained, Data Mining profiles and Data Scientist are different. The majority of scientists agree on the

fact that there is a Data Scientist shortage (H. Davenport - Harvard Business School). If companies want to

leverage the opportunity of Big Data it appears as mandatory to rapidly integrate these new competences. As

39

they do, some direct consequences and questions arise. Such as where, in term of organisation, should

companies integrate this new profile? Will this new job be best suited in the marketing or the IT department?

The organization chart is indeed completely shaken up. The place in the organization chart is right now in

majority in suspense in companies. (F. Cuttita)

The trend is that job role must be close to business problematic (Jean-Paul Isson, CEO Monster Inc). Linkedin

organizes its Data Scientists as a product team that includes product marketers, designers, web developer and

scientist. As it explains, companies and management need to rethink the role, to define the cost for investing

and the difficulty to identify profiles. The new job profiles need to be defined and a new scope of marketing

project. (F. Cuttita). A very pertinent outlook develop by Accenture has a way to rethink the role and the skills

of Data Scientist profile.

Figure 1: The Team Solution to the Data Scientist Shortage - Jeanne G. Harris, N. Shetterley, Allan E. Alter Aand K. Schnell

Diagram: the Data Science Venn Diagram was presented as a high-level summary of the requisite skills for Data Scientist profile

In this study, Accenture explains very well how we can recreate this profile and how capitalize better on a core

team specialized in Data science where we will find four main resources to build the best Data Scenarios for

business. Firstly, the company objectives need to be aligned with a business analyst which is able to provide

the best answer. A focus on a user experience and adoption with a profile of Visualization designer, and the

technical part with a software engineer and a System Architect, these profiles are really important because they

are the guarantee of the adoption of the final product. The final part is the Data intrinsic Competences with the

role of Data Miner. Between them, these data scientist teams will have the necessary knowledge of the

company’s business needs, and the ability to:

- design statistical models for getting desired insights out of the data that is being collected,

- create text mining algorithms for analyzing unstructured data,

40

- create machine learning algorithms for embedding analytics into business processes,

- clean and convert raw data into formats that can be used by other tools,

- carry out quality assurance testing to ensure the models deliver insights accurately, and

- design easy-to-grasp ways to display insights through data visualization.

In the book written by F. Provost, he highlights a confusion about what exactly data science is, for him this

confusion could lead to disillusionment as the concept diffuses into meaningless buzz. F. Provost refers to the

media publications about it, Like in the article written by Dj. Pahil, Data Scientist himself wrote in the Havard

Business Review. Much of the current enthusiasm for big data focuses on technologies that make taming it

possible, including Hadoop (the most widely used framework for distributed file system processing) and related

open-source tools, cloud computing, and data visualization, and not on the opportunity to change how company

create business opportunities.

The strategic central role of a Chief Data Officer:

In the marketing department side, there is a new strategic role that appeared a few years ago. An annual study

conducted by corporate executive board named Insight IQ40, have evaluated 5,000 employees at 22 global

companies. They found out that employees best equipped to make good decisions were those with effectively

balance judgment and analysis, possess strong analytical skills. They were also in capacity to listen to others’

opinions but evenly also willing to dissent. Results of this capacity skill research were noticeable, with only

38 percent of employees and 50 percent of managers possessing those skills. If we take the status of market

into consideration, the current results seem to be pretty optimistic for a topic as relatively new as analytics and

Big Data.

The Analytic skills are concentrated into few employees, it’s also unexpected when a new form of analytics

tools enter into the workplace, companies typically start by hiring experts versed in using it, reasoning that the

skills will trickle down for all employee seem complicated. According the study, the functions whose

employees had the highest analytic scores, on metrics including effectiveness, productivity, and employee

engagement, were in about 24 percent better than other functions.

The underlying question is how company can accelerate the integration of analytic skills for employees and

adopt more quickly this transformation. Some data use cases are obvious, other do not appear to be much

obvious. Identifying how data can be used to support the company’s most important priorities became a

40 http://hbr.org/2012/04/good-data-wont-guarantee-good-decisions/ar/1

41

strategic role and asset in companis. Deloitte consulting published a recent paper41 on this subject, for them the

Chief Data Officer is a natural role evolution of the Chief Information Officer, The CDO’s most important role

would be to understand when business units should be looking for answers in the company’s data. Then the

process of extracting those answers begins. Many companies’ employees are stuck in the “expert” phase, they

have a handful of highly technological skills and quality on their project, but they have difficulties to train

everyone else on their analytics technology.

2.2.2 Difference of expectations the classic divergence perspective of IT and

Marketing

Gartner predicts that by 2017, Chief Marketing Officers will wield bigger technology budgets than their IT

counterparts do. It’s rapidly becoming the marketer’s work to put Big Data analytics to work for their

department. It’s a new role and skills to adopt for marketing teams and managers. As we explained, the bridge

between IT and marketing could be the Chief Data Officer. IT departments and functions grew up working

with finance, supply chain and human resources. It is now time to develop these capabilities in other

departments that may have diverse possibilities with Data, or may need Data to clearly articulate their business.

For those types of challenges, it requires anthropological skill and overall behavioral understanding (Sh. Shah,

A. Horne, and J. Capellá). Firms in which the business people do not understand what the Data Scientists are

doing could be a substantial disadvantage, because they waste time and effort or, worse, because they ultimately

make wrong decisions (F. Provost). A recent article in Harvard Business Review concludes: ‘‘for all the

breathless promises about the return on investment in Big Data, however, companies face a challenge.

Investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex

decision making.’’ we have to take care not to use experimental techniques which could imply opposite effects.

In the investigation side, two conceptions have been developed. The first one is the technical discourse, mainly

for an audience techniques and decisional like an IT management. The content of these study are often about

how the technology works. The purpose is often about how it works and what kind of offer is behind the

technology. For example Hadoop is a technology to use and create services based on Big Data technology.

Moreover, the Big Data technology is seen like a revolution of storage and usages of information. The major

part of study and work developed are focused about architecture to provide Big Data scenarios solutions in it.

The main interest and focus is done, on a technological part, which is the most of the time not up to the interest

41 The Role of the Chief Data Officer – Deloitte [ http://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/us_consulting_ti_roleofchiefdataofficer_250108.pdf ]

42

of marketing teams. A marketer tends to look more on business scenarios and strategy to grow businesses and

increases opportunity.

2.3 Synthesis of the mobilized theories

Approaches Authors Key concepts Selected references

Technologic and

management

Erik

Brynjolfsson

Andrew

McCafee

Wernerfelt

Thriving in the

Automated

Economy (2011)

Enterprise 2.0

(2009)

Managerial

organization and

strategy (1962)

Race Against the Machine: How the Digital

Revolution is Accelerating Innovation, Driving

Productivity, and Irreversibly Transforming

Employment and the Economy Nations (2012)

Enterprise 2.0: New Collaborative Tools for Your

Organization’s Toughest Challenges (2009)

A Resource-based View of the Firm (1984)

Marketing Chris

Anderson

R.McKenna

Doc Searls

The long tail

(2006)

Marketing is

everything (1985)

Intention economy

(2011)

The Long Tail: Why the Future of Business Is

Selling Less of More, New York, Hyperion, 2006

Real time preparing for the age of the never

satisfied customer (1997)

The Intention Economy: When Customers Take

Charge (2012)

43

Barry

Schwartz

Kotler

social critique of

our obsession

with choice

(2005)

Marketing

Management and

strategic

organisation

The Paradox of Choice: Why More Is Less (2005)

Marketing Management (14th Edition) (2011)

Social Sciences

Work Psychology

Nate Silver

Ian Ayres

Predictive

analytic and low

signals

Intuition and

experience and

Data driven

The Signal and the Noise: Why So Many

Predictions Fail - But Some Don't (2012)

Super Crunchers: Why Thinking-By-Numbers is

the New Way To Be Smart (2008)

3 Study field methodology: qualitative study and research field

The objectives of my field study were to uncover dimensions impacts on the marketer job. The aim was to

explore the different impacts on a job profile mainly about marketing activation. The objectives of this

exploration were to precisely cartography the company transformation priorities around Big Data. More

intention on the job will see if in daily work the marketing teams in firms were impacted by Big Data

consequences. As consequences we consider all the changes that we previously described in the theoretical

review,

- The capacity to adopt new digital marketing strategies

- Adopt Data Driven Methodology in parallel of intuitive marketing

- Embrace the evolution of customer relationship

- Integrate new skills in marketing team

44

Furthermore, the field study will give us the information and feedbacks of new skills and if the new paradigms

are well accepted and integrated in these teams. The disruption is a phenomenon that I will detail and analyze

in this part of qualitative study to see if the changes and attitudes are adopted in companies. A part of this field

study will demonstrate how teams are involved differently in the projects and the key processes that makes the

transformation for company possible today.

3.1 Presentation of the quantitative methodology:

All along the first part of my thesis, I’ve analyzed the theoretical framework about Marketing and the first

scientist publications on Data Science and Big Data that I’ve found. In my field study I’ve decided to conduct

interviews rather than quantitative study. I’ve chosen to do a qualitative study firstly because the topic of Big

Data is recent on the market, as it is explained by researcher there is no common and unified definition of the

term because it has only been existing and popularized for a few years. Secondly it’s not so easy to begin a

qualitative study on the segment of professional from business to business and collect their point of view on a

subject where a majority does not really have a better understanding.

We have to consider that this qualitative study has given me the flexibility between profiles of people that I

have chosen in interviews. As my topic is Big Data and that the scientist community has some difficulty to

explore this topic, I’ve decided to explore different typs of expertise and role on this. Rather than to test a

specific hypothesis, this qualitative research tends to engage in a much more dialectic process between the

questions asked and data observed.

To conduct the study, I’ve decided to interview all the companies’ contributors in the process of a Big Data

project. To simplify the representation, we can summarize the different partners for marketing team into this

following process:

The advantages of such qualitative approach are to be more focus on the contingent nature of business reality.

With thirteen interviews realized, and eight transcriptions attached to my thesis, I've aggregated different types

of business situations and projects. My aim was to gather all the different interlocutors that would be include

Technology

provider and

platform tools

Editors : Ex.

Microsoft

Data-Scientist,

Agency/SSII/

Information

technology

consulting: Ex. 55

Consulting

Specialist: Real

Time bidding,

MultiTouch…

Companies

marketing

department: Ex.

Orange, Microsoft

45

in a Big Data project. The objective was to create a link between their interpretations of the transformation and

to link them. To achieve a qualitative study by adding value to successful interviews, a key features was that

the central context should lead the interpretation.

In fact, one of the key features of qualitative content analysis in contrast to classical quantitative content

analysis is that the context has to be central to the interpretation and analysis of the material. One of most

pertinent is the Content Analysis - which is the most popular method for studying the qualitative interviews

and observations (Krippendorff, 2003). Although qualitative studies are criticized by researchers and

managers, (Collesei 2003), I have taken into account the field consideration during my analyzed scope, mainly

on the relationship between analyze and interpretation which are sometimes contrasted in this type of study

(Evrard, Paris, Roux 2003).

A primary part of my study is the step of extraction of the most relevant content to answer my problematic. It

involves transcribing qualitative data to provide an analytical framework. The objective of this step is to encode

the information collected and treated. To lead the transcribing and analytical I’ve use a coding tools named

QDA Miner. This tool provides me the capacity to use a tree structure and create links between equivalent

topics. To extract the best of my field interview it allowed me to encode segments and organized my analyzed

field.

3.1.1 Hypothesis

Consequently, after the introduction of my study, we have to define research hypothesis for the qualitative

study. The hypothesis focuses on the evolution of Marketing Management competencies and key success factor

of a Big Data Project. These postulates have been essentially deduced from the field observation.

Consequences on an organization and management level - Project management and skills changes:

This first framework of study will focus on three variable impacts on a marketing department: the capacity to

define the strategy view of a strategy topic; if the organization company is adapted on this topic and the impact

on the project delivery.

Classification and definition: A first proposal is to gather the different projects launched and to propose a grid

of the critical part steps in a Big Data project. We have seen at the beginning of this study that the topic of Big

Data is one of the last trend optimization and strategic investment for companies. This grip will give us a view

of different definitions and current projects on the market.

46

Impacts on the organization: The impacts on the marketing department organization, which have created a new

strategic role and a new kind of manager. Few examples were detailed in the first part of this thesis, for example

does company have a Chief Data Officer, or someone in charge of the strategic Data program? What are their

prerogatives, the place in the organization chart and the relationship with top management, and the authority

of this role in the global strategy (CDO, DDBM42...)

Impacts on project: The management of projects based on Big Data technology is not the same as the one we

have developed so far. I have tried to gather in my analysis all the new processes and changes perceived by

professionals. Moreover, the digital and customers relationship through social media platforms is now a part

of this type of project. The analysis will show if the evolution lies in either to integrate Data management

competencies to increase the potential of relationship marketing; or on the opposite, to externalize a major part

of the data management of the customer relationship.

Success factors of Big Data transformation, key success factor for marketing project and consequences on the

customer or tool adoption.

The second framework of analysis is focused on the success factors of Big Data project adoption by companies.

The hypothesis to explore will be: the results of initiative of Data Driven projects. The second way of thinking

will focus on the role of sponsor or new job title to conduct the transformation by Data. The last part will

evaluate if Data Science has a strategic role on the typology of project.

Data driven results by marketing department: With the field interview we will have details about the different

projects on the French market. We will certainly have off-interview feedbacks with face-to-face collection

methods on the reality of business. We could qualify the successful project and if the marketing departments

are engaged in transformation to better drive their project with Data Driven methodology. We will also bring

up a focus on the Key Performance Indicators to successfully deploy projects. These additional indicators will

provide us more information about how the performance of a marketing department is measured.

Culture variable and management: We will see if there is a cultural gap between the top management and the

operational team. Most of the time, the field team seems to have a part of the competencies needed to conduct

these new projects internally, whereas the managers think, or consider that better results can be led by

outsourcing competencies. We will try to sum up the different way to launch project in company. We will

examine the best skill to launch those types of project, the differences between today Marketers and tomorrow

and the time to achieve this and become efficient on the Big Data topics.

42 Data Driven Business Model

47

Data Scientist integration realism: The evolution of marketing department must integrate new competences,

we will decrypt how the new job profile is considered within companies and if they interact with them alongside

different projects.

We explained that the marketer is not like in the past years anymore, as a conclusion we will present a landscape

with all the tools that will be described in the interview cross functionalities in marketing department.

1) Synthesis of hypotheses

Project

management and

skills competences

Classifications and

Definitions

Typology of the existing Big Data project advisory

activity. What are the current priority in strategic

Data Project in marketing organization company?

The companies have clear definitions of Big Data

topics (Predictive Analytics, Data Science). They

have convictions about it and they know what the

businesses scenarios are.

Impacts on the

organization: Gradation

and progression among

different types of

organization

The impacts on marketing department organization

have created a new strategic role and manager in

company. (CDO, DDBM)

Consulting activities are still major on this project.

Only few employees are integrated in these

activities.

Comparison of negative

and positive impacts on

different projects, tools and

relationship with

customers

Explore and gather the critical results of these types

of projects.

Powerful tools and customized targeting solutions

increase the ROI of marketing department.

48

Key success factor

of marketing

project centered

on Big Data

Results driven by

department – achievement

of the company objectives

Sponsor and project objectives in companies.

Adoption and return on experiences. There are key

factors of relationship with agencies or consulting

partner. And an important impact of change

management

Is there a correlation between IT performance

indicator and involvement of employee.

The impact of this type of project on organization

and marketing team skills. Integration of data

scientist/data mining competences. There is a

methodology of Data driven marketing.

Balance of structure and

Culture variable of

management

Companies drive the digital marketing

transformation around Data Driven culture. More

than decisional Tool, employees have equipped or

will be equipped by simply tool that provide

strategic information.

There is a cultural gap between the top management

and the local staff, due to the different culture

background. Field team are ready to launch Big Data

project, but management prefer lead them with

consulting and agencies.

Return on experience of

integration of Data Science

Companies want to integrated profile of Data

Scientist

Integration of data scientist/data mining

competences are very complicated on the French

market

49

3.1.2 Content methodology analyzed – Procedure for data collection: Extraction, and

interpretation

To lead my transcripts interviews, I’ve applied a strict methodology. Figure 1 shows the classic proceeding of

qualitative content analysis from the initial theory to the final documents analysis and interpretation. I’ve

applied this methodology on my corpus of interview, I’ve divide this methodology into 4 parts. The first one

was held to collect and analyze the content from the interview. Rather than to use audio or video recordings

directly, it was more efficient to put them in writing flat for easy reading and have a faithful trace (Auerbach,

Silverstein, 2003). The transcripts of interviews were conducted by hand and record (Silverman, 1999).

Although all my interviews did not take place in a face-to-face approach (Eight on thirteen), I’ve noted all non-

verbal signs when it was possible in the interview. In my transcription, I’ve analyzed the most important

"verbatim" that were talked about in the interviews, without changing the text nor interpreting but carefully

write everything down as it came off. Secondly, I’ve gathered all the interviews, integrated them in a coding

tool, and launched the coding process. The analyze codage process has included the transcription of oral record

interviews to a text interview. With the coding tool, the content analysis was structured to be recomposed in

subcategories. I've decided to divide subcategories in parallel of my hypotheses.

- Definitions or convictions around the new topics

- Management structures, stakeholders, partnership and project management

- Priorities, key steps and profile involvement

- Significations, frictions between company departments

The last part objective was to extract similarities between expressions, sentences, words, to allow me to identify

relevant points. Coding was the step of ‘process of attaching labels to a segment or a phrase that summarizes

and categorizes this data’ (Lapan et al., 2012).

3.1.3 Procedure for data analysis:

After coding analyze and creating the link between information and sentences in all the interviews, I’ve made

a convergence with the hypothesis that I’ve introduce before to start my field study. This convergence will be

conducted by a semiology study of the content of the interview. The objective was to detect main trends and

to verify them. To identify the different clusters of data-driven marketing in the context of marketing

departments, I’ve decided to start also a cluster analysis. The aim of cluster analysis is to discover distribution

patterns and to identify interesting correlations among qualification and data attributes (Han and Kamber,

2006).

50

Figure 1: Basic proceeding of qualitative content analysis (Source: Author based on GLÄSER & LAUDEL, 1999, p.4) [58]

The procedure to transform the records speech into text and the construction of materials for analysis was

structured in a context (Bardin, 1977). In fact one of the difficulties of collecting results interviews to consider,

is that sometimes it could be incomplete and contradictory to interpret the similarities and differences between

respondents and achieve an objective analysis. That is why in my analyze I’ve shade analyze, the most relevant

information from content analyze interviews.

3.2 Data collection protocol:

In any qualitative study design, the data collected is used to develop evidences and example practices from

which hypotheses and theories are built. In these cases, the data collected and coding are often applied as a

layer on top of the collected data. The opinions and results from qualitative interviews, come from experiences

that have been felt by professional. All this results are eventually derived into knowledge and evidences. We

must therefore review all the qualitative analysis as a picture of Big Data trends impacts on Marketers jobs.

All the evidences and keys analysis that will be summarized in results will have to be considered within a

framework of a typology of companies. For this purpose, analysis will be built through a generalization analysis

from a recurring data and the crossing of data for a more subtle analysis.

51

The data collection follows different procedures according to the used source. The main source of our

dissertation is interviews, followed by observations and secondary sources are derived from market study,

reports and indirect actors of the system.

Primary sources:

The primary sources were mainly collected through thirteen semi-directive interviews. These interviews were

conducted on the field, between mid-April to mid-July. The meetings were either fixed by email two or three

weeks before, and some rescheduled with text messages. For my interviews, my objective was to realize a

majority in face-to-face. This qualitative survey was the opportunity to meet influencers, top managers or

directors of departments. It was a great opportunity to have recommendation and an overview of the capacity

of the company they worked for (objectives, development...). The meetings were also a great opportunity to

understand better the variability of resources engaged in this typology of project, and to have a better

understanding of the topic overall. Secondly the majority of these interviews have been more of an open

discussion with semi directive questions with directions to get better results. Emails to approach the

interviewees would follow the same structure: presentation of the study, main themes of the interview and

length. These interviewees were mainly found by research on linkedin and introduction across different

conferences and introduction by professionals’ interlocutors on the market.

