A Conceptual Framework for Marketing Intelligence

Abstract

Nowadays, customers demand faster customer service, and expect that organizations know them and provide appropriate services and recommendations for products quickly. Many organizations are reacting to these market needs by driving toward pervasive business intelligence, augmenting traditional business intelligence (BI) with the ability to capture, interpret and act immediately on data to make faster decisions. With pervasive business intelligence, organizations data warehouse changes to a system that can proactively and reactively interact with the business stakeholders, and it provides appropriate decision options to help marketers to respond and take action based on knowledge discovery in current integrated data. This work based on the literature review presents the Pervasive Business Intelligence state of the art and related areas, leaving open for proposing a conceptual framework to guide the development of activities of Marketing Intelligence.

Full text

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Abstract—Nowadays, customers demand faster customer
service, and expect that organizations know them and provide
appropriate services and recommendations for products
quickly. Many organizations are reacting to these market needs
by driving toward pervasive business intelligence, augmenting
traditional business intelligence (BI) with the ability to capture,
interpret and act immediately on data to make faster decisions.
With pervasive business intelligence, organizations data
warehouse changes to a system that can proactively and
reactively interact with the business stakeholders, and it
provides appropriate decision options to help marketers to
respond and take action based on knowledge discovery in
current integrated data. This work based on the literature
review presents the Pervasive Business Intelligence state of the
art and related areas, leaving open for proposing a conceptual
framework to guide the development of activities of Marketing
Intelligence.
Index Terms—Business intelligence, marketing intelligence,
pervasive business intelligence, database marketing.
I. INTRODUCTION
The pervasive impact of business computing has made
information technology (IT) an essential part of regular
operations and a strategic element or key to all organizations.
It is not difficult to realize that organizations have
accumulated large amount of data. This data has different
origins and reaches the organizations through a range of
channels. It is strategically important to make these data
available for decision making, even because customers
demand faster customer service. The growing volume of data
generated every day in organizations and the crescent
competitiveness of the market, leads to the need to use tools
capable of generating knowledge from stored data. In the
global market where competition is fierce, companies
increasingly need to reduce their profit margins to remain
competitive. Thus, it is essential to use the information
proactively.
The fierce global competition leads organizations to
consistently obtain accurate information for decision-making
in order to sustain its competitive advantage. It's crucial for
an organization to be proactive, acting before its competitors,
by having a constantly updated vision of market development,
then the Information processing becoming the platform that
enhancing competitive advantage [1].
Manuscript received November 23, 2012; revised December 25, 2012.
This work was supported in part by the Isla - Superior Institute of Languages
and Administration of Leiria, Portugal.
Teresa Guarda is with Department of the Isla - Superior Institute of
Languages and Administration of Leiria, Portugal (e-mail:
tguarda@gmail.com).
Manuel Filipe Santos is with University of Minho, Guimarães, Portugal
(e-mail: mfs@dsi.uminho.pt).
Filipe Pinto is with the Polytechnic Institute of Leiria, Portugal (e-mail:
fpinto@ipleiria.pt).
The analysis of large volumes of data is impossible
without resorting to the appropriate software tools, making it
essential to develop frameworks that help to automatically
and intelligently, analyzing, interpreting and correlating data,
enabling the development and selection of strategies for
action [2]. In order to assist companies in this exploration of
data, concepts and tools for organizing information are
critical, highlighting the Pervasive Business Intelligence
(PBI) and Marketing Intelligence (MKTI) as pillars to
support the decision-making. The economic decline is
impelling organizations to examine ways of retaining
customers, speed up their services, spending less capital be
more efficient regarding their budgets, and observing
regulations. Business intelligence (BI) is the ability to access
data from multiple sources in an organization and deliver it to
appropriate business users for analysis. Manage the
performance of the business means know what questions to
ask and have the facts at hand at time to answer them, and this
is what business intelligence delivers. With pervasive
business intelligence, organizations data warehouse changes
to a system that can proactively and reactively interact with
the business stakeholders, and it provides appropriate
decision options to help marketers to respond and take action
based on knowledge discovery in current integrated data.
Pertinent and accurate information from relevant and reliable
sources entails to be successfully processed. This implies that
a company needs to be confident it has the right information,
at the right time, and dissembled to right people [3].
We propose a framework to guide the development of
activities of Marketing. A framework of satisfying
information needs for decision-making is complex and is
compound by different activities to be exploited. This paper
is organized as follows: after this introductory part we
present related background concepts. Then, the main
contribution is presented in terms of a framework proposal.
Finally we draw some conclusions.
II. BACKGROUND CONCEPTS
A. Business Intelligence
An increasing number of organizations are making BI
more largely available to all decision makers inside and
outside the organization. Internally, leads to greater
responsibility by all employees and greater management
stability. Externally, relation-ships with supplier and partners
can be reinforced through effective sharing of key
performance indicators for mutual benefits. However, it is
not easy to implement [4] in SMEs because of the following
factors: high price; high requirements for a hard-ware
infrastructure; complexity for most users; irrelevant
functionality; low flexibility to deal with a fast changing
dynamic business environment; and low attention to
difference in data access necessity in SMEs and large-scaled
A Conceptual Framework for Marketing Intelligence
Teresa Guarda, Manuel Filipe Santos, Filipe Pinto, Carlos Silva, and João Lourenço
International
Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 2, No. 6, December 2012
455
DOI: 10.7763/IJEEEE.2012.V2.163

