Choose one of the following exercises: 1. Using Tags vs6.1 run a query on a Twitter topic you may be interested in. Upload the csv file in R and run one of the following: 1. Topic modelling 2. Sentiment Analysis 3. Word Frequency Write a 500 word blog post explaining the charts and what these mean from a strategy perspective. IMPORTANT NOTICE: please add charts and explain them. Remember to speak like a social media marketer so focus on the element of “conversation” and “potential response”. OR 2. Discuss the concept “mind genomics” and how social media is redefining the ways in which strategy is being informed. In your answer, please add relevant discussions on the role of: – Bayesian Statistics – Real Time Monitoring Support your answer with examples and refer to the book “Mind Genomics” that can be found in the section “resources” as well as on Canvas under the unit “Week 10” that focuses on hard and soft metrics. ( I uploaded it down here )
mind_genomics_a_guide_to_data_driven_marketing_strategy.pdf
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SPRINGER BRIEFS IN BUSINESS
Veljko Milutinovic
Jakob Salom
Mind Genomics
A Guide to DataDriven Marketing
Strategy
123
SpringerBriefs in Business
More information about this series at http://www.springer.com/series/8860
Veljko Milutinovic · Jakob Salom
Mind Genomics
A Guide to Data-Driven Marketing Strategy
13
Veljko Milutinovic
Scientific Advisory Board
Maxeler Technologies
London
United Kingdom
Jakob Salom
Mathematical Institute
Serbian Academy of Sciences and Arts
Belgrade
Serbia
ISSN 2191-5482
ISSN 2191-5490 (electronic)
SpringerBriefs in Business
ISBN 978-3-319-39731-3
ISBN 978-3-319-39733-7 (eBook)
DOI 10.1007/978-3-319-39733-7
Library of Congress Control Number: 2016941600
© The Author(s) 2016
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
or information storage and retrieval, electronic adaptation, computer software, or by similar or
dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publication does not imply, even in the absence of a specific statement, that such names are exempt
from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authors or the editors give a warranty, express or implied, with respect to the material contained
herein or for any errors or omissions that may have been made.
Printed on acid-free paper
This Springer imprint is published by Springer Nature
The registered company is Springer International Publishing AG Switzerland
Foreword by Howard Moskowitz
Mind Genomics is a new, data-driven approach to understand the world of the
everyday. The organizing principle is that knowledge is developed in a pointillist
style, from the intimate, profound, and comprehensive understanding of specific
topics, be these situations (e.g., eating a breakfast), topics (e.g., ethics and practice
of digital piracy), products (e.g., a yogurt), and so forth.
Mind Genomics is best conceived as a way to discover and then to deeply
understand what aspects of the topic are important to people, how people differ in
the way they respond to these aspects of the topic, and how one goes about identifying these different viewpoints for specific topics.
What this means is that it is now possible to understand people in a deep way
and communicate with people in the language that they understand, in a language
which motivates them. Whether we are dealing with education (how students want
to learn), or medicine (how patients need to interact with the medical community),
or commerce (how to advertise to customers), we can base what we do on solid,
reproducible, publishable, and archival science. In other words, we can base it on
the standard scientific principles that professionals around the world accept.
Media power is shifting from the traditional to the digital landscape.
Communication such as the increased use of social media and search engines to
target particular groups of people becomes more important. Content personalization is another important trend. Companies need to create personalized content
tailored to a customer’s unique interests to capture attention. Programmatic advertising has become much more targeted messaging as marketers have the ability to
pinpoint specific consumer attributes.
These trends are led by technology development and changing consumer
behavior. Today, marketers spend more time on pulling in customers, not pushing
customers. Customers do not want overwhelming information. They just want the
information they need.
All of these trends need to address the right message to targeted group of people. Mind Genomics can help to identify what to say, how to say it, and to whom.
It will help businesses to improve the fundamental return. Our data suggest that
the increases in return can be of the order of 10 % to a high as 100 %. It is not the
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Foreword by Howard Moskowitz
magic of Mind Genomics as a machine. It is rather that the business, the school,
and the medical system all improve their performance when they know exactly
what to say to each person, appropriate for the person’s interests, and well-being.