When I started my research I wanted to exclude interviews through phone or software, but the context and the

availability of my interlocutors were sometimes very complicated to manage. For example, I’ve exchanged

more than ten emails and two phone call just to have an interview with a manager from Criteo. I can explain

this situation by two points: The first is that Criteo is very solicited for interviews by students, but mainly by

the Media. The communication is now managed by a Press-Relationship manager, who is in charge of the

situation and is the one to appoint the people who can answer to those type of questions. A recent news showed

that Criteo could potentially be bought by Publicis Group. The second fact, is also related to communication,

but more centered on the Big Data topic. Because it’s a very hot subject for many companies, their objective

is to monitor Criteo strategy and all the relevant information about it. Fortunately, Most of my request

interviews were accepted as well as performed in time. My opinion was to conduct face-to-face interviews, so

it would allow the interviews to be more profound and longer than expected. Therefore, a face-to-face interview

seemed a more natural approach. Initially, meetings were fixed to be lasting for one hour, some interviews

finally went over an hour, for two of them more than two hours. My interview tool was only my personal

computer and a voice recorder. While recording, I took notes and highlight the most important part of the

interview. Indeed, by transcription, we could not see all the non-verbal codes which emphasize the discussion.

52

Some observations were made during the interviews, but observations were mainly practiced on website

content and in press release.

Secondary sources:

These sources were indirectly linked to the marketing department or Business activities. They offered rich

information regarding the general trends of the Big Data sector and the Business environment of my field

interviews. These tools were used, to gather indirect information to help me understand and bring information

in a meaningful context. The main objectives were to stand back from the interviews to remember the purpose

of my thesis.

This information was captured in several ways. First information about products, programs or companies. The

monitoring was done with an analytic tool named Google alerts. It provided me with the last information about

the company that I interviewed. Although often limited and sometimes imprecise, they would offer the general

lines needed to orientate the analysis, and obtain a first degree of understanding of the happening events in the

part of the interviews.

The second was the observation of the analysis from both paper, and internal documents. In fact, my

experiences at Microsoft gave me the opportunity to work with a team dedicated to Business Intelligence and

Big Data opportunities. It helped me to better explore many documents about this new topic and to meet

practitioners. To complete the secondary sources, I reused specialized documentations from researcher as a

start to my qualitative interviews, for example I attended conferences43 where researcher speakers presented

their conclusions works, and recommendation for companies. It gave me also the opportunity to collecte

different information from IT editors and to discuss with them informally of my research thesis. I’ve also attend

several workshops and hackatons about the customer relationship and the open data opportunities, I have, for

example, done a workshop with three Ph.D. specialized in Artificial Intelligence, who provided me with the

background to better understand the new challenges of these technologies.

At last, I used different documentations written by institutions such as IAB44, EBG45…In these white papers,

I found many testimonials and interview collections about this topic. I also used to a comparative analysis of

documentation from IT editor like FICO, IBM… to conclude this study.

43 Example : Big Data forum [ http://www.bigdataparis.com/2014-fr-programme.php# ] 44 The Interactive Advertising Bureau (IAB) is an advertising business organization that develops industry standards, conducts

research, and provides legal support for the online advertising industry [ http://www.iab.net/ ] 45 EBG is the first business club of the digital economy in France [ http://www.ebg.net/ebg_live/ ]

53

3.2.1 Construction of the interview guide for the study field

To lead my research field, I’ve decided to propose an interview guideline which has contributed to follow a

weft during my interviews. This interview guide was an introduction to help me to extract my hypothesis. The

theoretical part of my thesis has showed that there are different ways to explore the Big Data topic. We could

consider that my guideline was more a frame to center the discussion, and target best people to answer my

questions. The role of this guideline was also to introduce my thesis and valid the content discussion with the

interviewees. This guideline was tried on a representing "sample" from my colleagues, and specialists. The

results of this interview were very important to go ahead in my analysis.

A questionnaire was established for the semi-direct interviews. The questions used to ask the interviewees were

classified into five main themes (translated from French):

Themes Example of covered issues

Personal trajectory Experiences before this position in company. Education and

Skills background

Role and job profile

Day-to-day organization

and management of the

firm

Tasks and processes

Job prerogatives in company, relationship with other department

Management company style.

Employee engagement process.

Marketing department

definition and project

management

Positioning and services

proposed

Projects developments

Strategy of the company

Competitiveness of the market.

Project development internally: Marketing activation, Real time

bidding activation…

Methodologies & KSF

Client management.

54

methodology, clients and

mission management

CRM Methodology

Adoption tool

methodology

Key success factors

Stakeholders.

Change management process.

Social and cultural constraints and habits.

Data Science integration

Data Driven integration

Client or intern evolution – Next projects development

Positioning in the long term.

Trend and vision for Data Science.

Synthesis of my methodology.

Choice of methodology Qualitative study

Tools for data collection Semi-direct interviews of marketing departments

and consulting companies

Job profile:

- Chief marketing officer // Chief Data

officer

- Data Scientist // head of business unit

Company typology:

- BtoC and BtoB companies

- Agencies

- Consulting firm

Collect:

- Conferences

- Workshops & Hackaton

-

Non-Academic publications:

55

- EBG, IAB…

- IT editors white papers

Data collection protocol Interview Grid:

- Personal trajectory

- Day-to-day organization and

management of the firm

- Methodologies & KSF

- Data science and Data Driven

integration

Observations:

- Based on notes

- Non-verbal sign observation

Data analysis Interview analysis:

Coding and semiology analyses

Landscape tool

Cluster analysis

3.3 Presentation of the field study:

3.3.1 Context of the study: Key stakeholders, historic

As we explained, Big Data has accelerated transformation in a majority of marketing departments. As discussed

in the first part, the digital has also itself contributed to the acceleration of the adaptation of competencies in

marketing departments. Technology is becoming less central in the decisional tool. The Data-driven approach

has made new skills in the daily work of marketers emerging. Big Data has become a current topic on the

market of technology. We have to notice that the context of study is in part done on the French market which

is less mature on this topic than the North American one. As it's presented annually by Gartner company, the

"hype circle" is here showing that some technologies or opportunities based on Big Data scenarios begin to be

industrialized in companies. In the two diagrams below we can see that there is a maturity breakthrough across

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the Atlantic. The technologies have now to be industrialized to take part in all the marketing teams in

companies.

Megatrends Driving Significant Profile Shifts46

I've chosen a small representative sample of business-to-business companies on the French market of Big Data.

I have targeted the companies specialized in platforms and tools, editors like Adobe, Salesforce and Microsoft.

I’ve also chosen these three companies because the Big Data is a central offer for their business activities.

Microsoft has recently developed machine learning tool available in the cloud. Salesforce bought startup

RelateIQ47 which uses searches of unstructured data from email, social networks, and calendars to automate

large portions of the sales process. The last one, Adobe, is currently the most oriented on digital marketing.

The company is recognized as a specialist on the digital advertising with more than ten acquisitions specialized

in this technology48 in the last few years. I’ve met representatives and employees of the three companies during

conferences, and made interviews with people from Microsoft. I have in fact, conducted two very interesting

meetings with their CMO, and Product Manager of big data solutions.

Afterwards, I’ve asked people from companies specialized on the topic of Marketing and Big Data, it took an

important amount of time to process this by email. I’ve started with Criteo, Weborama, Fifty Five and Ogilvy…

The three companies are young on the market and are specialized in media, segmentation and retargeting. It

was not so easy to connect with employees from these companies. Seeing my problematic, which is on the

spotlight of mass-media, companies markets. The strategic information and communication were hold by the

communication departments or press relation, which has complicated the task. My objective to contact them

was to understand how they worked and the key success factor of the relationship with their customers. I’ve

succeed to have meeting with one of the Data Scientist from Fifty-Five.

46 Megatrends Driving Significant Profile Shifts - 2014 [ http://www.gartner.com/doc/2816917?refval=&pcp=mpe#1095540185 ] 47 Salesforce Buys Big Data Startup RelateIQ For Up To $390M [ http://techcrunch.com/2014/07/11/salesforce-buys-big-data-

startup-relateiq-for-up-to-390m/ ] 48 List of acquisitions by Adobe company [ http://en.wikipedia.org/wiki/List_of_acquisitions_by_Adobe_Systems ]

57

I’ve also requested interviews from the international market by targeting medium startups on specific sectors

like "People pattern", "Agilone", who are two startups specialized in retail and the Big Data… To conclude

I've asked feedbacks from companies marketing department who have launched projects, like Orange who

have multi-projects around the topic of Big Data and who represents an important investor in marketing. In

fact Orange is the third advertiser in term of marketing budget in France. Moreover, a part of 30 percent of his

budget is invested on the digital. It represents the first advertiser on the digital canal on the French market.

Then, I’ve also met the SNCF, because they have a strategic program around Open Data, which is one of the

most strategic topic in France.

3.3.2 Challenges and key event:

To get in touch with Microsoft product team and marketing department was the more simple for me. With

direct connection with the Chief Digital officer, it has allowed me to discuss and exchange during several

opportunities. It helped me to better understand the strategy of a multinational company on a very complex

topic that Big Data represent. The context of Microsoft is also complicated; two aspects are important to

understand. On one side, you have one part of the company dedicated exclusively to the business-to-business

part of Microsoft activity. It’s a complicated market, where you have to adapt your product to the client

problematics; we are more on a scalability and technical competencies. On the other side, more recently, they

have changed the direction with the business-to-consumer part of their activity, and the mass media marketing

where a major part of the Microsoft products have to build a customer experiences, and adapt a program of

customer relationship management. It’s a new position for the Microsoft Company, they were used to

competition with Oracle and IBM. The new competition will be to provide transparent services, to provide

better customer experiences based on services using Big Data technology. We will give more details later in

the content analyze on this, especially with the implication in a company marketing product for different usages

of a same product.

I’ve also chosen people from media departments in the media and advertising industry; like the Chief Data

Officer of the Economist magazine. My choice was oriented on him because he is recognized as a specialist in

the launch of Ad-network and The Economist is one leading Medias on this topic. Because they have Kenneth

Cukier who is a famous data author49 on this topic. I’ve also interviewed Stéfan Galissié who is the Chief Data

Officer of the Ogilvy media group. He is the director a new practice inside the advertising agencies with

consultants specialized in Data Planning and Data mining.

49 Big Data: A Revolution That Will Transform How We Live, Work, and Think V. Mayer-Schonberger, K. Cukier

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Orange group was in my opinion a very interesting case. As I mentioned, Orange is one of the company in

France that invest the most in online advertising, but it’s also one of the biggest company data collector on his

customers. The telecommunication company has many information with the data of our smartphones, but also

web traffics from our internet box. In a new context with aggressive competitor It was very pertinent to have

a field interview from this company. For the SNCF, it was the end of the annual program on Open Data Schaker

experience50. This annual program is a competition between startups to propose the best scenarios with the data

provided by the SNCF Company. For SNCF, it’s a different integration of innovation and new marketing

scenarios that was pertinent to study.

My choice to interview two startups was mainly because they won international competitions with their

marketing algorithm and tool. The interview will provide me with a new scope of study and new field to

explore. The last interview that I’ve done is the one with Fifty Five, which is a new disruptive competitor on

the market. Founded by two former marketing directors from Google, they propose a marketing solution based

on Data Science methodology. It was directly addressing my topic and seemed obviously pertinent to interview.

The main characteristics of people that I’ve chosen for my interviews, is that they have all already spoken to

conferences or on a specialized marketing media. Most of them are specialized and recognized on the market.

All those key factors, have made my selection and the variability of my choice: 1) the availability of the person

for face-to-face interview, 2) the pertinence and the affinity on the topic of marketing 3) the market image and

recognition.

4 Qualitative study: Results

In this qualitative analysis part, I will try to verify my field hypothesis study. I will begin with the evolution of

the Marketer job, which will contribute to analyzing the different definitions of Big Data in different types of

company. These definitions will give me a fundamental material to analyze the consequences before and after

the evolution of an organization and management level. Then, we will focus more on constraints or

simplifications of this type of projects in the daily work of Marketers. As I explained, this first part will be

focus on how to start a project, and is a summary of interpretations of my interviews.

My objectives are also to explore the relationship in company process; the second part will be on the impacts

of Big Data projects on marketing department. I will review the coordination mechanism implication for a

marketing department in a Big Data project. This part will be oriented on the consequences as well as on the

50 Data Schaker accelerator program [ http://data.sncf.com/ ]

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evolution structure of different teams on projects, the returns of experiences and best practices applied in the

company. Consequently, I will try to focus more on projects which have maturity in the companies, it will

provide me the first return of experiences with the Data Science practice approach.

Lastly, we will define a landscape impact of the marketer interactions with decisional tool. We will see the

evolutions on competences, for instance, positive, negative or undefined effects, effectiveness. This tool will

gather all the information about the competence changes across the usages of new marketing solutions. It will

detail the compressive characteristics of the marketer job. And a cluster analysis will provide the different

stages of solution integration and complexity of sub-topic.

4.1 Characterization of projects and initiatives clarifications:

Beyond the buzzword, there is a real clarification of Big Data projects. For Damien Cudel, who represents the

head of all “Data products” in France for Microsoft. There is two different typologies of clients on big Data

Projects. This typology is very fundamental to understand the objectives of different projects in companies.

The strategy of Microsoft is also focus on engaging client adoption with tools by the users “So often in the

approach taken by Microsoft, the goal is to democratize accessibility to technology and solutions that would

take advantage of the data and somehow develop a culture of data to draw the benefit of the use of its data”.

For every people of my field study, they have considered that big data must be a business ambition before to

be an IT project and a company objective. Two scenarios are identified by the Chief Data Officers: The first is

the industrialization of the production of indicators, such as the “Business intelligence reporting”, it is nothing

more than just a little more advanced, “but it is the advanced BI” for Mr. Imbert.

The examples given by Microsoft of this typology of project is: 1) “When it is Led by IT, 2 questions on how

to implement (scalability) and development (cost)” at the opposite: 2) Marketing “want to get into the Big

Data experiment in trying to launch Data-Lab. Within extreme cases such as for example in the banking

sector.”

If we made an addition of extracted sentences, we could have a projection with two kinds of Big Data project

definition:

IT & Marketing difference Big Data picture

IT lead Marketing Project

Marketing lead project

Sponsor Lead by IT Innovation Or marketing

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Objective Implement Scalability Test & Learn

How Minimum viable product Proof of concept

Interlocutors Engineers interlocutors Marketing officer

Approach Project Experimental

KPI Estimated ROI Questions opportunities answers

Structure Advanced BI Data Labs

Key stakeholder Data Minner Data Scientist

IT integration Core IT Shadow IT

IHM focus Back ends Front end

Challenge Risk aversion Failed experience

Output Reindustrialization Business Case

When we focus on the topic, we have two main categories of projects in marketing. The first is about the

understanding of customers’ behavior (with four sub categories). The second is the improvement of processes

(Industrials or automation). These two classifications of projects have a strong concept which is the

“transversality”, this is in addition used in all professional in my interview. The current topic that companies

are working on, are more focus on the capacity of "interconnecting" their Data, and what they call DMP “Data

Management Platform” for the marketing part (Mrs. Pere, Galissié, and Imbert) and the technical part (Mr.

Mehl)

The evolution of this term refers to a “capacity”, but for experiences project interviews, the definition of the

term and of the project scope change as well very quickly. “I think Big Data from a technical point of view is

equal to a horizon, there fifteen years ago Big Data meant a technological horizon for 100GB of data, we had

a problem of Big Data in terms of space. Today Big Data at time T is a set of technologies that scenario does

not know how to address effectively”. They consider four opportunities for marketing departments:

- Customers intrinsic Data: Attributes eg demographics, to analyze the receipt of a product by manipulating

variables and customers

For example using these data to propose different prices in different contexts, as for example management of

price or Yield management.

- Data environments / contexts: reaction of customer, such as behavioral data. For example retargeting

customers (with recommendations of items, targeted codes, etc.)

- Sequential: signals for the perception / sales scenarios over the time: Data from purchasing behavior. The

question here is what should be the next best actions to be proposed. To give an example. The common best

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case is when they equipped hundreds of cars with beacons and analyzed how the driver reacted, as well as their

reactions, in order to conclude in adapting the different signals in the car.

- Analysis of the graph to defined relationship - The analysis of the graph allows marketers to define

relationship and influence factors between several people. It provides a new possibility of "influence

marketing".

These points represent five Data opportunities to build scenarios or project for companies. We will also add

that “scenarios” is term that is generally used to qualify an opportunity to increase sales, opposed to the term

“project” that is a standardized and industrial project of Big Data.

4.2 Complex transversal projects with agile partner structure

In many return of experiences, we have an important notion that happens to quite recurrent, which is the notion

of ambition. It corresponds to “something that it is selected and not imposed”. The company is changing very

quickly and collaborators may not have time to develop their competences in each part of the digital

transformation (Transform and expert of Print Marketing in Social Media expert, Mobile expert and Big Data

expert requires lot investment from companies)

Three relevant points are critical to this evolution:

1) The ambition (As explained by Mrs. Pere, Galissié)

The ambition of the Marketing Department is demonstrate by three points:

- Identifying an “ambassador” in the company

For example the ambassador of Open Data is clearly identified in SNCF Company. Mr. Lalanne has been

defined has a central ambassador of Open Data in the company. To help him in this tasks, he has designed with

the top-management, an ambassador in each business unit of the company. It can be sometimes seen has a

paradigm for employees who want to keep the lead on this project or in the “governance” of Data Mining

project for The SNCF Company. As we begin to see there is a friction between “Governance” and

“Ambassadors”. It is also important to consider the “ownership”, which is considered as a strategic value by

SNCF to keep a goodwill. The Chief Data Officer could represent this role ambition in the company. As

explained by Mr. Galissié 3 qualities are required for this role, 1) comes from business, 2) always be assisted

by a technical 3) role of catalyst.

- Communication and “internal sales”

Mr. Pere launched a big corporate internal communication campaign on the culture of Data Driven to improve

the comprehension of the topic internally. For them it was also facilitated by an iconic ambassador in internal

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with Kenneth Cukier. The Economist company explained that they have made important investments to

convenience and reliability and the scenarios of the tool. Their objective was to convince all their teams of the

utility of the tools, for that they have started to create Big Data scenarios. In fact, you can’t explain that this

tool or this algorithm will bring better daily-work to business team without examples. It is very well explained

by Mr. Pere, who began to test business scenarios from the beginning of the collaboration between “BlueKai”,

which is a subdivision of Oracle specialized in Big Data and marketing activation. These test scenarios allowed

to quickly preview the results of the possibilities. The forces of “adoption”, mentioned by all the interviewees

are very important for marketers if the companies are in capabilities to provide them with the right scenarios.

- “Un-silots” department but structure team in project

The Big Data projects focus on the customer and the customer's data. It is therefore essential to organize internal

actions with transverse approaches and processes. Because you are able to gather client information

everywhere you have to “adapt in consequences your business teams” for Ogilvy company. The notion of

“adaptability” is also very important, it is not only introduced by the project management, but it seems to be

a factor to apply the digital transformation and the changes of mindset. And when we talk about marketers who

applied the same methodology for ten years, the concept of change management become very fundamental. At

a macro-management level, the marketing department must be composed of people able to juggle between

intuitive and decisional. Mr .Hoang from orange falls in this role, “This requires an organization to evolve

from silos to a project mode. This is the meaning of the new cross-functional projects at Orange France” The

objectives of these projects are to be a part of the transverse digital transformation & acceleration, it's although

named “Big Data acceleration".

A project team with “capabilities to industrialize projects” and at the opposite an organization that has

“tranversality” and “scalability" to respond to the solicitations from all departments in companies. (Like Sales,

Research department, Financials...). These two notions are not opposite but could create frictions within the

global objectives of the Marketing department.

