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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
2
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.
3
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.
4
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
5
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
6
7
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 ]
8
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.
9
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 ]
10
(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 ]
11
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.
12
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
13
 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
14
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
15
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
16
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
17
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.
18
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 ]
19
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
20
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
33
Segmentation 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.
 How does big data disrupt marketing : the modification of a marketer’s job
 How does big data disrupt marketing : the modification of a marketer’s job
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 How does big data disrupt marketing : the modification of a marketer’s job
 How does big data disrupt marketing : the modification of a marketer’s job
 How does big data disrupt marketing : the modification of a marketer’s job
 How does big data disrupt marketing : the modification of a marketer’s job
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 How does big data disrupt marketing : the modification of a marketer’s job
 How does big data disrupt marketing : the modification of a marketer’s job
 How does big data disrupt marketing : the modification of a marketer’s job

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How does big data disrupt marketing : the modification of a marketer’s job

  • 1. 1 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
  • 2. 2 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.
  • 3. 3 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.
  • 4. 4 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
  • 5. 5 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
  • 6. 6
  • 7. 7 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 ]
  • 8. 8 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.
  • 9. 9 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 ]
  • 10. 10 (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 ]
  • 11. 11 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.
  • 12. 12 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
  • 13. 13  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
  • 14. 14 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
  • 15. 15 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
  • 16. 16 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
  • 17. 17 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.
  • 18. 18 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 ]
  • 19. 19 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
  • 20. 20 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 33 Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression http://www.sciencedirect.com/science/article/pii/S0148296306002323
  • 34. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.