Howard Moskowitz
Preface
Mind Genomics was first introduced by Dr. Howard Moskowitz, an alumnus of
Harvard University and the father of Horizontal Segmentation—a widely accepted
business model for targeted marketing and profit maximization. He is a Sigma Xi
Laureate and is portrayed by many as a genius (Google: Malcolm Gladwell about
Howard Moskowitz). Later on, Howard teamed up with Mr. Stephen Onufrey,
who spent most of his professional life at IBM, with Stephen contributing important additions to the business aspects of Mind Genomics. It is the talent of the
two of them, working in synergy, which created the major successes of Mind
Genomics, mostly in the USA. It is the hope of the authors of this book to bring
the worldwide attention to the promise of Mind Genomics, for business in particular, and society in general. The authors of this book, Dr. Veljko Milutinovic and
Mr. Jakob Salom, are thankful to Dr. Howard Moskowitz and Mr. Stephen Onufrey
for sharing with them the history and specifics about early developments in Mind
Genomics and for generously giving them the opportunity and support to do
research in the domains of engineering and scientific aspects of the overall system.
Consequently, this book is divided into two parts, one on the major engineering
aspects and the other on the related scientific research. The epilogue of the book
gives pointers to the most recent work of Dr. Howard Moskowitz and Mr. Stephen
Onufrey, related to issues of interest for business and society.
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Contents
1 Introduction to Basic Concepts of Mind Genomics. . . . . . . . . . . . . . . .
1.1 Phases of Mind Genomics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.1 Phase #1: Building of the MicroScience (Stage #1). . . . . . .
1.1.2 Phase #1: Polling of the Market (Stage #2). . . . . . . . . . . . .
1.1.3 Phase #1: Analysis of the Polling Results (Stage #3). . . . . .
1.1.4 Phase #1: The Broadband Marketing Campaign
with the Best Three Slogans (Stage #4). . . . . . . . . . . . . . . .
1.1.5 Phase #2: Typing of the Customer (Stage #1) . . . . . . . . . . .
1.1.6 Phase #2: Determination of the Target Action
(Stage #2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.7 Phase #2: Analysis of the Profit (Stage #3) . . . . . . . . . . . . .
1.1.8 Phase #2: Building the Experience Base (Stage #4) . . . . . .
1.1.9 MicroScience Revisited (Phase #1 Revisited). . . . . . . . . . .
1.1.10 Customer Typing Revisited (Phase #2 Revisited) . . . . . . . .
1.2 Horizontal Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Definition of Some Terms and a Conclusion. . . . . . . . . . . . . . . . . . .
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Research Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1 Media Understanding Oriented Research. . . . . . . . . . . . . . . . . . . . . .
2.1.1 Understanding Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2 Understanding the Still Image. . . . . . . . . . . . . . . . . . . . . . .
2.1.3 Understanding the Moving Image. . . . . . . . . . . . . . . . . . . .
2.2 Sentiment Detection Oriented Research. . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Sentiment Analysis on the Social Networks. . . . . . . . . . . . .
2.2.2 Sentiment Analysis Using Neuro-Economy. . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 Case Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1 Case #1—Veterinary Hospital and Pet Care Insurance . . . . . . . . . . .
3.2 Case #2—Louisiana Coast Spill. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Case #3—Higher Education Student Enrolment. . . . . . . . . . . . . . . .
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Contents
3.4 Case #4—Perfect Healthy Breakfast Cereal . . . . . . . . . . . . . . . . . . . 86
3.5 Case #5—Banking Industry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4 The Infrastructure Framework for Mind Genomics. . . . . . . . . . . . . . . 109
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5 An Efficient Technological Support for Mind Genomics. . . . . . . . . . . . 111
6 The Epilogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.1 Further Readings About Dataflow Supercomputing,
Part #1: Educational Books and Papers. . . . . . . . . . . . . . . . . . . . . . . 115
6.2 Further Readings About Dataflow Supercomputing,
Part #2: Research Papers and Books . . . . . . . . . . . . . . . . . . . . . . . . . 115
About the Authors
Veljko Milutinovic
Life Member of the ACM
Fellow Member of the IEEE
Member of Academia Europaea
Member of the Serbian Academy of Engineering
Member of the Scientific Advisory Board of Mind Genomics
Member of the Scientific Advisory Board of Maxeler Technologies
Veljko Milutinovic received his Ph.D. from the University of Belgrade, spent about
a decade on various faculty positions in the USA (mostly at Purdue University),
and was a codesigner of the DARPA’s first GaAs RISC microprocessor. Later
he taught and conducted research at the University of Belgrade at the School
of Electrical Engineering, the School of Mathematics, the School of Physical
Chemistry, and the School of Business Administration. His research is mostly in
DataMining algorithms and DataFlow computing, with the stress on mapping of
data analytics algorithms onto fast energy efficient architectures. For 7 of his books,
forewords were written by 7 different Nobel Laureates with whom he cooperated on
his past industry sponsored projects. He has over 40 IEEE or ACM journal papers,
over 400 WoS citations, and over 4000 Google Scholar citations.