2) The state of mind

The fact that operational teams have to be involved is very important to succeed in this type of mission. To

conduct a Big Data project transformation in intern, you have to get your team involved. We presented the

capacity of the “adoption” without employee back to the wall (M. Pere). The next step of an adoption is the

capacity of “training” the team. Moreover, the mindset of Big Data corresponds in majority in my interviews

to “the pursuit of a deeper understanding of customer behavior through data analytics”. Some elements are

relevant to enter in this culture “a culture of testing throughout the organization”. Use a “bottoms-up

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approach”, although the ambition must be led by an executive sponsor. The state of mind is more a global

“adoption” of Marketers department who believe in the business capacities of the project.

3) And the capacity platform

When we say platform, we make more reference as an adoption tool that is easy to use. The platform and use

cases are requested to be known by the marketers. By platform we mean as well, an IT functional operational

as a question of process and usability for the marketer questions. For example for Mr. Pere, the choice of

Bluekai has been conducted because there is a “plug & play”. Their choice of platform are always a question

of “Arbitrage”, which is a complex selection between “cost”, “experiences value”, and the “capacity to

deploy” the best solution in the best delays.

Growth Businesses and influences on project capacity:

In the context of market companies, there are many different business environments for marketing departments.

This environment impacts the capacity of transformation and adaptability. Mr. Lalanne Head of Open Data

SNCF explains the deployment of this program by a cluster of maturity very well: “Earlier in the process we

had lines of business in very competitive situations”. He means that top department leader Business Unit can

quickly adopt the Data topics, and at the opposite, individual business departments could go very fast in the

process, and other business departments could be very slow to adopt the tool or processes "taking into account

their business issues.”

The last point on the capacity platform is the ability of marketing teams to bring value to Data Scenarios, two

point of views were adopted during my interviews. For SNCF, which is more oriented as a Service Company

“The Data itself has no value, it's rather more than the possibility of crossing data which adds value and the

possibility to node Data. This possibility will give value when it comes to enrich services. That's also added

value when it is done by a start-up, a popular transport that enriched an existing personal service with little

cost. It is a relationship win-win” The opinion of SNCF is to open the Data to the Startup external ecosystem.

At the opposite M. Pere believes in the value of the “capacity to grow” in competences and comprehension of

customer behavior by the inside of the marketing department. He also adds that keeping the Data inside the

Company should be a main objective. “There will be less and less needs for third party data collection, because

companies start to think more internally. How do I bridge the gap, how to have me third hand data. If someone

can analyze such behavior online and fill out an online form, the next coming features online with this behavior

is to apply them to the socio-demographics profile. That is why you have to keep the capacity of acquisition of

data and the targeting part. This second step is easier than the first one in terms of effort. It is only the first

one that we start which is currently complicated to deploy”

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4.3 Contextual evolutions of marketer skills:

We tend to say that there is an important culture gap between the existing marketer and what the companies

would like to become with the emergence of Big Data tools. In most of my interviews, the marketer of today

considers it like an opportunity to grow their business. They tend to be more driven by Big Data, which means

for them that they have to integrate new competences that are not existing today in their team. In the quest of

the new digital marketers there is a limit and reality that is transcripted in my interviews.

1) The impossible dream:

Most of the managers or executives want to effectively exploit “opportunities” through Big Data. Most of the

time, it’s the role of the Chief Marketing Officer to define these opportunities, or at least, to define it like a

“captain in the storm” to CMO quote of Microsoft. The “battlefield” to explore is for operational teams. It's

them who have to ascend the information’s of business opportunities. For example at Microsoft, Mr. Imbert

explained to me that the focus strategy is done on Social Media and Events. They think that they can enhance

the customers’ relationship BtoB and BtoC in using Data scenarios on these channels. To address them, they

work with a team mix (Data Scientist, Digital Marketer, and Social Media manager) to provide the “best

experiences possible” through these two channels. But every company doesn’t have integrated solutions and

technical facilities. There is a real risk of failure regarding the transition of the culture of data, because if tools

are not user-friendly or do not answer to the marketing strategy alignment, it may never be used. To be able to

engage the transformation, we must answer to the most asked questions to marketers, firstly on an advertising

side, an answer is given by the most requests that the tool providers receive. Like People pattern teams, gave

me some example:

- Give insights quickly,

- Who is talking about their brand,

- What people are saying about their brand, how they are saying it,

- How to best engage with my customer audience or prospects,

…

Overall, some questions seem to be redundant for marketing, the capacities to define segmentations, targeting

and estimate Result On Investment. With the advent of Big Data and for the first time for marketers it seems

they will be able to “quantify their discipline”. For Sebastien Imbert there is a turn in competences inside his

team “If it's a Marketer landa that formed the traditional marketing methods it is almost impossible to get to

interpret and visualize its data better”. And it has an important impact in their recruitment they seem to have

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turn to profiles that are more specialized in statistics, or more centerd on the “optimization capacity” –

“marketing thereafter with an affinity with numbers and that will take roles in optimizing”.

As reminded by the marketers in my interviews, the fundamental of marketing is “the relationship”. One

problem is then pointed out “The problem is that often customers Btob says that they want make finer

segmentations”. But in most of these cases, if it’s to create a new segment, they also need a new line of products

or different stories, and often they do not have something new to propose. Better understanding the customers

for marketers means also to have something to propose to them. “In this case, we explore most of the

possibilities of Big Data, but it could backfire”.

This is the reason why we saw some new emerging companies on the market. Like Fifty-Five which is a mix

between “technical, agencies and consulting”. According to the person that I’ve met from this company, they

have a flexibility which can provide 1) answer to the business question (not only segmentation questions), 2)

respond technically quickly by deploying algorithm of “learning capabilities”. A major part of the market

considers that companies are looking too much for something that do not exist, as I have explained it in this

thesis, for the job of Data Scientist, but it’s also the same thing for the marketing team.

The diagram shows a part of the sentences extracted from my interviews. A part of it is asked by the market,

the other one is what marketing manager or companies ask from the Big Data opportunities. As we saw there

are a lot of constraints for marketers, they already have a lot of responsibilities, and they must also find

opportunities and scenarios through Data, a task that seems complicated by the numbers of daily task already

impacted by Data. We want them to have competences in: Business Analysis, Communication Management,

Media Strategy, CRM Management, sometimes Social Media management and now Expert in Big Data.

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2) The “Reluctance” and change management:

There is still “reluctance” encountered among some of employees who would settle relational databases whose

technologies are known and mastered by them. For The Economist, during the process aggregation around the

project of DMP, they have seen some reluctance from old parts of their marketing department. Some had found

it hard to project into the future involved of the move to Big Data and to be a part of the technological shift.

For People Pattern Company, the main objective is more to follow their clients on project rather than to focus

on Big Data, more focus on transforming unstructured data from many sources into “meaningful structured

data for our clients”. As it is impossible to be an expert in all the specialties of the theme (Obligation to select

multiple specialists), they have chosen to be specialized in this topic. A very important part to assure the success

of the project is to “be aligned with the best partner”. To engage marketing team easily in Big Data, you must

provide them with the strategic asset to be “better in their job”. At Microsoft this adoption strategy is conducted

by the “Smart Data”. Indeed this strategy is less antimonial than Big Data, because the Smart Data refers more

to the acceleration of "intuitive capacities of the marketer". In Internal team, Smart Data is considered as an

accelerator of ideas of business models and possible dispositive. The CMO also explained that the situation is

more focus on to “adapt quickly Marketing competencies” rather than to transform it brutally. It seems less

expensive and dangerous to adopt, and to decline this strategy in small steps, seems to be more implementable

and incremental for long terms.

4.4 The expectations of Data Driven Marketing

The Big Data technology is considered as a rupture technology for Stephan Galissié “The Big Data is often

represented as a phenomenon of rupture, but is not new to the concept. It is rather than a decade that we are

talking about data mining and the rise of data volume. Behind the term Big Data, there is especially a change

in attitudes, which grows more and more in companies to make the data a key asset”. For the agencies, they

consider that Big Data is no more than a strategic asset to answer decisional needs. For Stephane Pere “Big

Data has changed our report to the data, allowing us to behave to exploratory in a different way”.

1) Data Visualization experience exploration

One of the initiatives that has grown quickly on the market is the experience through Data Visualization. For

the SNCF, “The tracks of data visualization is more about raising awareness of an issue of mobility” it’s a

strong challenge and approach on the transparency which is very interesting internally and externally for

companies. Because most of the part of the topic of Data as been considered as a complicated topic, it is not

really interesting to adopt for marketers. “For more than 95 percent of people internally of the SNCF Group

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the Data is a boring topic”. The rupture really lies in the capacity of interpretation, according to your needs

and uses. You can propose different approach to facilitate the comprehension of a topic. This tool can be used

both internally to convince business partners and externally to explain something complicated to the

shareholder. It is not for nothing that M. Cudel at Microsoft is also in charge of the excel products who provide

new experiences with Data like Power BI51 “Customize report, capacity of gather social media data, build

interactive report, are powerful functionalities that give immersive experiences for users… We are no more in

constraints of usages, but really in user-friendly usages”.

Examples of functions and visualization – Data Publica

These points are really important to understand, if you give all the capacity of a team to work in Data Driven

Approach, but that all the tools that they used are not at all performant, It could create a dissension, and being

a big failure. For The Economist there is an experience of this type. In their project, there was no user-

experience thinked “The fail of the project was the interface, it was not at all easy to use, there were plenty of

separate features and we cannot do everything in the interface.” This case is a classic problem in the IT

projects, and moreover in the Data Project, where there is a basic complexity which is find a relevant data, if

the tools are not able to help you to find the information more rapidly, your lost two more times. “The interface

is the place where everything is done and at the launch time, the marketers had decided not to use.”

2) “Machine learning” and “artificial intelligence”

The machine learning is the part that is the most complicated to integrate in marketing departments. All

marketers are conscious that these technologies will impact their business “Artificial intelligence has had a

bad press in the 90s but there are plenty of things that shows artificial intelligence in our everyday life has

arrived.” We are just at the beginning of the projects focused on this topic, but something is sure, that’s a hot

topic for every Chief Marketing Officer, some industries are very impacted like in the case of Orange where

51 Best Places to Trick or Treat: A Power Map for Excel Tour [ http://youtu.be/bIgM-N4wbnM ]

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they gather very important quantity of data “We set up a big data platform that allows better access to data

with Data Lab (in sandbox mode) configured with specific permission levels”.

All are currently testing things about it, but it is not already in the public sphere. In fact there is a very interesting

cross features between characteristics: 1) Natural UI and improvement of user experience with technologies.

Sebastien Imbert says that it’s a work topic between R&D and marketing, for example in the video games

industry, he explained that “Modeling a personal avatar with the players behaviors and providing a computer

competitor that will anticipate your behaviors. It gives us marketing ideas scenarios”, robotic and IA can now

be engage to “replace creative agencies”, it was tested more than several times on a panel of customers. The

concept was showcased at SXSW conference in 201152, and the approach of retargeting and automation

advertising goes in this direction. 2) Combining to automatic interactions and intelligence of this interaction.

3) The diminution of human intervention in the process. All these three things can bring Big Data technology

and usages in another level.

Everywhere you can find frictions between partners or internally could be transformed by the Data Science

approach. But for Orange and Microsoft there is a limit “Big Data projects focus on the customer and the

customer's data. It is therefore essential to organize internal actions with a transverse approach. The Opt-in

and the respect of the privacy are always in the specifications”. The Big Data and all its consequences are now

the new exploration bridge between Marketing, business opportunities, research and development.

The data driven convergence rupture test & learn and ecosystem

4.5 Towards the marketing strategy Test & learn

The new paradigm in marketing intuition and the Big Data driven marketing is the Test and Learn

methodology. The test & learn opportunity has mostly been developed with the increase of internet usages.

52 Can creativity come from a robot? – [ http://panelpicker.sxsw.com/vote/38868 ]

Rupture Ecosystem

Test &

learn

DATA

DRIVEN

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During my interviews it comes out as the best practice that have been the most repeated. For Orange, it’s a

daily practice “Big Data should allow marketer to better target our prospects while measuring the solicitation

messages”. The Big Data “test and learn” also gives marketers an opportunity “to better target our prospects

while measuring the solicitation messages”. Orange is very attentive about customer expectations, on respect

for their private lives” for example “we operate no cross data (CRM / surfing) without the explicit consent of

the subscriber”.

For 55 Agency, specialized on the topic of media targeting, test and learn is becoming a standard process. “To

be successful in activating a data (eg. Data driven marketing) project, it is best to proceed with 'Test & Learn',

if possible, an AB Testing”.

1) The test and learn contribute to increase the targeting opportunities. It increases the tactical

decision opportunities very quickly.

2) Then, with an AB Testing 'well calibrated, we measure the difference between the solution and the

former Data Driven solution you want to challenge. By analyzing in detail, we identify what worked

better than expected and what did not react in order to identify areas for improvement (new

variables, the best prediction model / optimization, etc ...)”

We test, one individual proposition, then each test is tracked separately, we gather them and we keep those

with a positive results. Finally, it is repeated until satisfying results.

3) It starts with simple models initially with very little assumptions and variables. Many clients of Microsoft

are focused on the exploratory vision, they will try things and are not in the “reversal of risk”. They are wrong,

and it is important that some tracks to not give anything where innovation means being wrong.

Where the error is valued, it is important to know that a track gives nothing. Two reasons explain this approach,

1) Moving forward quickly to produce results, 2) It is not expensive if they are not wrong. In opposition there

are some limits of the Test & learn approach, for example explained by Microsoft “The Heisenberg uncertainty

principle (we cannot measure everything), the act of observing affects the phenomenon you observe.”, and the

fact that to test more and being too much precise can give the opposite results “If you're too sharp on the

analysis, that will disrupt, you will made offers that will hinder and become too specific. Big Data is the

attention economy, and there it sometimes goes too far, the most relevant message at a time T”.

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4.6 Data science perspective integration:

Finally the Data Science business seems to be difficult to progress in companies. For the Data Scientist from

55 (former Google employee): The "Data Scientist" is “the crossover of research, engineering and

consulting”. The researcher proposes responses with a complex model that fits. An engineer develops

prototypes and is capable of taking into account operational constraints. Finally, Consultant is able to conduct

sophisticated business analysis and to adapt quickly to the client context. The Data Science is a methodology

of these three parts. In many of my interviews, we have the sentiment that it is really difficult to define the

Data Science. Below this difficulties, we see moreover a not really good comprehension of what are the role

of Data Science, but the difficulty to project how it is integrated in departments and what business competences

are involved in it. There is two typologies of project identified by the agency.

“Data Exploration: We explore granular customer data to answer specific and detailed questions, or to identify

areas for improvement and assess their potential explored.

- "Data Activation" We develop operational tools that make decisions based on the amount of data collected

by our customers, automatically and to optimize performance. ”

Big Data represents a multi-scopes consideration in companies (IT storage perception, cost, virtualization) -

marketing as an opportunity - Mining information from the CRM – and business opportunities oil seem to be

centralized in the term “Data Science”. As previously explained the “Data Science strategic assets” are not

only the capacity to crawl or mining data, because in this case there are a lot of expectations around that, as

qualified by Microsoft, we are more in the business case of “complex query” rather than in the real Data

Science opportunity.

As we saw, the classifications and definitions are progressively being understood by marketing departments,

we have an increase of competences around the big data topic, but this have to be nuanced. As we explored

marketing crossing the Data Science, there is an important confusion and mix between all the possibilities of

Big Data. In fact, the best opportunities qualified by the researchers don’t seem to be really translated at a

Marketing level. This fact creates some ambiguous situations.

- Ambitions versus capabilities:

(You communicate that you’re going to lead a transverse program to change all the marketing

strategies, you engage the investments, and you have disillusion…)

- Lack of maturity on global opportunity for companies (What is surprising is that the impact on the

organization are quantified, we defined a strategic role responsible of the transformation through Data,

but we tend to externalize most of Big Data projects…)

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- A strong expectation and a main focus on over-segmentations (We want new scenarios, but we are

focused on request of segmentation or traditional marketing challenge…)

- Marketing Managers are like “captain in the storm” (The managers give horizon on two years on

project…)

- Arbitrage (cost, experiences value, capacity of deployment) vs Explore business scenarios

(We want marketing teams to be able to quantify business scenarios that they have never launched…)

- A lack of maturity about Data Science (Difficulty to define the scenarios and the implication in the

organization)

From intuitive marketing to data driven, the impact on a personal model (As we explained is moreover more

important than we could of have expected). To conclude, my interview analysis was a very constructive

experience to complete this thesis. It added a field experience feedbacks and global view of the French market.

5 Discussions

5.1 Personal contribution to the topic:

The topic of big data is under the spotlights in the Media. Sometimes it is compared to the new oil, there are

many studies and publications which build castle in the air. You have a lot of contributions on the topics of

how to manage the information deluge, how to exploit the social media with Big Data etc. There also are many

publications by Information Technologies companies who tried to predict, adopt and say what the best practices

to adopt Big Data Strategies are. A plethora of information exists on the internet about this topic, but not

enough are focused on the impact that it could have for specific industries and for jobs. The perspective is often

axed on the classical e-business and the business-to-consumer industry.

My contribution enrolls in this observation. My objective was to go through this scope, I centered the discussion

more on a managerial and organizational approach about the impact on the work of marketers. Effectively, the

digital marketing team has adopted the decisional tools and the capacity to test and learn very quickly. But

there are many questions in suspense. The organizations have difficulties to transfer the marketing investments

from classical channels to the digital. Sometimes because they have no proof of the potential results. And at

the opposite, executives feel that the consumer behavior is quickly changing. The marketing transformation

will continue and the change of paradigm of media transformation has cornered companies in a way that they

canalized their efforts in the capacity of learnings about their clients.

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I’ve decide to center the discussion of this study on two emerging subjects, firstly the changing skills of

marketer. As I demonstrated, there are few studies that try to define the new skills involved in marketer’s daily

work. We often talk about the integration of new jobs, such as Data Scientist or Data Miners. I’ve also focused

on their main objectives which are now more oriented to the customer-centric objectives, mainly the marketing

activations, which has switched from products centric strategy. Secondly, I’ve tried to understand how the Data

Science implementation approach is currently formalized in companies. I’ve also reviewed what is the reality

of Big Data projects according to the professionals I’ve met; and I have categorized the different

characterizations of a Big Data project I’ve draw the consequences of Data Driven marketing process on the

marketing team, positive as negative, the marketer should more and more being balanced between the intuition

marketing and his capacity to use Decisional Data to improve his personal choice in the marketing strategies.

This study has also contributed to show the results of these projects applied on a marketing department. I’ve

observed the consequences of Data Driven marketing on organization, on the management of the competences.

5.2 Management implication results:

On a management consequences side, my contributions explore the repercussions of Big Data in the

relationship between departments in firms. Effectively, the leadership on the project is less given to the IT

department, there are more and more projects launched without the implication of the technical team. The

reasons are various, but it seems that marketing teams are not accustomed to collaborate with the IT, they prefer

to integrate marketing agencies or marketing specialist partners. Sometimes they directly connect with media

platform, such as new competitors like Facebook or Google.

Management of the teams:

It must be regarded that the historical partner consumer of IT inside the company was the financial department.

They were consumers of many Business Insights, but they had the basic skills competences to explore the data.

As we explained marketers are not accustomed to use complex data, they don’t have habits with this typology

of tools. That’s why we explained this problematic of adoption. But the approximation seems to operate thanks

to the topic of Data management. Especially with the emergence of new topics like the utilization of data

available in all the departments to create a Data Management Platform for example.

External implications:

An important point is that the external stakeholder are extremely present on the topic of Big Data. As we

explained the competences are rare and highly sought, that is implied to call for new approach and to involve

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new partners with new processes. Indeed, most of the competitive companies on this topic are start-ups (they

have slow cost, adaptability, and can be in rupture with the classical view of editors or advisory…).

As we explained, externalizing is a risky investment, in a world where data has become a strategic asset to

engage and target customers. Companies have to keep their strategic advantages which is in the customer

comprehension, and in the possibilities of relationship.

On the other side there is the stress of always broaden the data ecosystem of its marketing department.

Consequently, a fragile asset became the strategies to engage the customers marketing by digital channels.

Marketing are becoming dependent of these tools provided by IT editors, or internet actors, but their data are

also fathom by the same actors, and the consequences are maybe to weaken their relationship with customers.