Jakob Salom
Consultant at The Mathematical Institute of the Serbian Academy of Sciences
Consultant to the Vienna Congress COMSULT
Consultant at Hypo Alpe-Adria-Bank a.d. Belgrade, Serbia
Consultant at Alfa Bank AD Skopje, Macedonia
Vice President for Strategic Development of IPSI, Belgrade, Serbia
President of the Jewish Community of Belgrade, Serbia
Jakob Salom is the President of the Jewish Community of Belgrade, Serbia, consultant at the Mathematical Institute of the Serbian Academy of Sciences, consultant at Hypo Alpe-Adria-Bank a.d. Belgrade, Serbia, consultant at Alfa Bank
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About the Authors
AD Skopje, Macedonia, and the Vice President for Strategic Development of
IPSI, Belgrade, Serbia. He graduated from the School of Electrical Engineering,
University of Belgrade (1975). During his tenure in the banking industry, his
teams developed successful statewide payment system solutions, retail banking applications, and a number of sophisticated e-banking solutions. In recent
years, he has been working as a consultant in the fields of data warehousing, data
archiving, and data mining for banking industry. His innovations were successfully applied in business development processes. He is now involved in banking and insurance industry projects using Mind Genomics and Dataflow Maxeler
Technologies. His current research is mostly in business processes for the Internet
and the ubiquitous computing.
About Howard Moskowitz
Howard Moskowitz is a well-known experimental psychologist in the field of
psychophysics (the study of perception and its relation to physical stimuli) and an
inventor of world-class market research technology. Among his important contributions to market research is his 1975 introduction of psychophysical scaling and
product optimization for consumer product development. Whereas these methods
are standard and well accepted today, they required a massive culture change in
the 1975 business community. He is best known for the detailed study he made
of the types of spaghetti sauce and horizontal segmentation. By providing a large
number of options for consumers, Moskowitz pioneered the idea of intermarket
variability as applied to the food industry. In the 1980s, his contributions in sensory analysis were extended to health and beauty aids.
He is the CEO of iNovum Inc. as well as president of Moskowitz Jacobs Inc.,
a firm he founded in 1981. Dr. Moskowitz graduated from Harvard University in
1969 with a Ph.D. in experimental psychology. Prior to that, he graduated from
Queens College (New York), Phi Beta Kappa, with degrees in mathematics and
psychology. He has written/edited more than 25 books, has published well over
400 articles, and has lectured in the USA and abroad, and now, he serves on the
editorial board of major journals.
His book with co-author Alex Gofman, Selling Blue Elephants, demonstrates
and popularizes how IdeaMap (iNovum’s flagship product) creates new products
and messages from areas as diverse as credit cards, jewelry offers, presidential
messaging during election years, stock market communications, and transnational
innovation.
Dr. Moskowitz has won numerous awards, among them the Scientific
Director’s Gold Medal for outstanding research at the US Army Natick
Laboratories, and the 2001, 2003, and 2004 awards by ESOMAR (European,
now World Society Of Market Research) for his innovation in web-enabled,
self-authored conjoint measurement and for weak signals research in new trends
analysis and concept development. In 2004, Dr. Moskowitz was elected as an
IFT Fellow and was also awarded the “David R. Peryam Award,” from ASTM,
in recognition of outstanding contributions to the field of basic and applied
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About Howard Moskowitz
sensory science. In 2005, he was awarded the Charles Coolidge Parlin Marketing
Research Award for his substantial contributions and dedication to the advancement of marketing research practices. In 2006, he was the recipient of the ARF
Research Innovation Award and The Market Research Council Hall of Fame
Award. Moskowitz Jacobs Inc’s Mind Genomics(R)/IdeaMap(R).net, received the
2012 Edison Bronze Award for New Product Applied Technology Research Tool.
The awards recognize ideas at the forefront of new products, services, marketing,
design, and innovation.