In fact, the IT editors and internet companies can re-use the data to propose their services to other industries.

For example in other Industries, they re-used the pattern to propose client typologies. If you don’t control your

data, some company could just use it to create monetization on it. Moreover if companies lost the

comprehension and the insights of the customers, they will lost the leadership and their value on the market.

5.3 Limits of my research:

The choice of this topic highlights the complexity of responding to a trending topic problematic. My conviction

is that a field of research was not so easy to define and to deploy. As I explained there are many path that can

be explored. It can be complicated to choose the front door in the theory topic of Big Data. I’ve tried to focus

on some of them, by crossing researches and studies: Marketing, Technology, Psychology works, Management

and organization. That’s was not that easy to play with this four sciences. One of them that would have thorough

my problematic is maybe the study conception of the marketer psychology.

An obvious limit lays in the research part, indeed, I would have better structure this part with more interviews

and a better segmentation. For example I would have interviewed on a more centered framework within

different formats. I would have focused more on a principal company, and tried a quantitative methodology.

When I talked about that, I focused mainly on my results that are not in the same industry sectors. The

consequences is that you may not have the same granularity of Business & Marketing scenarios. My data are

crossing many point of views from different industries which is in my opinion a bias in my results. I would

have conducted this study on a more targeted marketing department with a critic size sample of a same

company. It could have been a better resource: more contextual and more pragmatic. There is also a focus that

I’ve not taken into account, which is the part of decisional tool in the company. Decisional tools are a big part

of the IT investment and it is considered as a strategic use of Data.

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A very interesting topic that I found during my research, and that I want to continue to explore is the topic of

Data Driven Business Model, which is also very pragmatic in a study of the influential features of a company’s

strategies, and how to drive all these strategic assets to create a business model.

6 Conclusion

The transformation in Marketing Department has just started, an important impact has already been seen in the

digital marketing, and all classic marketing departments should follow this transformation.

During this thesis, I demonstrated that the marketer role is central in the Business of a company, but that there

are many silos that constrain them. They have no choice to be influenced by the culture inside the company

they work for. If the culture is an important factor of strategic management in organization, this is also what

inspires the way of their daily work. The objectives and the hierarchic factors are predominant in the

orientations that marketers take today. The marketing have to be driven by the psychological good intuition,

marketers must have empathy to their customers and be able to explore a lot of customers’ data information.

By 2015, disruption for the marketing model would already have changed, at that time that include fast growing

evolution of tools and consumers’ behavior (from web to mobile, to connected devices). The marketers will

have to be closer to business and will need to really understand the product that they promote, and the customer

who use it, whether it is in marketing activation or in strategic planning of the company. The Marketing

department must adapted as fast as possible to the new behaviors of their clients. For that, the strategic

marketing analysis and tracking are one of the most important assets to be closer to this behavior

transformation.

The feedbacks of a management competences asset:

The management of competences seems to be the most complicated part of human resources of marketing,

with the rise of new jobs on the market, and the mediatization of successful use cases, companies and managers

give the feeling that they want to engage in the opportunity of Big Data. Most of the time, they want to engage

rapidly into this transformation, but what we see is that the structure lacks of profiles corresponding to the

demand. They are still too much focused on recruiting a specific profile that will provide every solution.

As explained in my research, companies seem to not consider the basic objectives of marketers enough. The

disruption will come from the fact that they want to focus on all the business opportunities scenarios and the

opportunity of Big Data in only one department or sometimes only one person. This ambition is sometimes

real, they create a specific role like Chief Data Officer which comes from the strategic business side, but the

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reality shows that it is possible to have this job title and to not access to the board, and impact in digital

transformation. After leading my field studies, I think that there is a real opportunity of rupture by giving the

capabilities to marketers to lead this transformation. They have to integrate an important part of changes in

the management, but if they deliver the good scenarios, and if they have capabilities by solution, the adoption

will be successful.

Towards an open organization to better integrate Big Data:

The organization has a strategic role in the integration of big data strategies, as we have seen it is mandatory

to adapt the skills of each employee but it’s also important to adapt your organization chart. The rupture may

be to integrate technical competences directly, with, for example, the competences of a Data Miner with a team

of developer directly with the strategic marketing. The relationship between partners have to be experimented,

we have the example of failed experiments. We also have to consider how a team have to be built on this topic.

Depending on the scope of integrations you could have a transversal business unit on this topic to address more

than just the operational business marketing. The factors of rupture lay in this capacity of comprehension where

the limits of the Data opportunity meet. That’s why we detailed the importance of building a strategic project

with a team that is well-structured and an integration of the marketing teams.

The methodologies support by intuitive and performant technologies:

The modification of marketing through the Big Data shows that marketers explore new methodologies to target

and test marketing campaigns. The culture of “test & learn” corresponds to the bridge between the intuitive

marketing and the Data Driven Decisional. Beside this, it’s a decrease of the cost for marketing campaigns

(with example of advertising market place) and a possibility to confirm the marketer intuition.

Tools became predominant in the capacity of advertising and CRM campaign, but it’s not the only part of the

relationship with customers. We saw different new approaches on marketing with the VRM and this capacity

to change the customer relationship and the data privacy, to increase the value of customer. Nothing tells us

that in the next few years, the marketing relationship will be not driven by the company, but by the decision of

client. This finding is developing in the mind of companies and managers. They have to prepare to an unstable

environment, where a tool or a relationship between media channels will not increase their revenue and their

proximity with clients. These may be the capacity of marketers, an empathy that an algorithm could never

have.

Companies have been collecting Data on customers for decades, with the digital, the amount of customers to

give away information about them and the number of ways to monetize these information are increasing very

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quickly. This has been great for forward-thinking companies like amazon, or Netflix, but continues to

drastically change traditional businesses marketing teams, organization, collection of data and analysis. They

all take thoughts and resources, and must be done more efficiently than the competition. The disrupt of Big

Data came from this new battlefield for companies, they have to create the new space of competition where

team organization, consumer behavior, skills and use cases are yet to build, the competitiveness is becoming

the capacities to manage this strategic transformations.

7 Bibliography

Books:

T.L. Friedman, 2005, The World Is Flat: A Brief History of the Twenty-First Century(London: Allen Lane,

2005) Publisher Farrar

K. Ohmae, “The Next Global Stage: The Challenges and Opportunities in Our Borderless World” (Upper

Saddle River, New Jersey: Wharton School Publishing, 2005).

P. Kotler, G. Amstrong,Saunders, J. V. And Wong, , 1999, Principles of Marketing 2nd European Publisher.

Prentice Hall

Lapan, S., Quartaroli, M. and Riemer, F. 2012, Qualitative research. An introduction to methods and designs,

Jossey-Bass, Publisher San Francisco University

V. Mayer-Schonberger, K. Cukier 2013, Big Data: A Revolution That Will Transform How We Live, Work,

and Think, Publisher John Murray

N. Silver, 2012 The Signal and the Noise: Why So Many Predictions Fail - But Some Don't, Publisher Penguin

E. Siegel, 2013 Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Publisher Wiley

I. Ayres, 2007 Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart, Publisher Bantam

F. Provost, T. Fawcett, 2013, Data Science for Business: What you need to know about data mining and data-

analytic thinking, Publisher O'Reilly Media

J. W. Foreman 2013 Data Smart: Using Data Science to Transform Information into Insight, Publisher Wiley

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M. Jeffrey, 2010 Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know, Publisher

Wiley

Articles:

De Meyer, A., Dutta S., Srivastava, S (2002): The Bright Stuff, Financial Times/Prentice Hall

Amoni A, Biren B, Dutta S (2001): Business Transformation on the Internet 2000, INSEAD, available from

http://knowledge.insead.fr/docs/2001-22.pdf

E. Brynjolfsson (2011) Strength in Numbers: How Does Data-Driven Decision making Affect Firm

Performance? - MIT - Sloan School of Management

Y. Wind, V. Mahajan and R.E. Gunther, (2002) Convergence Marketing: Strategies for Reaching the New

Hybrid Consumer (Upper Saddle River, New Jersey: Financial Times/Prentice Hall

J.P. Workman, Christian Homburg, Kjell Gruner (1998) Marketing Organization: An integrative framework

of Dimensions and Determinants - – Journal of marketing Vol. 62

Lisa A. Burke, Monica K. Miller, (1999) Taking the mystery out of intuitive decision making - - Academy al

Management Executive

Ruekert, Robert W.; Walker Jr., Orville C.; Roering, Kenneth J. (1985) The Organization of Marketing

Activities: A Contingency Theory of Structure and Performance – Journal of marketing

Y. Wind (2008) A Plan to Invent the Marketing We Need Today (2008) - MIT Sloan Management Review

Source: MIT Sloan Management Review

A. Payne & P. Frow A Strategic Framework for Customer Relationship Management (1995) – Journal of

marketing

T.H. Davenport and D.J. Patil Data Scientist: The Sexiest Job of the 21st Century (2012) – Harvard Business

Review

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Webography:

The Lines Between Software and Hardware Continue to Blur – The Wall Street Journal – Dec. 2012 [

http://online.wsj.com/news/articles/SB10001424127887324677204578188073738910956 ]

Why Software Is Eating The World – Marc Andreessen – Wall Street Journal – Aug. 2011 [

http://online.wsj.com/news/articles/SB10001424053111903480904576512250915629460 ]

The Future of Decision Making: Less Intuition, More Evidence – Harvard busness Review – A. McAfee - Jan. 2010 [

http://blogs.hbr.org/2010/01/the-future-of-decision-making/ ]

Big Data's Management Revolution - E. Brynjolfsson and A. McAfee [ http://blogs.hbr.org/2012/09/big-datas-

management-revolutio/ ]

Big Data News Roundup: Correlation vs. Causation – Forbes – 2013 [

http://www.forbes.com/sites/gilpress/2013/04/19/big-data-news-roundup-correlation-vs-causation/ ]

Analytics in Action: Breakthroughs and Barriers on the Journey to ROI - Accenture – 2013

[ http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Analytics-In-Action-Survey.pdf ]

Ethics in a data driven world – [ http://techcrunch.com/2014/06/29/ethics-in-a-data-driven-world/ ]

It Takes Teams to Solve the Data Scientist Shortage – The Wall Street Journal – 2014 [

http://blogs.wsj.com/cio/2014/02/14/it-takes-teams-to-solve-the-data-scientist-shortage/ ]

Why CMO Leadership Is Essential To Data-Driven Marketing – Forbes – 2014 [

http://www.forbes.com/sites/onmarketing/2014/04/29/why-cmo-leadership-is-essential-to-data-driven-marketing/ ]

Big Data Analytics a Big Benefit For marketing Departments – CIO Review – 2013 [

http://www.cio.com/article/2392067/data-management/big-data-analytics-a-big-benefit-for-marketing-departments.html ]

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8 Appendix

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81

The Gartner Big Data Hype Circle - 2014

Capgemini Big Data Technology Vendor for Marketing - 2014

[ http://www.capgemini.com/blog/capping-it-off/2012/09/big-data-vendors-and-technologies-the-list ]

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Figure 1: The research question tension

How does Big Data disrupt marketing: the modification of a marketer’s job

(1) (2)

Understand the impact Manage marketing competencies in data centric context

of a complex technology opportunity

Market trend Business daily impact

Automation reorganization

Buzzword Program management

Change management Communication and training

Competences Knowledge management

Technology ROI

Technical functionalities Skills switch

Data driven centric Intuition driven

Ambassador Manager

Automation & Learning experiences

Practices & business cases

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9 Interviews

Interview: Damien Cudel – Chef de Marché - Microsoft France

NS : Pouvez-vous vous présenter ? Quels est votre profil ? Quel est votre rôle au sein de Microsoft ?

DC : Donc je suis Damien Cudel chef de marché data insights Microsoft. Concernant mon parcours j’ai fait

du développement, j’ai fait beaucoup de projet, j’ai fait aussi de la recherche autour de la fusion de la vidéo,

des réseaux, des télécoms. J’étais à l’origine très technique au début de la VOD. Mon parcours a commencé

j’étais chez TF1, j’étais en charge des nouveaux formats, de la digitalisation des contenus. J’ai été manager de

consultants. En termes de formation, je suis ingénieur, j’ai une culture très orienté ingénieur, je suis passionné

par les modèles mathématiques.

J’ai rejoints l’équipe marketing de Microsoft, car j’ai eu une expérience qui été lié à répondre à un besoin d’un

client quand j’étais chez TF1, ensuite j’ai œuvré à un besoin de plusieurs clients quand j’étais chez MCS53, et

ensuite j’œuvre beaucoup plus en amont en étant au marketing.

Je m’occupe de toutes les offres et solutions de bases de données, et aussi celles qui permettent de faire de la

data visualisation. Concrètement d’un point de vue produit SQL server/ power BI dans office 365 et toutes les

offres d’Hadoop dans Azure. Je suis chef de marché j’ai la responsabilité P&L, donc business et chiffre

d’affaire et marketing en France

NS : Comment se positionne Microsoft sur le sujet du Big Data ?

DC : Comme bien souvent dans l'approche retenue par Microsoft, l'objectif est de démocratiser l’accès aux

technologies et au solution qui permette de tirer avantage des données et quelque part développer la culture de

la données pour tirer l’avantage de l’accès et de l’utilisation de ses données

- Ambition

- Etat d’esprit

- Plateforme

Quand on parle de Big Data, on fait une erreur par introduire les 3, 4, 5, 6V que l’on entend parler à longueur

de temps. C’est une erreur car c’est un prisme technique à la question. Le Big Data c’est avant tout une

53 Microsoft Consulting Services

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ambition métier. L’ambition c’est métiers d’utiliser toutes les données de l’entreprise et aussi en externe.

C’est une ambition, donc c’est choisie et non pas subie. La presse parle trop de la vague de données, qui va

submerger etc...

Avec le fameux exemple de cette banque qui vient nous voir avec sa problématique Big Data. On nous parle

du traitement de beaucoup de données 5go, on compare avec les use cases de marché en montrant des

similarités, avec un concurrent de Singapour qui à margé l’analyse de son centre d’appel, la tonalité de

l’échange, sur l’analyse vocal de l’appel on peut comprendre le comportement, le « churn » du client. Et

là effectivement on a une volumétrie et on explique via une complexité algorithmique, là on réussit à associer

au projet, on est plus dans un projet Big Data. On réussit à corréler de la volumétrie et une opportunité business

très importante. Donc le client a choisi, donc quand je dis ambition, c’est donc un choix.

NS : Quels sont les grands types de projets métiers et marketing sur le sujet du Big Data ?

DC : C’est un état d’esprit du projet, si je devis caricaturer des projets 2 grands types. Le premier c’est

l’industrialisation de la production d’indicateurs, par exemple du « reporting » un peu avancé, certes

avec un peu plus avancé, mais cela reste de la BI avancé. Ce sont souvent des projets piloté par l’IT, pour

lequel le « ROI » est souvent connu et peut être estimé, je m’appelle GRTGAZ, je dois être capable de chiffrer

mes pertes en gaz sur mon réseau.

1. Piloter par l’IT

2. interrogations sur les moyens à mettre en œuvre (montée en charge)

3. Approche très ingénieurs (Bottom up et Top down)

On se retrouve dans les fonctions régaliennes de l’IT, on est souvent dans une aversion au risque. Je suis l’IT

je suis garant du fonctionnement du fonctionnement de l’entreprise, on est sur la montée en charge, je m’appuis

sur des choses connus je ne prends pas trop de risque.

A l’autre extrémité du spectre, on voit de plus en plus d’entreprises, notamment le marketing, les responsable

innovations, donc plutôt les métiers qui veulent se lancer dans l’expérimentation Big Data en voulant

lancer des Data-Lab. Pour mener une expérimentation travaille avec une boite externe pour créer une

expérimentation et ferrer de la valeur.

1. Les métiers sont intéressés et voit des choses à faire

2. Ils ont les budgets pour lancer ses projets – et de plus en plus

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3. Ils estiment ne pas avoir le répondant de la part de l’IT ou la possibilité d’avoir une réponse < et bien

souvent pour eux BI et Big Data > très différents dans leur esprits = Doit faire appel à des nouvelles

recettes pas aux recettes connues et existantes.

Lorsque l’on lance une expérimentation de ce type on est plus dans le registre du « core IT » mais dans

le « shadow IT », ce sont les métiers, ce qui les intéresse c’est la production rapide de résultat plutôt que les

moyens on est dans le « test & learn ». Ils sont dans une démarche plus scientifique qu’ingénieur. Ils sont dans

l’exploratoire, ils vont essayer des choses, ils ne sont pas dans l’inversion du risque. Ils sont dans l’erreur, c’est

important que certaines pistes ne donnent rien où innover c’est se tromper. Où l’erreur à de la valeur, c’est

important de savoir qu’une piste donne rien.

1. Aller vite pour produire des résultats

2. Pas couteux si ils ne sont pas tromper

Tout l’enjeu c’est de réconcilier ses deux mondes. Puisque lorsqu’un métier trouve un business case, dans

lequel l’analyse des données « Big Data » fait sens, la question que l’on doit se poser c’est l’étape suivante,

à laquelle ils ne pensent pas, c’est comment je réindustrialise, et c’est là qu’un éditeur comme Microsoft se

positionne.

Nous on a une plateforme qui permet aux IT d’aider les métiers à réussir leurs expérimentations Big Data. En

leur fournissant des plateformes qui permettent de provisionner des ressources, d’avoir des ressources et de

manière peu onéreuses, il s’agit typiquement l’avantage du cloud. Lorsque l’on fait de la « Data Science » on

est confronté à des sources de données divers et variés et plein de technique pour les manipuler PIG, EYE,

SQL et j’en passe des meilleurs. Nous on fournit une couche d’abstraction, avec un seul langage on peut

interroger tous les back ends. On peut gagner du temps dans l’étape d’agrégation et pour faire tourner les

modèles. Vous avez besoin de performance, pour pas faire tourner vos modèles c’est là qu’avec le cloud, et les

solutions d’in-memory que l’on propose dans nos modèles on les exécute beaucoup plus vite. Et enfin, lorsque

vous avez tiré les avantages de votre expérimentation, les technos utilisé dans le POC seront les même que

utilisé pour la réindustrialiser l’IT.

Par ailleurs dans l’étape de la Data Science, je n’ai pas mentionné pour exécuter des modèles de façon

performante, il y a un outil qui est la data visualisation, parce que à cette étape il n’y a pas meilleur cerveau

humain pour identifier des corrélations et orienter les modèles de Data Science qui sont exécutés, c’est là qu’on

accélère cette étape en permettant de tirer facilement des Insights.

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Et là, notre offre est complète, elle va du backend au front end, du client à l’utilisateur final. Quand je dis que

l’on est une offre unique, mais la combinaison d’On Premise, Cloud,

Le mot Big Data en soit, si on décide d’en faire

Le mot Big Data n’est pas important

L’ambition ce n’est pas Big Data, c’est réduire votre churn, segmenté vos clients, comprendre le graph de

relation de vos clients pour déterminer le pourcentage d’achat d’un produit, lorsque l’on regarde le sujet Big

Data on a 3 sujets. Lorsque l’on regarde le sujet Big Data on se ramène à deux sujets + 1.

La connaissance client (avec 5 catégories)

La connaissance et l’amélioration des processus Métiers/Industriels

Monétisation de mes données voir de mes modèles

L’exemple c’est l’entreprise Orange qui met à disposition des données anonymisées à des offices de tourismes.

Comme ça ils mettent à disposition quelles informations ils vont voir à quelle heure. Ils aident à construire

l’offre par un tiers à conditionner son offre etc…

Sur un sujet comme ça, on se positionne plutôt en market place // en provider de données // consommateurs et

on fournit les modèles d’apprentissage statistique. Je pourrais facturer l’ouverture du modèle. Si je suis par

exemple un acteur spécialiste de la manufacture de voiture

Dans les cas extrême en Angleterre certains opérateurs téléphoniques ce sont associés, en mettant à disposition

leurs données anonymisées à un tiers de confiance. Ce tiers de confiance a accès à l’intégralité des jeux de

données des utilisateurs qui souscrivent au service et il fournit l’analytique sur ses données. L’intérêt c’est que

l’opérateur peut avoir une vue plus global de son marché, et pas une donnée biaisé de son propre marché. Grâce

à ça Il peut avoir des insights plus pertinents de son marché et celui des autres. Dans le pot commun il y a une

vision plus proche de la réalité. Encore une fois pas forcément exact, elle ne reflète que les abonnées, tout le

monde n’est pas abonné elle n’est pas forcément exact. Au lieu d’avoir une part du gâteau pour faire mon

analyse je vais avoir.

Il y a pas mal de petits éditeurs qui font ça sur des marchés spécialisés, par exemple dans le Retail où ils

compilent des informations issus de plusieurs retailers.

NS : Qu’en est-il de l’automatisation du marketing ?

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DC : Pour moi c’est antagoniste, entre l’automatisation et le marketing. Automatisation du marketing et du

commerce, les fondements du commerce c’est la relation. Avec l’avènement du Big Data et pour la première

pour les responsables marketing on a l’impression qu’ils vont pouvoir quantifier leur discipline. On se

retrouve dans la situation excessive qui est de savoir avant même que les gens le sache ce qu’ils vont

vouloir, je range mes différents clients selon une segmentation dans des cases. Je n’ai plus besoin de te

poser des questions ni d’interagir avec toi car je sais ce que tu vas faire.

Il n’y a pas tant que ça d’offre de segmentation sur le marché. Le problème c’est que souvent nos clients,

se disent, je vais pouvoir faire de la segmentation plus fine. Mais dans ce cas si l’on fait un segment c’est

que l’on a une proposition différencié pour le segment et des histoires différentes. Alors dans ce cas on

peut le faire, mais sera-t-il possible de l’exploiter. Le scénario type sera. Pousser à l’extrême l’expérience du

Big Data fait l’inverse de ce pourquoi il peut être employé.

Le use case souvent donnée c’est le coupon en mobilité,

L’enjeu n’est pas selon moi sur le Big Data côté enjeux de stockage. Les côtés du stockage, tout le monde sait

plutôt faire, c’est peut-être pas Big Data qui restera mais ce sera peut-être plus le Machine Learning.

L’intelligence artificielle a eu mauvaise presse dans les années 90, y’a plein de chose qui révèle de

l’intelligence artificielle qui sont dans notre quotidien, dès lors que tout est plus magique sa résonne moins

chez les gens. Dès que c’est, expliqué ce n’est plus magique et cela ne correspond plus à l’intelligence

artificielle. Dès que l’on a la capacité autour de ses algorithmes, de nombreux modèles statistiques, sont par

ailleurs plus efficaces. Mais tous les algorithmes non pas besoin de données pour progresser. C’est ce qui

résonne dans l’esprit…

En fait l’enjeu n’est pas tant Big Data où complexe data, l’enjeu c’est plutôt « complexe query », j’ai un

truc qui qui relève de l’apprentissage statistique, là pour moi on est dans l’enjeu du Big Data.

Si le Big Data existe, c’est parce que l’on parle qu’il y a plus de données, les réseaux sociaux, mais tous le

business traditionnels génèrent plus de données, je prélevais une fois par semaine maintenant c’est toute les

15min c’est finalement du business classique qui génère beaucoup plus de données.

1. Plus de data (pas que l’innovation)

2. Baisse des coups

3. Des algorithmes (machine Learning)

4. La preuve que cela marche (si Google, Facebook, eBay existent c’est parce qu’ils exploitent la

data et qu’ils marchent)

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NS : Quels sont les succès clefs d’un projet type de Big Data ? Avez-vous mis en place des méthodologies

pour accélère le succès d’un projet ?

DC : Alors concernant les projets de Data science (donc modélisation mathématique), mais pas forcément

mathématique. Par exemple l’INRIA, qui a fait un témoignage avec nous, fait de l’analyse de cerveau (en

tranche) et croise ça avec des données génomiques. Par exemple l’objectif est de déterminé si une pathologie

du cerveau est typique à un gène dans la population. En l’occurrence là c’est des données non structurés, mais

on parle de quelques terras. Là on est dans du Big Data mais cela résonne trop comme du volume alors que là

c’est plus la complexité des algorithmes à mettre en œuvre derrière. A un instant T, le Big Data est tous les

scénarios

Je pense que Big Data d’un point de vue technique c’est égal à un horizon, il y a 15ans Big Data signifiait d’un

horizon technologique pour 100go de données, on avait un problème de Big Data en termes d’espace.

Aujourd’hui Big Data à un instant T, c’est un ensemble de scénario que les technologies ne savent pas adresser

de façon efficace. Et par définition un horizon cela recul dans le temps.

Il s’avère que aujourd’hui Hadoop c’est très techniques, cela veut dire que les données deviendront

transparente, il y aura de nouveaux scénarios métiers.

Sur un projet de « Data Science » ou d’expérimentation il doit y avoir « core team ». Très souvent, on parle

directement du « Data Scientist », qui va sortir tel un prophète, omniscient le bon algorithme. La réalité est

différente, le format qui marche est un triptyque 3 rôles :

Mathématique (statisticien pour mettre en œuvre les algorithmes, comprendre que des modèles peuvent

être assez biaisé et que les données en entrées aussi)

Business (Rôle pivot avec double enjeux : Oriente les recherches du core team sur des scénarios

business et qui donne la valeur aux scénarios étudiés, et en retour c’est à lui qui a la responsabilité

d’interpréter ce que le data scientist aura extrait de modélisation pour l’expliquer à ses pairs et sa

direction et donc en quoi le modèle propose un avantage)

Ici la data visualisation peut permettre de passer les messages clefs.

Mais dans le cadre du modèle je détecte une marge d’erreur de 10% comment cela se traduit sur le

churn, ou sur la live time value au final. Ensuite c’est les rebonds qui vont permettre d’avoir un résultat

pertinent et ensuite le résultat vers les décideurs.

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Developper (dans beaucoup de cas lorsque l’on traite des données non structuré, il faut intégrer les

sources d’informations qui seront exploités, je n’accède pas à un fichier vidéo comme à une cellule

dans un fichier Excel)

Avec un second cercle (éventuellement) qui peut être le responsable infrastructure, la partie Legal (j’ai les

données personnelles, ai-je le droit de les gérer etc…). Lorsque l’on dépolit hadoop, ce n’est pas toujours

évident, c’est pour cela que l’infra n’a pas toujours un rôle essentiel. Cependant plus on va vers du Azure et du

hadoop moins on a besoin de l’infra IT. Car il suffit de provisionner très rapidement et tout est lancé au bout

de très peu de temps. Il y a un risque derrière ses méthodes et la place du Data Scientist. Un jeu d’apprentissage,

un jeu de validation et un jeu de test

La limite de quelqu’un d’expérimenté dans les modèles de mathématique et de mise en œuvre est la limite de

la causalité et corrélation. Parfois même dans des études sérieuses avec des gros biais. Corrélation n’est pas

causalité.

Faut-il savoir expliquer pour comprendre la cause, si une étude sérieuse est bien menée, si on sait qu’il y a

deux corrélations, c’est là qu’il faut avoir assez de données et les bonnes données, car si je fais les

échantillons sur des personnes non concernées. C’est là que le data Scientist joue un rôle.

Par rapport au marketing j’aime bien faire une analogie entre ce que le Big Data a offert au marketing et ce

que la physique à offert à la théorie des gaz. Juste un exemple aujourd’hui pour décrire un gaz on prend t°,

pression, je n’ai pas besoin d’avoir la position des gazs pour expliquer le comportement des gaz, la réponse est

non.

Reprenons le panel client : est-ce que j’ai l’idée excessive consiste à penser qu’avec le Big Data comprendre

ce que chaque individu fait.

De 1 c’est la probabilité d’occurrence de certains phénomènes

2 le phénomène observé est perturbée par la mesure

Le principe d’incertitude d’Heisenberg (on ne peut pas tout mesurer) le fait d’observer influe sur

le phénomène que tu observes. Si tu es trop pointue sur l’analyse cela va perturber, tu vas faire des offres

qui gênent, trop spécifiques. Le Big Data c’est l’économie de l’attention, et là on va parfois trop loin, le

message le plus pertinent à un instant T.

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Il faudrait passer à une économie de l’intention. Tu deviendrais dépositaire de tes données. Et cette économie

de l’intention où finalement le dépositaire des données devient le client et plus le marketing qui essaie à tout

prix d’avoir le maximum d’information pour pousser le meilleur message.

Je pense que le virage utopique sera celui du consommateur, tu es assis sur un trésor, les services marketing

sont assis aussi sur des trésors que sont tes données, si un jour le consommateur reprend la main sur ses datas

et que le politique prenne en main le sujet. Si c’est lui qui en tire avantage ce sera toute l’économie qui en tira

avantage.

Une fois que la transition est passée c’est un levier de croissance à trouver avec les échanges avec les clients.

Les entreprises je vois surtout les travers du système actuel. Il faut un tipping point pour ça. C’est trop

progressif comme processus.

Le problème aujourd’hui tu acceptes une page de conditions que ce soit sur Facebook Google et même

Microsoft, c’est unilatéral et personne ne lis rien, et n’a rien à dire… Cela pourrait être critique, par exemple,

voilà les termes que je comprends, voilà les termes que j’autorise. Et pourquoi pas un matching automatique.

Ce qui permettra d’enclencher, ce sera d’enclencher un prototype, pour la première fois on a crypté des

fonctions holomorphes. Il faut imaginer que tu as deux nombres, tu les cryptes chacun, tu ouvres les deux

nombres cryptés, j’effectue la somme de ses deux entités crypté et je t’envoie la somme, le cryptage

holomorphe permettra de faire ça. Je peux par exemple proposer ce type de recherche, soit l’application client

permettrait de faire. On pensait il y a 3 ans que cela n’existerait jamais, IBM l’a montré y’a 3ans. On est à l’état

de la recherche avancé. Mais si demain je peux avoir des données cryptés chez un tiers sans jamais possédé

mes données, je peux retirer la clef et qu’il n’est plus accès à mes datas.

NS : Comment s’articule le Big Data et ce qui découle des Data-Science sur les métiers du marketing ?

DC : Alors il n’y a pas exemple les données provisionnelles, toutes les données intrinsèques clients

- Données intrinsèques clients : Ses attributs par exemple démographie, pour par exemple analyser la

réception d’un produit en manipulant les variables clients et

- Données d’environnements/contextes : données de réagir de tels comportements

- Séquentiels, signaux pour la perception/ ce qu’il fait dans le temps : Données de comportements

d’achats. La next best actions à proposer. Pour donner lieu à de la recommandation – Mais ce n’est

pas que dans la recommandation, par exemple dans accidentologie, ils ont équipés une centaine de

voitures et analysé comment conducteur réagit, et ils ont analysé des temps de réactions, pour adapter

les signaux

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- L’analyse de son graph au sens relation – Les relations entre plusieurs personnes - comment les gens

s’influence.

Les algorithmes mis en œuvre sont très différents selon ses 4 cas

Si tu es dans le cas de par exemple, Givenchy, tu peux faire des parfums pour les machines à laver, tu as une

centaine d’ingrédients sur des savons et pas loin de 1000 pour un parfum de luxe, cela à un cout, tu veux savoir

combien d’ingrédient qu’il va falloir pour maximiser l’achat ou l’appétence.

Là se sont plutôt des données intrinsèques et d’apprentissage supervisé, j’aime/j’aime pas, ou alors tu vas te

dire, je veux réduire les couts sur un parfum, j’ai 600 composé, je vais identifier peut être 10 composés qui

font la valeur. Ils optimisent les produits pour réduire la marge, ils peuvent par conséquent orienter le travail

d’un nez.

Tout à l’heure on a parlé du séquence mining, cela intéresse un industriel comme EDF. Ce qui les intéresse,

c’est la courbe de consommation la courbe de charge de chacun de ses consommateur, pour identifier des

patterns récurent dont la fonction on consomme au fil du temps (Heure de retour à l’appartement).

C’est très intéressant et d’ailleurs Microsoft research peut faire du sequence mining, tout en préservant

l’anonymat, tu vas faire remonter des données agrégées au marketing. Ce que tu vas remonter aux gens ce

n’est pas les données détaillés, car le risque c’est d’avoir des informations trop précises sur les profils. Il faut

donner un degré d’agrégation qui garde les caractéristiques de de ce que je veux étudier tout en préservant le

caractère anonyme des données privées.

On est dans le « séquence mining » de la permission du coup on voit l’influence.

Là dans le graph on est plus dans les relations entre les utilisateurs et l’impact de l’imagine et des avis. L’intérêt

des graphs sociaux c’est que j’ai choisi ma relation, on est dans le marketing de la permission.

Bing connecté au graph Facebook, tu as les commentaires de tes amis dans tes résultats de recherche, on est

complétement dans ce registre-là. Attention aux fantasmes cependant ! Les limites de l’éthique.

On est la seule, je crois d’internet à effacer les données de recherche, les données de suivi de Bing sont effacés

au bout de 6 mois, tous les 6 mois ont supprimé les requêtes recherché sur Bing. On a été validé par la

commission européenne pour valider le stockage des données, on ne fouille pas les données d’office 365 de

leur mail pour faire de la publicité ciblé. La loi c’est aussi la CNIL, ce qui prévaut dans chaque pays. Notre

rôle est d’alerter, on n’est pas spécialiste de tout c’est pour ça que quelqu’un du legal dans un projet Big Data

c’est important.

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« Bétise naturel vs Inteligence artificiel » aujourd’hui beaucoup de business sont pilotés par l’intuition et ce

n’est pas toujours du bon sens. Le Big Data ne te coupe pas du terrain, une étude sur l’épidémiologie avait été

réalisé, ils sont arrivés à des conclusions fausses, alors qu’il suffisait qu’il aille d’aller sur le terrain,

Le Big Data est pour renforcer des constats et convictions ou hypothèses qui doivent renvoyer tôt ou tard à la

réalité.

NS : Comment Microsoft s’implique les équipes dans les missions spécifiques marketing ?

DC : Il y a un centre d’excellence européen de Data Scientist, avec un background et une expérience lié, qui

peuvent être détachés sur les projets. On a une grosse expertise dans le machine Learning chez Microsoft

Research, localement la stratégie, c’est se dire c’est de s’associer avec un fournisseur du secteur avec une

offre premium. Avec par exemple Quantmettry.

Interview : Romain Lalanne – Head of Open Data - SNCF

NS : Bonjour, pouvez-vous présenter ton rôle et ton rôle au sein de la SNCF ?

Romain Lalanne : Responsable du programme Open Data SNCF, Janvier 2013, après avoir participé au

premier « hackaton » organisé par la SNCF en juin 2012. Autour de la réutilisation de données Open Data.

C’est le premier élément qui a permis de mette en lien les gens qui avait lancé ce programme Open Data et de

futurs partenaires. (Développeurs, startups, spécialistes de données, Mobilité)

Ma mission dans ce cadre-là, elle a vraiment été de déterminer comment le projet Open Data est organisé en

plusieurs branches d’activités et filiales c’est un groupe complexes et multiples par le type d’activité, le type

de relation client. Par exemple, si tu prends des transports (Transilien et TER, c’est du transport conventionné

c’est-à-dire financé par la région Ile de France/Paris) donc la dimension est très institutionnel, alors que TGV

c’est une activité très concurrentiel par rapport à d’autres modes de transports la voiture… Dès le début de la

démarche on avait des branches d’activités en situation très concurrentiels, des branches d’activités

pouvaient aller très vite dans la démarche et d’autres pouvaient moins vite en tenant compte de leurs

enjeux business.

Je travaille concrètement avec plusieurs correspondant Open Data dans les branches d’activités, qui

vont être dans les branches d’activités, pour identifiés les données à ouvrir, et comment ils peuvent les

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mettre en phase avec leur branche d’activité, toute la partie représentation, de mettre en phase la

stratégie avec les opportunités de ce que l’on peut faire.

Enjeux Juridique – Cadrage – Datashacker - 10aine de personnes de leur temps.

NS : Comment se gère les projets Open Data entre les projets ?

RL : trois cas possibles d’utilisation de l’Open Data : Services détecté données intéressantes, on va les voir on

leur présente la démarche Open Data voilà ce que vous pouvez faites et on va vous accompagner sur des

démarches d’ouvertures, ce que l’on fait sur des données sociales et économiques/financières de l’entreprise.

La seconde approche un service ou direction vient voir pour l’accompagnement, donc moins d’évangélisation

de conviction à faire, puisque ils sont déjà sur les sujets.

Enfin le dernier cas, branche de direction extrêmement proactive dans la démarche, qui vient d’elle-même

identifier les données. Ce qui n’empêche pas qu’on les a déjà sensibilisées.

Il y a un effet de transformation interne qui marche très bien, puisqu’après elle est autonome sur la démarche

Open Data.

NS : Comment ont été initiés les projets quels ont été les facteurs clefs ?

RL : Le sujet a été lancé au cours de l’année 2011 avec une phase de réflexion et avec l’ouverture d’un portail

fin 2011 initié par le directeur de la communication de la SNCF Patrick Ropert (beaucoup de sujet sur la

transformation interne) très actif sur la transformation du Transilien, il y avait un vrai sujet qui était d’avoir

plus d’information voyageurs, et personnalisé les informations voyageurs.

Moi je travaille donc principalement sur des sujets Open Data, sur le sujet Big Data qui est un peu plus

émergent, je suis plus en sensibilisation en accompagnement, je ne suis pas sur la conduite de projets long

termes et qui sont sur des segments particuliers de l’activité SNCF. Cela prend en compte une phase macro

qu’uniquement une phase technique ou marketing. Il faut une phase de comm, de relation interne,

d’accompagnement pour ceux qui vont réutiliser ses données, c’est beaucoup plus large. Pour des raisons liés

à la réglementation, il n’y a pas aussi les compétences en interne pour le faire au global.

Les sujets Big data il s’agit plus de démarche par sujet :

En haut Personnalisation offre et services : Connaissances plus fines du client on va être en mesure

de lui proposer la bonne offre en cas de pluies, de perturbations etc…c’est l’enjeu de la personnalisation

que Google a compris avec des services comme Google now. Donc il faut une maitrise très forte de tes

données clients, donc cela fait appel aux technos aussi bien en termes de stockage que de méthode de

calcul. C’est aussi un sujet lié à la vie privée.

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Modélisation et optimisation des flux optimisation des transports. Opérer des trains en ile de France

c’est en Zone dense // le contexte fait que l’on ne peut plus ajouter d’infrastructure, on a atteint la limite.

Le modèle en 70 on adaptait la ville à la voiture – cela ne marche plus – A partir du moment où le ne

pas plus construire d’infrastructure le réseau. Il faut optimiser le réseau tel qu’il est avec une couche de

Hardware et une couche de service que l’on capte. On va être en mesure de leur donner une bonne

information pour optimiser le Traffic. Cela donne l’intelligence à la mobilité des clients. Prenons 3

exemples :

o Premiers sujets : les trains communicants, qui ont un certains nombres de technos dont les

capteurs infrarouges pour compter les entrées et les sorties, pour voir dans quel gare cela se

rempli voir les différentiels cela nous sort des comptages annuels, on a plus une information

lissé sur l’année, mais une information a un instant T. Si on peut savoir en temps réels on peut

savoir combien de bus affecter pour le cheminement les gens bloqués

o Sur la modélisation des flux, en 2012 suite au hackaton on a développé « tranquilien », on a eu

beaucoup de reprise presse, on croise des données historiques sortie et entrée des trains dans les

gares, il y a de la donnée crowd-sourced en l’occurrence, « ce train est rempli/pas rempli » et

de la donnée contextuelle qui va permettre d’affiner les algorithmes prédictifs. L’application te

dit quel train tu peux prendre sur quel itinéraire, faiblement moyennement plein très plein (via

un code couleur) et tu pourras différer ou non ton voyage si tu en as la possibilité pour voyager

confortablement, tu peux aussi affiner ça, par le biais d’un check-in permettant de crowd-sourcé

l’info, et l’algorithme rajoute des données contextuel (par exemple sur une tranche horaire

normal, le train sera plein si il y a un match de l’équipe de France) et là on inverse le mécanisme

et on devient intégrateur d’Open Data. Ce projet fait appel à de l’Open Data et de la co-

construction dans un cadre big data. Ce service on l’a par exemple sorti avec SNIPS.

o Modélisation des flux dans les gares, notamment avec les smartphones, qui ne sont d’ailleurs

pas forcément des voyageurs cela peut être les gens qui accompagnent les voyageurs, cela peut

être aussi les gens qui viennent faire du shopping. De plus en plus on est dans des services dans

les gares. Là on peut suivre les flux, sur le moyen terme, on peut imaginer comment réduire les

consommations énergétiques dans les gares, on peut imaginer régler l’importance de la

luminosité.