Introduction
Books like this one are targeting different audiences: engineers, business people,
politicians, etc. We have to look at what motivates each audience. Engineers, academics, and scientists are motivated by intellectual content and explanation. If the
process is not masterfully crafted, they find no elegance in the result, even though
solid measured results are in place. The engineer/academician/scientist focuses on
the design and process, not the results. He/she typically does not worry about the
ultimate financial viability of an endeavor. On the other hand, business people and
politicians are results oriented. As part of the results, they want to insure that the
process is as efficient as possible to keep costs of getting to the end in check in
order to produce a cost/benefit analysis in the “black”—profitable. They will want
to know what they need to do in order to make it happen, but they do not care
about the science or process “under the covers” but do care about the process that
their people will have to use in order to bring this to life. They begin with the end
in mind.
The conclusion is that there are two separate writing work products which target two separate audiences. One target group consists of people who want to intellectually understand how something works and are not concerned in the financial
viability of the idea. The other target group consists of those who want to understand in order to use the idea, and to get someone to embrace their product, service, or idea. This book is aimed for the first target group.
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Chapter 1
Introduction to Basic Concepts of Mind
Genomics
Abstract This chapter gives a detailed overview of the Mind Genomics process,
from the implementation point of view. It defines all relevant terms of interest for phases ONE and TWO of the process, and it marks essential differences
between the usual approaches to targeted marketing and the Mind Genomics based
approach to targeted marketing. Issues are discussed like: How to prepare the polling campaign that determines the mind types on the market, and how to do all the
steps needed to place new prospects (potential customers) into one of the existing
mind types.
Keyword Mind Genomics process · Targeted marketing · The polling campaign · Mind types · Linear regression · Classification
Marketing can be broadband or targeted. Broadband marketing means that all
customers get the same message, via radio or TV, Internet or newspapers, billboards or taxicabs. Targeted marketing means that each customer receives a different message, tuned to the real needs and mental characteristics of that particular
customer.
Targeted marketing is nothing new; it existed for centuries. For example, in
medieval times, in a village shop, where the owner knows all the villagers, which
is a limited number, to some of them the owner offers guns, and jewelry to the
others. What is new, and originated with the appearance of the Internet, is targeted
marketing spanning a huge amount of persons unknown to the vendor. This paradigm is referred to, by many, as modern targeted marketing.
Most approaches to modern targeted marketing imply the following drawbacks:
a. At the entry into the system, customers are asked to fill forms.
This is time consuming and represents an attack on the privacy of individuals.
b. During the exploitation, customers are tracked.
© The Author(s) 2016
V. Milutinovic and J. Salom, Mind Genomics, SpringerBriefs in Business,
DOI 10.1007/978-3-319-39733-7_1
1
2
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Introduction to Basic Concepts …
For example, if bacon is purchased in one store, than, when the customer
appears at the web portal of another store in the same chain, he/she is again
offered bacon, which is annoying.
c. When placing offers to customers, the system is based on socioeconomic principles. Rich customers are offered one set of things, poor the other.
Mind Genomics, which is the main subject of this book, does not have any of the
above mentioned drawbacks. It does not ask the customers to fill forms at the entry
into the system, it does not track the customers, nor it divides them along the socioeconomic division lines; yet, it enables the vendor revenues to be at least two
times higher.
Mind Genomics is based on sophisticated math, on the notion of value
exchange on the top of fast Internet, and on DataMining. Mind Genomics is based
on the following three production phases:
a. Micro Science (the phase based on sophisticated math).
b. Customer Typing (the phase based on value exchange and fast Internet).
c. Machine Learning (the phase based on DataMining).
In this context, sophisticated math implies linear regression and related techniques for improving the effects of linear regression. Value exchange implies that
whenever a customer is asked to do something, he/she must receive an immediate
award. DataMining spans all techniques that can help the system self-learn and
adapt …
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