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Le troisième sujet est très techniques mais c’est le big data pour la maintenance d’infrastructure et la

capacité de faire une maintenance préventive en aujourd’hui il y a des trains qui sillonnent toutes les

voies de chemins de fers et qui captent les données sur la vibration des caténaires, sur la structure des

rails, on va surveiller avec des rayons infrarouges, on croise avec des données météorologiques et on

peut mieux cibler la maintenance que l’on fait sur le réseau, le premier on va améliorer la performance

industriel de ceux qui font la maintenance, si on cible mieux la maintenance on est en mesure d’avoir

un réseau beaucoup plus souple

NS : Comment se gère un projet Big Data ? Quelles sont les meilleurs pratiques et les méthodologies

mises en place ?

RL : La validation d’un projet se gère toujours par celui qui gère la donnée, si on veut ouvrir une donnée

sociale c’est la direction des ressources humaines qui va décider, si on veut ouvrir une donnée sur les horaires

ter, c’est la direction des ter qui va décider. On respecte les enjeux, les intérêts et les menaces de chacune des

directions.

Quand tu as des données tu sais comment elles sont produites comment elles sont enrichies et si elles ont un

intérêt pour un concurrent, en général, ils n’ont pas besoin de moins pour mettre des bâtons dans les roues de

l’Open Data.

Au début on s’était demandé, on avait écrits, on faisait du méta donnée du jeu de donnée. Quels personne

chargé du jeu de donnée, la fréquence de mise à jour, le format, téléchargeable via une API, des informations

sur le jeu de données.

Et on a également une question si le jeu de données peut représenter un risque du point de vue commercial,

concurrentiel. Un concurrent peut attaquer un marché.

Et une question était si mettre en public ce jeu de données avait un intérêt, à priori cette question n’avait aucune

raison d’exister. On ne pense pas à toutes les possibilités de réutilisations des données. Quand tu ouvres les

horaires de Transilien en format gtfs, on va pouvoir faire de la cartographie, en utilisant les horaires de trains

et ça personne n’y avait pensé, tout ça à partir d’horaires théoriques.

L’intérêt de la dataviz, c’est beau à voir, c’est intéressant, on ouvre un public assez large, la Data nous nous

intéresse, mais la data pour 95% des gens c’est un sujet chiant. Cela rajoute du design de l’interet, de la

beauté autour de la donnée. Les gens cela les sensibilise plus autour des enjeux essentiels autour de la donnée.

C’est quoi la réalité d’exploiter les transports dans la zone dense. C’est un outil de sensibilisation pour répondre

aux enjeux qui permette de mieux expliquer pourquoi cela marche et pourquoi cela ne marche pas sur un réseau

de transport.

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D’ailleurs cela peut être le cas d’autres projets et on peut travailler avec des gens pour enrichir notre dataviz.

Si tu es un acteur tiers pour devenir un acteur tiers dans la distribution des horaires de trains, quand tu ouvres

le temps réels tu ouvres les horaires théoriques, tu pourrais comparer tous les moyens de transports.

C’est l’exemple des compagnies aérienne aux états unis, c’était gratuit au début, ensuite Google leur a facturé

la présence sur le service puis ensuite, non pas l’acte d’achat mais le simple clic d’un internaute. Aujourd’hui

lorsque Google fait de l’intermédiation avec les compagnies aérienne Google prend 30% du prix du billet. Tu

as concrètement un acteur qui capte une partie de ta valeur et une partie de la relation avec le client. Et quand

tu ne comprends plus la relation client, tu deviens le sous-traitant de l’entreprise plus global qui elle a la maitrise

de la relation client. Tu es dans la merde surtout à l’ère du numérique

NS : Qui historiquement est le plus avancé sur le projet ?

RL : Si on prend 2 branches d’activités avancés (Transilien et TER) ce sont les branches marketing et services

qui gère les démarches Open Data, dans ses branches respectives, il y a une direction marketing service qui va

piloter la relation client la relation voyageur, sur plein de canaux et c’est des gros sujets.

Ce n’est pas un enjeu de tarification, il n’y a pas de « Yeld management ». Les seuls enjeux, c’est la

personnalisation de la relation voyageur et un enjeu d’innovation ouverte, en l’occurrence, l’Open Data,

permettait de diffuser des ressources et créer des liens avec les utilisateurs pour les tirer à nous. Si on

contractualise les innovations avec les startups en termes de communication on le fait toujours sous formes de

partenariat. On ne fait pas de sous-traitances, on veut respecter la co visibilité des deux acteurs.

Notre conviction est que la donnée seul n’a pas de valeur, c’est plutôt le croisement de donnée qui

apporte de la valeur et comment se croisement de nœud de données est réalisé qui va donner de la

valeur, lorsqu’il vienne enrichir un service et qu’il représente de la plus-value pour la start-up, une

donnée de transports qui enrichie un service existant, très personnel.

Du côté SNCF cela fait encore peu, pur nous la création de valeur n’est pas direct la valeur est dans la qualité

de service apporté au client c’est plus difficile à mesurer l’apport. On a bien sur des méthodologies pour

mesurer la satisfaction client, tu peux la voir mais c’est assez difficile, car l’Open Data reste une démarche très

limité par rapport à tout ce qui est fait sur l’amélioration de la relation client. Tout ce qui passe par

l’amélioration de service cela passe par l’information voyageur la rapidité de traitement des incidents, la

relation qu’on les agents avec les clients en termes de posture et de discours, cela joue beaucoup.

On a des grands objectifs sur l’Open Data, c’est trop émergent pour que du reporting, en gros l’objectifs est de

favoriser la création de service, de favoriser la majorité de services créés, de favoriser la réutilisation de service

dans des services tiers, je pourrais t’envoyer une liste, cela marche plutôt bien, on n’a pas un objectif chiffré.

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On à un autre objectif qui est la transformation interne du groupe, cela leur ouvre l’esprit, cela leur fait changer

les projets. Là on est plus sur une mentalité de management.

NS : Comment s’articule toute les différentes initiatives entre-elle les liés au données ?

RL : Le principe global que l’on a évoqué c’est la distinction entre les branches d’activités pures business et

conventionnement, ok pour ouvrir tout ce qui est en délégation de service public, ce qui est en business. On

dit clairement que l’on n’a pas envie de le faire. Dans le cœur business on va le développer nous, à partir du

moment d’autres peuvent faire mieux que nous et qu’il n’y a pas d’enjeux business on laisse ouvert et on

permet à des tiers de les développer. La distinction que l’on fait à ce niveau.

Ce que l’on accepte de développer avec d’autres dans la démarche des projets Open Data :

Des applications :

o On n’a pas besoins d’applications supplémentaires, beaucoup de réutilisation de calcul

d’itinéraire sont principalement basé là-dessus. On veut que les utilisateurs puisse avoir une

UX différents une nouvelle interfaces, et on se rend compte qu’il y a des gens et une cible qui

sont intéressés. Une grande partie des données sont réutilisées pour faire du calcul d’itinéraire.

En ouvrant les données on a une nouvelle expérience. On veut une interface différente.

o Une personnalisation selon des pratiques ou préoccupations de mobilité très précises. Par

exemple dans le cas de tranquilien on a un sujet de l’affluence dans les trains et un usage qui

est le crowd-sourcing par la co-construction de données.

On a la partie enrichissement de l’expérience utilisateurs et d’autre part des applications qui peuvent répondre

à des usages très précis. En gros nouvelle façon de concevoir l’UX, innovation dans l’usage ou un sujet très

particulier de la mobilité. Cela peut être l’affluence, les personnes qui ont un handicap, les applications dédiées

au tourisme qui vont croiser les données transports et données de POI. Très concrètement, les applications de

calcul d’itinéraire représentent 70% de nos réutilisations d’Open Data, sur la partie mobile.

Sur la partie site web :

On essaie de faire en sorte que des services qui ne sont pas positionné sur des sujets de mobilité réutilise de

l’Open Data, un exemple c’est le site bureau à partager, qui utilise plein de données sur l’état puisse réutiliser

nos données.

Le tracks des data visualisation on est plus sur la sensibilisation d’un enjeu de mobilité, c’est un enjeu fort

d’une démarche fort sur la transparence, ce sera des données corporate.

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Enfin le dernier sujet c’est comment les données vont nous permettre de construire des ressources

technologiques. Exemple c’est Noway qui a fait du calcul de cheminement de calcul pour personnes en mobilité

réduite, alors qu’avant c’était tout type d’handicap regroupé, maintenant on personnalise ce type de profil, c’est

vraiment intéressant.

NS : Quelles sont les opportunités pour les départements marketing ?

RL : On reste un groupe public, on a un devoir d’alimenter des startups et des PME. Lorsque l’on vend la

donnée horaire, on vend l’accès aux API avec tous les services qui viennent avec. Par exemple pour les

entreprises, on transfert des grands écrans, la personnalisation par exemple les écrans dans les entreprises avec

les horaires de trains et commerce pour les employés. C’est un service et pas forcément un sujet spécifique à

l’Open Data.

Notre cible reste des startups et des entreprises émergentes, La seul limite c’est sur l’API temps réel Transilien,

c’est principalement une question de rupture de charge, et après c’est aussi la question d’un trop concurrent

qui pourrait construire un service.

On évolue aussi vers la contractualisation, les entreprises qui vont intégrer des données et là on va tarifier en

fonction de l’API comme le fait Google avec son API de cartographie. Il y a peu d’entreprises qui utilisent nos

données pour créer des services derrière. C’est principalement sur la mobilité. Il y a par exemple Vinci qui

utilise nos données, et ADP. On a des projets de plateforme de données qui permettrait de la diffuser en Open

Data selon un certain nombre de requête ou un format d’affiliation pour avoir des données d’utilisation de ces

applis et avoir des informations sur les types de requêtes de la tarifier ensuite, pour l’instant on réfléchit plus

sur ce sujet-là.

NS : Opportunité niveau France et enjeux de DSI :

RL : Je trouve que beaucoup de travail a été fait par etalab depuis l’ouverture de la palteforme en 2011, la

direction qui a été prise sur comment la donnée au-delà de l’innovation peut être utilisé pour moderniser

l’action public sous les angles de transparence, c’est plutôt un sujet qui va dans la même direction, bien qu’il

y a beaucoup de contrainte techniques puisque les SI de l’état et des grands groupes, ne sont pas construits

pour être ouverts et faire appel à des API.

Il faut convaincre en interne. Les DSI, sa dépend, à la SNCF on a des gens très ouverts sur cette démarche,

mais dans certains grand groupes, il ne voit pas la différence entre Open Data, Big data et données personnelles.

Sa dépend vraiment de l’organisation avec qui tu parles.

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La plateforme DataGouv dont la manière elle a été conçue avec l’idée de la plateforme sociale. La possibilité

de proposer des données et de réutilisation et là sa permet de créer un catalogue de différentes, et enfin c’est

aussi la possibilité de corriger les données. Au risque de refaire un GitHub, mais c’est ça l’idée.

L’Open Data nous a permis de nous rendre compte, de mettre des idées concrètes sur les problèmes, c’est-à-

dire à la SNCF, mais dans plein de grands groupes, les SI, non seulement, ne sont pas compatibles pour être

ouverts, mais même pas ouverts entre les métiers. On se rend compte que quand tu veux exploiter les données

d’une autre branche d’activité, il faut créer des protocoles d’échanges, etc.. Et l’Open Data a montrer comment

construire de la donnée ouverte, d’abord entre les différents acteurs de l’entreprise et pourquoi pas avec

l’externe. Et la pas mal de chantier on été lancer pour casser les silos.

Interview : Stefan Galissié - Chief Data Officer – OgilvyOne Paris

NS : Pour commencer pouvez-vous s'il vous plait, vous présenter et présenter parcours professionnel ?

SG : Je suis Stéfan Galissié, statisticien de formation avec maintenant plus de 15 ans d’expérience en

Marketing Client, à la fois sur des sujets de stratégie marketing globale, digitale, Retail ou purement VADiste.

J'ai commencé sa carrière dans le conseil en Data Mining pour la SSII Lincoln, puis pour l’agence Draft

Worldwide. J'ai ensuite dirigé le pôle Intelligence Clients et CRM Interactif chez Yves Rocher de 2005 à

2013. Je suis aujourd’hui Chief Data Officer chez OgilvyOne Paris.

NS : Quel est le positionnement stratégiques des entités dans lesquels vous opérez ? Quelles positions vis-

à-vis des éditeurs de solutions (BI, RTB, Retargeting...) ? Des cabinets de conseils ? Qui sont

vos interlocuteurs privilégiés dans les entreprises ?

SG : Chez OgilvyOne, nos grandes idées ont un objectif simple. Modifier durablement le comportement

d’achat. Elles naissent avec l’analyse des données afin de découvrir les insights clés. Elles prennent vie dans

des expériences créatives 360 orchestrées pour chaque individu. Elles s’articulent au bon moment et sur les

points de contact les plus performants. Elles évoluent et s’ajustent pour créer des relations pérennes et

rentables.

On représente souvent les Big Data comme un phénomène de rupture, mais tout n’est pas nouveau dans cette

notion. Cela fait ainsi plus de dix ans que l’on parle de Data Mining et de montée en puissance du volume des

données. Derrière le terme Big Data, il y a surtout une évolution des mentalités, qui pousse de plus en plus les

entreprises à faire de la donnée un atout principal.

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Pour répondre aux besoins de nos clients, aller d'un point A à un point B, nous utilisons les services d'éditeurs

X ou Y, voire de partenaires conseils W ou d'experts d’une technologie précise Z. Nous sommes agnostiques

vis à vis des solutions du marché. Les solutions que nous embarquons dans nos recommandations sont sur-

mesure et adaptées le plus possible aux besoins de nos clients.

Nos interlocuteurs privilégiés dans les entreprises sont les CEO, CMO et CCO.

NS : Comment se structure vos équipes avec les nouveaux enjeux liés aux données ? Opérez-vous en

équipe mixtes (data scientist/creative technologiste) ?

SG : Chez OgilvyOne, l'exploitation profitable de la Data est inscrite dans les gènes de l'agence. Cette data est

à la fois fortement présente en terme de compétences dans les équipes data bien entendu mais aussi dans les

équipes créatives et du planning stratégique.

Cette proximité nous permet d'adresser des problématiques consommateurs sur l'ensemble de leur parcours.

Qu’il s'agisse de data media, CRM ou Shopper, cette organisation nous permet d'apporter une réponse sur

l'ensemble du cycle de vie client.

Le mode opératoire est souvent mixte et suivant les objectifs attendus, croise les différents métiers de l'agence.

Quelques exemples : Data Scientist + Creative Technologist pour une solution Dataviz, Data Scientist + Planer

Strat pour une recommandation stratégique ...

3) Quelles sont les initiatives du groupes Ogilvy permettant d’accélérer les solutions liées au Big

Data et d’accompagner l’expérience de vos de clients ? Avez-vous des méthodologies précises ?

SG : Nos offres autour du Big Data se construisent à partir de chaque besoin métier : Par exemple, les approches

liées aux Data issues des objets connectés sont spécifiques. Celles liée à la modélisation du parcours client

(qu'il s'agisse de parcours marchand ou de détection de leads) se basent sur des données de log ou de raw data

issues du web analytique (ex : BigQuery) sont moins spécifiques.

Plus globalement, notre offre Data se décompose en trois parties.

- La première concerne nos services d’accompagnement des entreprises dans leur projet de

transformation. Nous apportons des solutions et du conseil sur la stratégie, l’organisation et les outils à

mettre en œuvre autour de la data. Qu’il s’agisse de projets CRM, de mise en place de programmes de

fidélisation ou tout simplement de développement de la connaissance clients.

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- La seconde concerne la mise à disposition des informations issues de cette data. Nous travaillons

particulièrement à des approches qui permettent un reporting efficace (outils de Dataviz qui permettent

une meilleure adoption et compréhension des résultats basés sur des KPIs triés sur le volet) et interactif

(un reporting qui est véritablement utilisé par les forces de ventes, les marketeurs, …).

- La troisième concerne notre capacité de modélisation du comportement du consommateur. Qu’elle soit

supervisée comme c’est le cas dans les analyses prédictives que nous pratiquons pour anticiper le churn

ou détecter les futurs potentiels dans une base de clients ou prospects installée. Ou bien non supervisée

lorsque l’on cherche à typer une population de consommateur ou bien extraire des thématiques de

discussion autour d’un produit ou d’une marque.

Nous avons également développé des outils à partir d'analyse sémantique et de modélisation d'impact. L'un

d'entre eux a donné naissance au site http://www.linkreaser.com que l'on vient de sortir en beta test.

NS : Existe-t-il différentes approches de Data Driven marketing ? Quels sont les principaux KPI à retenir

de ce type de stratégies ?

SG : Oui, les approches sont aussi diverses que nos produits sont spécifiques. Les principaux KPI tournent

autour de l'impact généré sur le comportement de la cible à laquelle on s'adresse. Ils sont bien entendu

spécifiques au sujet traité.

NS : Quelles sont les étapes clefs de ce type projet pour arriver à un niveau de maturité fort avec vos

clients ?

SG : Le Big Data, au dela du buzzword, c'est un véritable changement de paradigme pour les entreprises.

Un projet Big Data n’est pas juste un projet informatique, c’est un élément de la transformation digitale d’une

Entreprise.

C’est sa capacité à s’ouvrir à une connaissance nouvelle, sortir de l’exploitation de ses seules données d’achats

et introduire des notions d’engagement, d’advocacy dans la mesure de sa valeur clients.

Mais les projets Big Data ne peuvent pas supporter d’effet tunnel. Il est nécessaire de faire rapidement la preuve

de son impact business. La donnée est nouvelle, le champ des possibles s’élargit mais il faut savoir raisonner

par étapes, commencer petit et délivrer des premiers insights, les rendre actionnables et industrialiser cette

approche test and learn dans la montée en puissance du projet.

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NS : Arrivez-vous à créer des approches de design de services et d’utilisation des données, qui peuvent

permettre aux équipes terrains de vos clients d’aller plus loin dans la transformation d’un prospect ?

SG : Oui, les solutions que nous proposons intègrent un important volet UX. On l'associe notamment à l'un de

nos outils data d'audit de l'existant et d'identification de piste d'amélioration pour, par exemple, la détection de

lead. Par exemple sur la mise en place d'un service via un support digital, nous analysons la structure réelle de

navigation de l'utilisateur du support afin de la comparer à la structure théorique mise en place par l'équipe UX

lors de la création du service. Cette analyse permet de détecter les principales toutes de détection de lead mais

aussi les impasses. Pas seulement les points d'abandon de l'utilisateur mais également les endroit où il se perd...

avant de retrouver son chemin.

NS : Quelles sont les limites et points bloquants rencontrés par vos équipes et clients sur les

nouvelles opportunités d’utiliser les données ?

SG : Les limites et points bloquants sont divers et variés.

Retenons un point toujours sensible sur ce type de sujets : celui qui concerne l'identification unique de leurs

clients. Leur capacité à lier cette nouvelle data à celle plus "classique", maîtrisée pour certains depuis très

longtemps.

Une autre limite concerne l'usage trop intensif de la personnalisation, le manque de maîtrise des algorithmes

de recommandations. Nous proposons toujours de ne pas se laisser séduire par les modèles auto-apprenants

sans en avoir vérifié au préalable la capacité de mise en place de filtres métiers. Afin d'éviter de recommander

une descente de gamme par exemple. Nous proposons également de garder toujours une place à l'aléa, à la

découverte fortuite.

NS : Quelles sont les grands sujets qui font évoluer le positionnement des agences sur l’exploitation des

données ?

SG : Les objets connectés et les réseaux sociaux. Deux nouveaux gros providers de data. Cette nouvelle donnée

est le plus souvent gérée en dehors des SI des annonceurs, sauf pour les plus matures qui les ont déjà intégrées.

NS : Avez-vous des méthodologies où des approches qui favorise le développement des opportunités de

vos clients ? La démarche Data Driven favorise-t-elle de nouveaux territoires à explorer ?

SG : L'approche exploratoire, non supervisée de la data client permet d'identifier de nouvelles opportunités

business. Plus globalement, une approche Data Driven permet à nos clients d'apporter plus de services, plus de

relations personnalisées et équilibrées. Car c'est l'approche de la connaissance (clients) qui, à chaque nouvelle

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découverte, apporte son lot de nouvelles questions et donc de nouveaux territoires à explorer. A ce titre, la data

non structurée a priori pour l'analyse est très intéressante car elle n'est pas provoquée, supervisée par nos clients

et donc porteuse de nouveaux insights.

Interview : Stephane Pere – Chief Data Officer – The Economist

NS : Pouvez-vous vous présenter ? Quel est votre profil ?

SP : J’ai un parcours media plus medias publicitaire (Sales Advertising), chez Canal+, chez Bloomberg

télévision, j’ai travaillé sur la transition des medias (presse pour vendre de la TV). Après Yahoo Medias pour

les aidez à percevoir le média online comme un média de performance. Ensuite chez The Economist j’ai

accompagné les gens de Presse papier à vendre du Online. Ensuite chez The Economist, j’ai poursuivi et j’ai

lancé une offre publicitaire, j’ai lancé ce que l’on appel un Ad-network aux Etats-Unis. Un nouveau produit

avec une alliance de 65 sites médias de qualité, un peu de l’interprétariat. C’était un environnement très touché

par la data. La data tout seul cela ne suffisait pas pour faire de l’image.

Ensuite on m’a proposé de devenir Chief Data Officer. Je comprends les contraintes du marché publicitaires et

les contraintes qui s’y présentent, je connais tous les interlocuteurs en interne. L’objet est d’utiliser la data pour

mieux connaitre nos consommateurs pour être de meilleur Marketeurs nous-même (promotion et abonnement)

et aussi des offrir insight publicitaire pour nos clients. La connaissance client au service de nous-même et de

nos propres clients (Customers nos lecteurs, Client nos annonceurs)

NS : Quel est votre rôle au sein du media que représente The Economist ? Vos missions et prérogatives en

tant que Chief Data Officer ?

SP : Alors Chief Data Officer c’est un rôle qui casse les silos en entreprise, quand je dis casse c’est de manière

gentil. La valeur de la data vient de la connectivité de la data. Il faut que l’on ramène ensemble des données

types pubs par exemples des données web analytics. Des données ventes d’abonnement qui viennent des

équipes abonnement.

On se retrouve alors avec différentes bases de données de différents Business Units avec différents process.

Rôle de catalyses en interne qui va faire tomber les barrières politiques mais qui peut parler la langue du

business. On est au final là pour démontrer pour aider les Marketeurss en interne à devenir de meilleur

Marketeurs, via des insights et des outils. D’où le titre de Chief Data Officer, je ne fais pas parti du board mais

c’est de nouvelles fonctions, sa donne le gravitas en interne, c’est top down. A un niveau international, global

et tout métier.

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NS : Comment traitez-vous le sujet de la Big Data chez The Economist, vos sujets sont Comment se

positionne un media comme the economist sur le sujet du Big Data ?

SP : The Economist n’a pas de Big data en soit, la première étape c’est déjà croiser les sources de données

pour ensuite lancer des projets Big Data. Et là il y’a déjà du boulot à faire en interne. Les données first party

ne sont pas s’y grandes que cela, mais déjà il faut s’assurer de les croiser, pour cela on a lancé une DMP avec

le soutien de Bluekai.

NS : Quel est la définition de ce sujet principal autour des big data que vous venez de lancer ?

SP : Dans cette DMP on va amener différents fil d’informations interne (le webanalytics, le crm et enfin toute

les camapgnes analytics =Combien d’email, combien de push notification, Medias, Promos…) Comme cela

on a tous les points de contacts sur mon consommateurs et tous les éléments qui les caractérise. Il n’y a pas

besoin de big data pour ça. Pour la capacité à traiter en temps réel des insights oui il y a des outils qui nous

permettent de traiter tout ça en temps réel. Pour la seconde étape pour les données tiers pour la third part

data là il y a des outils big data, des gens comme bluekai, Experian, excelate… Eux collectes des données,

et après ils se sont rendu compte qu’il fallait qu’ils offrent des outils pour offrir des services. On pouvait acheter

des données complémentaires : Collecter, Structurer et proposer des ciblages. On peut avoir accès à un

foisonnement de données tiers, on se pose la question de ce que l’on peut faire aussi avec l’open data. Notre

gros projet est donc de créer un cerveau qui apprend, on essaie d’avoir une connaissance plus fine de nos

clients.

1er projet = Segment très avancé

2eme projet = DMP connecté aux places de marché display

2eme étape : on peut pousser des segments faire du marketing automatisé (place de marché vers le

display=va m’acheter ce profil, va me retrouver sur du social media, plus intéressant, je peux activer des

segments spécifiques via)

NS : Quels sont les succès clefs d’un projet type de Big Data ? Avez-vous mis en place des

méthodologies/process pour accélère le succès d’un projet ?

SP : L’appel d’offre de la DMP, était d’expliquer la connectivité de la data en interne avec l’activité online et

offline, 2nd point une interface pour créer des segments intuitif, et 3eme point pouvoir se connecter à des outils

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de marketing varié en interne et à l’externe. C’était vraiment un brief métier construit en interne. Donc c’est

moi qui est lancé cet appel d’offre soutenu par un ingénieur interne, un ingénieur de data, car le projet de DMP

était un projet qui avait déjà été initié en interne mais n’avait pas marché. Quand j’ai eu mon rôle j’ai ressuscité

ce projet et décidé de le lancer par des prestataires externes pour travailler avec des gens qui ont ce type de

projet au quotidien.

L’échec à l’époque était l’interface, c’était une usine à gaz, il y avait plein d’étape séparée et on ne peut pas

tout faire de puis l’interface. L’interface est l’endroit où l’on fait tout et à l’époque, les marqueteurs avaient

décidé de ne pas l’utilisé. Sur le choix de bluekai, il y a une historie d’argent, plus important que l’argent il y’a

l’histoire du Plug&Play est-ce que les spécifications sont disponibles, à la fin on a fait un arbitrage sur pas prêt

et déjà prêt. De manière étonnante on est arrivé à BlueKai, je dis de manière étonnante, car c’est un acteur

perçu de la third party, mais ils avaient une bonne interface, une bonne connectivité in et out. Et un accès

a énormément de base de données. Donc ça c’est la première étape, et dernière chose sur BlueKai, on s’est

rendu compte qu’à l’abonnement que l’on a différente région, qui font appel à différente agence, qui font appel

à différente DSP, différents fournisseurs de données, puisque bluekai donnait accès à media mass, toutes les

grandes places de marchés de données. D’un coup avec un DMP interne BlueKai, on peut ramener tous les

trackings à un trackings qui lui est connecté à toutes les bases de données.

Cela simplifie les workflows, cela permet de ramener la connaissance campagne en interne. Souvent on

externalise les campagnes par les agences, mais comment ramener la connaissance en interne c’est compliqué.

Notamment l’attribution marketing des campagnes.

Les KPI de ce projet c’est donc l’adoption des Marketers. C’est un KPI assez marrant. Il y a de manière

classique le KPI de revenus attaché donc ça c’est vraiment de l’argent, les revenus médias attaché à l’utilisation

de la DMP.

Deuxièmement le nombre de campagne marketing interne, trois types de campagnes les campagnes

acquisitions et les campagnes rétentions et engagement.

- Je veux ramener de nouveaux abonnés (à l’externe)

- Engagement c’est augmenter les usages

Enfin les troisièmes critères est le nombre de « connectors », le nombre d’interface marketing connecté à cette

DMP, donc plus cela devient central, plus cela devient central. L’idéal c’est que cette DMP soit connecté à

tout. Parce que finalement on vend tous de l’audience aux annonceurs et nous-même. Sachant qu’il n’y a pas

de régie spécifique détaché du groupe The Economist. Le succès est donc comment cet outil DMP devient

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central dans l’utilisation des équipes marketing. J’ai deux métiers marketing interne : Circulation et

Advertising.

NS : Comment s’articule le Big Data et ce qui en découle les Data-Science, quels sont les applications au

métier du marketing ?

SP : Le packaging des données In, c’est notre équipe avec l’équipe ingénieur interne. C’est un choix rapide,

quand je dis je veux ramener le web analytics, via Drupal et omniture. On veut ramener la base de

données. Ça c’est assez simple c’est les données qu’on collecte déjà donc on sait ce que veut collecter et

où on n’a pas besoin de beaucoup d’engineering. Concernant les données tierces on fait confiance à BlueKai

qui a fait son choix sur des chevaux de courses sur les données tiers. Il n’y a pas eu de Data Scientist du tout

sur le in. On pourrait aller plus loin bien sûr, sur les devices, les signaux avancés.

On aura de moins en moins besoin de third party data parce que je collecte plus en interne, et là je peux réfléchir

plus en interne. Comment je bridge the gap, comment avoir moi de third part data. Si quelqu’un à tel

comportement en ligne, et remplis un formulaire en ligne, les prochain qui viennent en ligne avec ce

comportement je leur applique tel profil sociodémographique. Cette seconde étape est plus simple que la

première en termes d’efforts.

NS : Quels sont les démarches les plus intéressantes des projets liés au marketing ? Que peut-on penser

du data driven marketing ?

SP : Sur les sujets de l’insight et l’automatisation :

Première chose sur l’insight : chaque silo bénéficie de ne plus avoir un mur, par exemple l’équipe advertising

qui avait plutôt les web analytiques. Enfin ils ont des données qui ne sont pas que comportementales mais

sociodémographiques et transactionnelles. A l’inverse, pareil pour les abonnées, ils ont accès à des

informations comportementales sur les abonnées ce qui permet de faire des segmentations plus fines, dérrière

j’envoie un email à mes abonnés avec des démarches marketing plus efficace. Derrière y’a pas de début mais

sa il faut l’expliquer.

Sur le fait de l’accès à la donnée c’est assez rapide, j’ai le gravitas la mission et le titre, et l’accord du PDG.

Après c’est l’es usages, les process et les métiers.

Il est vrai que j’ai moins de résistance avec la publicité. Ils savent que le marché est maintenant data driven, ils

ont l’habitute de l’automatisation, 100% motivé. La diffusion ils sont aussi motivé ils savent que la diffusion

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et l’acquisition digitale est primordial. Là où cela prend un peu plus de temps, c’est la personnalisation de nos

emails, nos pushs notification (dans la partie retention & engagement)

Les cycles ont été très courts, c’est maintenant que je rentrer dans la sensibilisation et la formation, y’a plusieurs

choses qui vont être menés, une grosse campagne en interne corporate sur la culture du data driven. Que

l’on est sur un monde corrélation et pas de causalité, je suis par ailleurs en contact en interne avec l’éditeur

Kenneth Cukier sur le sujet afin d’expliquer le changement de paradigme en interne, qu’il faut faire du

testing qu’il faut « test & scale » on travaille avec maximizer à ce sujet, et que l’on apprend par le test.

C’est plutôt ça être data driven. Chaque Marketeurs y verra une modification de son métier.

La second phase sera du training, vu que l’on aura les campagnes et analyser les campagnes tests réalisés, ce

que l’on a appris et les résultats. Le test & learn s’applique même à la gestion du projet, pour gérer l’adoption

il faut même gérer et stimulé une curiosité en interne. Il faut trouver des allier et des early adopters. L’équipe

publicitaire à demander le projet, le sponsor était advertising et ceux qui ont sauté dans la barque c’était le pole

acquisition. C’est une autre source de connaissance pour toutes les équipes, par la suite...

Data base driven business model :

- Insights liés aux traitements des données

- Cabinet d’étude

NS : Qu’en est-il de l’automatisation du marketing ? Quels changements sont engendrés par exemple

par le RTB dans les organisations ?

SP : De notre côté la vision c’est s’équiper d’outils et de démarche qui permette de connaitre plus sur le client,

principalement sur les données first party, pas du tout sur l’esprit d’ouvrir nos données aux autres. Plus se

connecter mieux savoir pour pouvoir nous même, offrir aux annonceurs mieux connaitre du couplage

média+data. On ne veut pas vendre la data seul, car la data nous permet de mieux vendre notre media.

Cela veut dire que l’on fait le ménage sur collecte de données sur notre site, on fait attention aux tags des

annonceurs, aux scripts posés, car certains peut-être collecte des données à notre insu. Certains lâches des

cookies et retarget de manière illégal sur d’autres places de marché. Il y a tout un travail qui se fait en ce

moment de fermer l’accès à la donnée pour les tiers, le seul accès par les tiers doit passer par nous-même. On

monte, qui mieux que The Economist pour comprendre et toucher les leaders d’opinion à l’échelle globale et

pour ça c’est du couplage Media+Data. Il faut faire des arbitrages malins, nos partenaires tiers comme Google

nous aident à comprendre nos clients, il faut éviter d’être 100% dépendant, car ils peuvent couper l’accès aux

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données. La donnée est un actif stratégique à l’entreprise, il faut la protéger, il faut trouver l’arbitrage entre

sécurité et privacy.

De manière intéressante on se pose la question si nous allons vendre des insights. La valeur revient dans le

packaging et l’usage de cette donnée. Avoir donc un métier service marketing différent. Si on comprend mieux

les leaders d’opinions, on peut aussi vendre des insights sur cette population de manière globale, on pourrait

traiter la donnée pour poser des insights. Et presque devenir un cabinet d’étude mais vendre la donnée brute.

Il faut montrer en quoi une logique data driven sert au quotidien permet d’avoir

1- Plus de revenus publicitaire

2- Facilite le quotidien des Marketeurss en interne

3- Peut-être une source de revenus incrémentales avec de nouvelles démarches

Sur l’automatisation et le re-targenting nous n’aurons plus besoin de critéo pour faire du retargeting, en effet

quand je suis une marque ou un site marchand, ce que je souhaite c’est recibler quelqu’un qui a déjà un intérêt

sur mon service. Avant ces mécanismes n’existaient pas, en plus le risque était de leur côté en plus en vendant

au CPC et en achetant au CPM le risque était que de leur côté donc très agréable pour les marqueteurs et ensuite

Critéo faisait un travail pour avoir accès à un très grand inventaire sur la place de marché en temps réel. Et

aujourd’hui des acteurs comme Bluekai, peuvent réaliser tout ça, aujourd’hui le point ou Critéo va peut-être se

ré-inventer c’est dans la personnalisation (Pousser la bonne offre dans la créa) cela devient la meilleure offre,

aujourd’hui les marques s’équipent elle-même avec les outils.

Interview: Sebastien Imbert – Chief Marketing Officer Microsoft France

NS : Pouvez-vous vous présenter ? Quels est votre profil ? Quel est votre rôle au sein d’Orange ? Vos

missions et prérogatives ?

Sebastien Imbert : Je suis Sebastien Imbert Chief Marketing officer, en charge de la stratégie digital de

Microsoft France sur des sujets qui vont du web marketing au social media en passant par les canaux de

distributions numériques. Je suis aussi responsable des évènements que représentent les TechDays, et des

campagnes médias.

NS : Comment se positionne Orange sur le sujet du Big Data ?

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SI : Je ne vais pas parler de big data je vais parler de smart data. Et au marketing au final dans nos métiers en

étant à la direction digital. C’est avant tout de données de qualité, de la donnée que l’on peut se fier des données

qui sont un point de références autour de laquelle je vais pouvoir, soit savoir si une campagne ou un dispositif

performe ou non. Si je dois l’améliorer le modifier ou non, soit de m’éclairer sur des directions possibles, des

insights qui me permette de définir stratégies en vue des futures campagnes, qui me permettent de définir une

stratégie.

Smart Data pour la partie exécution et pour la partie stratégie. C’est vraiment de la donnée sur laquelle je vais

pouvoir me fier. Pour moi le Big data, dans mon travail c’est la capacité à recevoir une information qui va

permettre donner du sens.

Recevoir une information qui va permettre de donner du sens

o Une direction pour l’équipe

o Pour nos campagnes

o Et du sens pour les clients

Les clients, le facteur de différenciation est sur l’expérience que l’on leur apporte au bon moment, au bon

endroit sur bon devices. C’est de plus en plus essentiel pour nous et sans la data nous ne pouvons pas le

faire.

Le smart data n’est pas du tout antinomique avec l’intuition, au contraire c’est un accélérateur

d’intuition d’idée, de business model de nouveaux dispositifs et plus que jamais, on est dans l’ère du test

and learn. Auparavant c’était compliqué d’avoir des retours de campagnes de segmentations d’usages,

pour faire des relevés de prix de promos en linéaires en grande distribution. Y’a 4-6 ans il fallait au

moins 1 mois, aujourd’hui en une journée on peut prendre des décisions, améliorer notre plan, si on

maintient ce que l’on a établi, et donc être plus créatif derrière.

NS : Quels sont les grands types de projets marketing sur le sujet du Big Data ?

SI : Il y a un point important il a deux catégories de données c’est les données déclaratives et non

déclaratives. Il y a l’intersection des deux en respectant toutes les contraintes liées au respect de la vie

privée, des directives données par la CNIL, la communauté européenne. Sachant qu’il y a pas mal de

choses qui se font entre les deux. L’aspect déclaratives, c’est la donnée stockée à des relations associé à un

client direct ou indirect (exemple : je suis commercial, j’ai un certain nombre d’informations que je peux

stocker dans le CRM, j’ai des infos sur ma supply chain que je peux stocker, tout cela va créer une approche

CRM).

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Sur l’aspect non déclarative, c’est l’axe le plus en développement actuellement et qui est en croissance et qui

vient compléter les stratégies de CRM. Quand on parle de CRM management, aujourd’hui on parle cross canal

Relationship management, mélangeant données déclaratives / non déclaratives. Car les données qui

n’appartenant pas à l’entreprise peuvent avoir le même niveau d’importances que des données qui lui

appartiennent, d’où la mise en place de nouvelles approches comme le data management plateforme où

je vais créer du first part data que je vais garder que pour moi. Ce que font une majorité des acteurs sur

le marché et puis des acteurs qui vont vendre la data, ce qui correspond à third part data pour un

annonceur, pour des annonceurs pour créer des plans de ciblages avec des archétypes clients pour diffuser des

messages en fonction de leur comportement et de leur usages sur internet ( les sites où ils vont où ils ont acheté,

fréquentation de sites.) cela permet d’étendre les services liés aux campagnes tout ce qui est lié au social. (Un

mélange de paid & earned). Lorsque j’ai un nouveau follower sur twitter ou LinkedIn, c’est une forme de CRM

parce que je vais pouvoir lui envoyer un message spécifique même si je ne l’ai pas dans la base de données, et

donc les stratégies de social CRM que j’inclus dans x-rm et la partie Smart Data. Permette de nouveaux

scénarios, d’engagement et de rapprochement avec la marque.

Il y a un autre domaine sur lequel je suis encore assez novice, c’est l’internet of things avec l’avènement d’ipv6

mélangé au nfc qui permet de faire du machine to machine et du sans contact qui permet de faire smartwatch

et de la voiture connecté, définir de nouveaux usages, de nouveaux comportements, pour faire des aller/retour

paris marseille, a quelle vitesse, quelle type de marque d’eau j’utilise au quotidien, quel est mes types de

consommations.

Un exemple très concret est Amazon, champion du monde dans le domaine quand ils mettent en place des

dispositifs, on peut s’abonner à des en fonction des couches culottes en fonction de l’avancé de l’âge du bébé.

Y’a plein de scénarios qui vont apparaitre, un mélange de données froides et d’extrême créativité.

NS : Quel est l’impact sur le métier du marketeur de toutes ces nouvelles opportunités ?

SI : Du côté positif, c’est l’avènement de l’intérêt des jobs dans le digital c’est le test & learn, avec cet accès

à des données en quasi temps reel, je peux tester des choses à moindre coup voir si cela marche si cela

marche je peux puis industrialiser. Avant faire du test & learn fiable efficace rapide et à moindre coups

pour une marque c’était relativement compliqué. Je vois de l’AB testing, je vois des nouvelles approches

d’adressages de clients avec les DMP, du retargeting, je vois les DMP, j’y vois plus d’agilité.

Le test & learn c’est au quotidien, cela devient un mode de fonctionnement quasi quotidien. Je peux voir en

co-création, en crowd sourcing des créations qui fonctionne ou ne fonctionne pas, je peux tester comme un

miroir. Par exemple chez l’opticien comme le cas https://www.warbyparker.com/ , je peux demander à mes

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proches l’avis, ce qui est intéressant, c’est là que l’on voit que des dispositifs de réseaux sociaux en ligne

marche dans la vente, cette marque envoie des montures de lunettes, l’internaute envoie la photo et il y a un

poul suffisamment fort de follower pour dire à l’internaute si les lunettes lui vont où non. Et ensuite

gratuitement il peut faire modifier en fonction. Avant cela coutait très chère ce type d’approche. Avec ce type

de scénario c’était y’a 4-6 ans, peu probable, c’est des nouvelles approches de test liant physique et virtuel. Le

point négatif, c’est l’hyper ciblage oui, l’hyper intrusivité non, on peut aller tellement loin dans le big

data, que cet exemple de minority report c’est super excitant, et c’est hyper flippant. Faire des dispositifs

publicitaires qui permettent de faire des reconnaissances faciales.

L’intérêt c’est le positionnement marque, le rôle qu’elle doit jouer auprès du concitoyen et du consoacteurs.

On voit que le RSE dans les entreprise c’est de plus en plus important, il y a 20 ans on achetait une voiture par

rapport à sa vitesse etc… par exemple cela devient fondamentale l’émission de CO2 dans l’industrie et pour

les marques automobile.

Dans l’informatique c’est un peu la même chose, sur le long terme c’est important de considérer le client dans

la relation à la marque. Peut-être sur le court terme je vais à accélérer le cycle de vente auprès de certain client

mais je risque d’en détruire d’autres. Ma perception de marque d’acteur responsable, elle passe aussi par ma

capacité à gérer les données.

Au-delà de l’hyper ciblage apporté par le smart data c’est aussi par les outils la complexité qui, on a une

multitude d’outils, de sources. Il n’y a aucun Marketeur qui peut connaitre tous les outils et qui peut être un

expert à part entière, c’est assez déstabilisant parce que l’on sait, que l’on ne peut pas être expert, on ne peut

pas maitriser tout ce qu’il se fait.

Ce que l’on voit apparaitre chez nous, ce sont des personnes qui ont fait des écoles en statistiques, ou

des masters en finance à Dauphine, qui se sont intéressés au marketing par la suite, qui ont des affinités

avec les chiffres et qui vont prendre des rôles liés à l’optimisation digitale lié aux données que l’on reçoit

pour créer des plateformes hébergés pour toutes ses données, en lien data visualisation. Si c’est un

Marketer landa que formé aux méthodes de marketing traditionnel c’est quasiment impossible d’arriver

à interpréter et visualiser ses données aux mieux. Il faut comprendre les deux ce n’est pas qu’un compte de

résultat, loin de là, il y a aussi beaucoup d’ambigüité faut comprendre les mécaniques de campagnes en tant

que tel, ce compréhension du GRP, qui évolue complétement aujourd’hui, de capacité d’intégration du plan

media. Chez Microsoft il y a des IMC, intergreted marketing & communication manager. Ils vont être en charge

de mettre en place les dispositifs de communication intégrée puis nous des personnes chargés d’analysés tous

ses dispositifs intégrés, en interne, en lien avec des experts en interne.

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NS : Comment s’articule le Big Data et ce qui en découle les Data-Science, quelles sont les applications

au métier du marketing ?

SI : Alors je ne préfère pas en parler. Sur le prédictif on a des sujets, notamment dans le Btob. Je vais plutôt

prendre l’exemple du Machine Learning en BtoC. Nous on est des Marketer des utilisateurs de technologie et

Microsoft est éditeur de logiciel, le Machine Learning c’est une tendance de marché, c’est pour cela que l’on

veut permettre au plus grand nombre d’utiliser les capacités du Machine Learning. C’est pour cela que vont

proposer au travers de notre cloud Azure des outils de Machine Learning.

Forza5 permet à quelqu’un qui joue à ce jeu de course de jeu en ligne. Chacun a sa manière d’écrire, et bien la

manière dont on accélère on freine, c’est un style de conduite, dans Forza5, au fur et à mesure de la conduite

plus vous jouez aux jeux, on va créer un drivatar, il peut être sollicité par un joueur connecté au réseau de la

Xbox, même si ce joueur n’est pas en ligne. Avec ce drivatar je peux faire des tournois en ligne avec des

personnes ayant le même profil à moi. C’était des scénarios impossibles il y a 3-4ans.

Autres types de scénarios, dans Halo 4 ou 5, la capacité de détecté des tricheurs, ce n’est possible que par

l’algorithme, proposer des nouveaux scénarios de jeu. On peut acheter des scénarios de jeu qui sont fait que

sur des parties passés.

Par extrapolation et que je pourrai étendre au btob Chez Microsoft on a un Chief Economist Officer spécialisé

dans l’analyse comportemental et le prédictif, il a pris des sports européens et des compétitions qu’il ne

connaissait pas, il a choisi l’eurovision, il a compilé toutes les informations.

Il a réussi à prédire avec 48 heures d’avance le résultat de l’eurovision, et c’est très difficile à prévenir, il a y

120 personnes dans le jury, qui doit voter pour des groupes et chanteurs qui ne sont pas dans le pays. Il a fait

de même pour Obama, il la refait pour les oscars, il a été bon sur 19 catégories, il a lancé un site qui s’appelle

« predictwise », il a un algorithme lié aux comportements humains et aux réseaux sociaux, il est assez précis.

Sa laisse entrevoir l’avenir pour les Marketer et les citoyens, et pour les citoyens il y’a des projets géniaux,

comme le « human brain project » qui permette d’acquérir des données du cerveau, comme la capacité de faire

en sorte d’ici 2 3 ans en 24 heures. Alors qu’au début des années 90 on pensait qu’il faudrait 1 siècle, tout sa

grâce au capacité de stockages et algorithmes.

NS : Quels sont les démarches les plus intéressantes des projets liés au marketing ? Que peut-on penser

des nouveaux sujets ?

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SI : C’est une bonne question, assez vaste, donner la capacité au client de gérer sa relation avec la marque ou

de lui donner ce qu’il souhaite recevoir ou non c’est fondamental aujourd’hui. Maintenant s’appeler VRM en

tant que tel. Là où je pense que cela va être compliqué c’est qu’il n’y aura pas de standard, pour les prestataires

…

NS : Quels sont les impacts organisationnels et managériaux que vous observés lors de mise en place de

ce type de projets ? Le metier du marketeur est-il remis en jeux ?

SI : Souvent je prends l’exemple di « Capitaine de bateau » Si je prends internet au sens large qui n’est

composé que de données et du tuyau on est sur un océan géant composé de continents à frontière variable, a

superficies variables, et à impact et puissance variable. Mon rôle de manager c’est juste de définir une

trajectoire en lien avec les problématiques business et marketing de l’entreprise, pour éviter de se faire

renverser par une vague ou de s’associer avec une ile qui serait faire attention à ne pas tomber dans le tactic

trap. Dans mon équipe j’ai un marin pour chaque poste (Social media, Site Web, poste gestion d’évènement,

poste d’analytique, poste vente en ligne). L’information doit bien circuler d’une entité à l’autre, toutes les

entités doivent naviguer de manière fluide et cohérente, un mélange d’expertise et de manière très transversale.

Dans l’équipe digital les nouvelles technologies sont bien absorbé, mais dans le reste des départements pas

forcément.

Interview : Khalid Mehl – Datascientist - 55

NS : Pouvez-vous vous présenter ? Quel est votre profil et vos expériences ?

Khalid Mehl : Je suis Data Scientist chez fifty-five. J’ai fait mes études d’ingénieur à l’école polytechnique

X-Ponts Mes expériences précédentes étais plutôt centrés sur des sujets spécifiques de la data, j’étais

précédemment Business Analyst en E-commerce pour les 3suisses, Ingénieur Production chez Renault et enfin

j’ai eu une expérience en Chercheur en prédiction Business chez Orange.

Pour détailler ma dernière expérience qui était directement lié au métier que j’exerce aujourd’hui j’étais en

charge d’un vaste projet d’outils de prédiction de comportement sur plusieurs médias d’Orange, ceci m’a

permis de toucher à l’algorithme et faire mes premiers pas dans la « Data Science ».

NS : Quel est votre rôle en tant que DataScientist ? Comment définiriez-vous votre métier ? Quelles sont

vos missions et prérogatives?

KM : Le « Data Scientist » est à la croisé de la recherche, de l'ingénierie et du conseil. Chercheur quand il

s’agit de répondre à une nouvelle question en proposant un modèle mathématique qui convient. Un Ingénieur

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qui développe des prototypes et qui est capable de prendre en compte les contraintes opérationnelles. Enfin,

Consultant capable de mener des analyses business pointues et de s’adapter rapidement au contexte client.

NS : Quelles spécificités font d’un projet qui fait appel à des compétences d’un DataScientist ?

KM : Les data Scientists chez fifty-five travaille sur deux types de projets:

-"Data Exploration" : On explore les données granulaires de nos clients soit pour répondre à des questions

précises et pointues, soit pour repérer des pistes d'amélioration et évaluer leurs potentiels.

-"Data Activation" : On développe des outils opérationnels qui prennent des décisions sur la base de la masse

de données collectés par nos clients, de façon automatisée et pour optimiser la performance.

NS : Comment se positionne votre vision de la Data science sur le sujet du Big Data ? Quels sont les

sujets connexes liés au marketing que vous adressez dans votre métier ?

NS : Quels sont les grands types de projets métiers orienté marketing sur le sujet du Big Data sur lesquels

vous avez pu être en relation ou lancer des initiatives ?

NS : Comment s’articule le Big Data et ce qui en découle les Data-Science, quelles sont les applications

au métier du marketing ?

KM : Le Marketing fait de plus en plus appel à l’expertise des Data scientists. L’objectif est soit de dresser le

profil des « clients/utilisateurs » en analysant et croisant leurs données comportementales. Soit de développer

des outils de prise de décisions basé sur de l’apprentissage et la prédiction.

Dans le cas du E-Commerce, il y a plusieurs applications sur lesquels on est amené à travailler:

-L’optimisation du Merchandising (l’ordonnancement des produits dans les rayons)

-La gestion du Pricing (Pricing Temps réel ou gestion des Décotes)

- Relance des clients (avec des recommandations d’articles, des codes promo ciblés, etc.)

-Cross-Selling

…

NS : Quels sont les succès clefs d’un projet type de Big Data ? Avez-vous mis en place des

méthodologies/process pour accélérer la réussite et l’adoption de ce type projet ?

KM : Pour réussir un projet d’activation de données (ex. data driven marketing). Le mieux est de procéder par

‘Test & Learn’ avec, si possible, un ‘AB Testing’.

On commence par des modèles simples au départ avec très peu de d’hypothèses et de variables.

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Ensuite, avec un ‘AB Testing’ bien calibré, on mesure l’écart entre la solution Data Driven et l’ancienne

solution qu’on veut challenger. En analysant en détail, on repère ce qui a fonctionné mieux que prévu et ce qui

a mal réagit afin de dégager des pistes d’améliorations (nouvelles variables, meilleur modèle de

prédiction/optimisation, etc...).

On test, ensuite, chacune des pistes à part, et on introduit celles qui ont un apport positif.

Enfin, on réitère jusqu’à l’obtention de résultats satisfaisants.

NS : Quels sont les démarches les plus intéressantes des projets liés au marketing ? Que pensez-vous du

« data driven marketing » voir du « Data Driven Business Model » ?

NS : Qu’en est-il de l’automatisation du marketing ? Quels changements sont engendrés par exemple

par le RTB dans les organisations ? Avez-vous des cas concret ? Quels sont vos relations avec vos

collaborateurs et vos clients lors du déploiement de ce type de projet ?

KM : Ces questions ne sont pas encore traitées par des Data Scientists, mais plutôt par des spécialistes du mix-

média.

Les deux principales raisons, c’est qu’il ne s’agit pas d’une grande masse de données à traiter et que les

décisions à prendre dépendent énormément de facteurs extérieurs (concurrence, évènements, etc...) qui sont

difficilement mesurables.

NS : Quels sont les impacts organisationnels et managériaux que vous observés lors de mise en place de

ce type de projets ?

KM : La mise en place de ce type de projets renforce les liens entre la direction Marketing et la DSI. Ce genre

projet transforme les DSI de correcteurs de BUG en force de proposition et d’amélioration.

Les opérationnels, qui passait un temps considérable sous Excel pour prendre des décisions, se libère de ses

lourdes taches grâce à l’automatisation et devienne souvent des analystes capables de prendre du recul par

rapport à leur métier. Ils se sentent plus valorisés.

Interview: Bruce Hoang – Big Data Project Director - Orange

NS : Pouvez-vous vous présenter ? Quels est votre profil ?

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BH : Je suis directeur projets Big Data chez Orange France, j’ai une expérience globale dans les médias et de

communication. J’ai été directeur recherche et étude en Angleterre au sein d’une régie nommé Hunamis. sur

des sujets d’analyse de connaissances prononcée sur le digital, la publicité, le marketing et les études.

NS : Quel est votre rôle au sein d’Orange ? Vos missions et prérogatives ?

BH : Directeur de projets transverses ‘accélération’ ayant pour objectif d’augmenter l’efficacité commerciale

d’Orange France et la satisfaction client en optimisant les points de contacts clients ainsi que la connaissance

– en temps réel - de leur comportements et usages.

NS : Comment se positionne Orange sur le sujet du Big Data ?

BH : L’utilisation du Big Data doit permettre de mieux servir nos clients : mieux anticiper des problèmes liés

au fonctionnement de leur équipement télécom (ex : foudroiement des livebox, problème de qualité réseau) en

croisant différents types de données (météo, signal réseau, feedback utilisateur…) en temps réel. Le Big Data

doit aussi nous permettre de mieux cibler nos prospects tout en mesurant la sollicitation des messages. Orange

est très attentif aux attentes des abonnés sur le respect de leur vie privée, par exemple nous n’exploitons aucune

donnée croisée (CRM / surf) sans l’accord explicite de l’abonné(e).

NS : Quels sont les grands types de projets métiers orienté marketing sur le sujet du Big Data sur lesquels

votre entreprise a lancé des initiatives ?

BH : La qualité réseau, les livebox foudroyées, le re-marketing publicitaire digital. On intervient sur de

nombreux sujets qui peuvent paraitre éloigné de notre cœur de métier, mais où de la donnée client est générée.

Par exemple nous avons des activités spécialisés dans le sponsoring ou encore des services événementiels de

luxe qui peuvent nous permettre de récolter des données très précises sur les clients ce qui nous permet

d’enrichir cette connaissance pointue sur des segments spécifiques et d’enrichir notre cible marketing globale.

NS : Quels sont les succès clefs d’un projet type de Big Data ? Avez-vous mis en place des

méthodologies/process pour accélère le succès d’un projet ?

BH : Le succès se mesure par l’efficacité incrémentale en termes de ventes, économies, et satisfaction client.

Nous mettons en place une plateforme Big Data qui permet de mieux accéder aux données, avec des Data Lab

(en mode sandbox) paramétrés avec des niveaux d’autorisation spécifiques en fonction des utilisateurs (Data

Experts, Data Scientists).

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NS : Comment s’articule le Big Data et ce qui en découle les Data-Science, quels sont les applications au

métier du marketing ?

BH : Notre conviction est que le Big Data répond à des enjeux marketing et métier, c’est une transformation

du marketing (et non de l’IT).

NS : Quels sont les démarches les plus intéressantes des projets liés au marketing ? Que peut-on penser

du data driven marketing ?

BH : La data est un moyen, pas une fin, l’enjeu marketing est surtout de commencer par comprendre le

comportement et les usages des clients en temps réel, et non de commencer par penser ‘produit’ ou ‘campagne’.

La Data nous permet cette transformation marketing.

NS : Qu’en est-il de l’automatisation du marketing ? Quels changements sont engendrés par exemple

par le RTB dans les organisations ?

BH : Nous y venons petit à petit, il y a un fort potentiel de rapprochement des données media digitales et

CRM. Les data management Platform sont des projets intéressants.

NS : Quels sont les impacts organisationnels et managériaux que vous observés lors de mise en place de

ce type de projets ?

BH : Les projets Big Data sont centrés sur le client et la data du client. Il est donc essentiel d’organiser en

interne des actions ayant une approche transverse. Cela nécessite d’évoluer d’une organisation en silo vers un

mode projet. C’est tout le sens des nouveaux projets transverses chez Orange France (dits projets

‘accélération’), dont le projet transverse accélération ‘Big Data’.

Interview: Stephen Tarleton - Vice President of Sales and Marketing at People

Pattern.

NS: Could you introduce, your profile, what’s your study and what is your role and your prerogative

in people pattern team?

ST: Stephen Tarleton, I’am the Vice President of Sales and Marketing at People Pattern. I lead our Sales and

audience outreach efforts at People Pattern. I started my career at in engineering at Alcatel, but eventually

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moved to client-facing roles. After receiving an MBA at the University of Texas at Austin, I held roles

Marketing and Sales roles at Deloitte, Dell and Bazaarvoice prior to joining People Pattern last year.

NS: How do you define Big Data? Wherein Big Data is a part of your strategy and value proposal?

We approach data in a decidedly academic manner at People Pattern, using data science methodologies like

Natural Language Processing and Machine Learning. We actually try not to use the term “Big Data” because

it means different things to different people. So, rather than focusing on Big Data, we focus on transforming

unstructured data from many sources into meaningful structured data for our clients.

NS: What is your offer? What services do you propose to your clients company?

ST: People Pattern offers clients the ability to understand their audiences from the ground up, for uses such

as lead generation, market research and content creation. Our platform stitches together enterprise and social

audience data to create a robust people-based dataset for our clients.

NS: How do you work with clients? How are your profile clients in company, more marketing or

Informatics?

ST: At People Pattern we have built an advanced analytics platform to build, classify and segment audiences

for marketers. We also offer a services layer for clients who have specific needs, such as building bespoke

personas and designing data-based marketing strategies.

NS: What are the link that you make with marketing, collected and analyze data?

ST: The People Pattern platform analyzes audience conversations to arm marketers, content management

professionals and market researchers with the tools to understand the people that make up their audiences.

NS: How do you collect data from clients? Do you target specifics informations from the customer

journey?

ST: The People Pattern platform ingests proprietary and open social audience data and then applies data

science techniques to segment the audience personas based on demographics, interests, sentiment and

influence. The information forms the basis of detailed, data-driven personas, which provide clients with an

alternative to simple, vertical-based, segmentation.

NS: What are the marketing objective with all these data collected?

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ST: With data-informed audience insights, clients are able to target high-value personas with relevant,

engaging content.

NS: What’s the difference between your service and classical media targeting?

ST: We use a bottoms-up approach and identify the individuals in an audience before making definitive

assumptions about the audience as a whole. We create segments / personas by clustering individuals within

an audience. With data-based insights provided by the People Pattern platform, clients are able to directly

market customized content to persona groups based on demographic and psychographic information.

NS: In what Data driven marketing could be a business opportunity to marketing departments?

ST: Our data-driven marketing provides marketing departments insights into who is talking about their

brand, what they are saying, how they are saying it and how to best engage with them.

NS: What are the main trends in big data marketing?

ST: We are seeing a trend of marketers using data to add context to their marketing efforts. By having more

data about their customers and prospects they can better target them with relevant content and offers,

providing more value from messaging.