enterprises. But it’s more important (and difficult) than ever
today for organizations to make the right customer decisions.
Companies know that the ability to frequently make the right
customer decisions is essential to profitable growth, risk
management and general performance. Due to
non-controllable factors like fast-moving markets, economic
and regulatory change, and new sources of competition, the
right decision isn’t a peaceful matter.
Although Howard Dresner being considered the father of
the term “Business Intelligence” since he used the term in
1989, at the time an analyst at Gartner, Hans Pete Luhn was
the first to use the term “business intelligence” in a paper
with the title “A Business Intelligence System”, published in
1958 by IBM [5]. Dresner was looking for a term to define
the best tools that enabled access to information and
quantitative analysis of the same, and describe the area as
“concepts and methods to improve business decision-making
by using fact-based support systems” [6], for Negash [7]
“Business Intelligence is a data-driven DSS that combines
data gathering, data storage, and knowledge management
with analysis to provide input to the decision process.”, Luhn
defined business intelligence as “the ability to apprehend the
interrelationships of presented facts in such a way as to guide
action towards a desired goal.” [8]. According to Barbieri [9],
the BI can be understood as the use of various sources of
information to define the competitive strategies of an
organization.
Business Intelligence bridges between different systems
and users wishing to access information. Provides an
environment that facilitates access to information needed for
day to day activities, allowing analyze the current situation of
the business and its performance. Systems and BI tools have
a key role in the strategic planning process of organizations.
These systems allow collect, store, access and analyze
organizational data in order to assist decision making [10].
B. Pervasive Business Intelligence
Pervasive Business Intelligence (PBI) emerges as a natural
evolution of the application of BI in organizations, with a
movement in two directions, vertical (top-down) and
horizontal (cross-Departments), with an application from the
strategic level to the operational level. There are various PBI
definitions, is the ability to deliver integrated right-time data
warehouse information to all users, providing the necessary
visibility, insight, and facts to make decisions in all business
processes [11]; PBI is the improvement of the strategic and
operational decision-making capabilities of an organization,
through the design and implementation of it as a whole
(organizational culture, business processes, and technologies)
[12].
The implementation of PBI in organizations is supported
in applications that access the data in real time, supporting
the actions of CRM and marketing campaigns. The
application of PBI is enhanced when the front-line employees
are in contact with the client and can generate new sales
opportunities, up-sell and cross-sell [11]. PBI aims to
integrate and align all processes to enable the delivery of
relevant information to users who need to support decision
making. According IDC there are five key factors with large
influence on BI pervasiveness [12]:
1) Design quality: users’ expectations about BI solution
components are met.
2) Degree of training: satisfaction level with training on use
of BI tools, and the use of analytics to improve decision
making.
3) Prominence of governance: importance of data
governance and associated data governance policies in
BI system.
4) Nonexecutive involvement: nonexecutive management
involvement in promoting and encouraging use of the BI
tools at the organization.
5) Prominence of performance management methodology:
importance within the organization of a formal
performance management methodology.
C. Marketing Intelligence
According to Mackenna [13], the information technologies
are an essential key component to react to market changes
and satisfy customers, helping marketers in decision making
and implementation of marketing plans. A well-known
example was the application made by a major U.S.
supermarket chain, where it was discovered a universe of
buyers of diapers also bought beer on the eve of the weekend
in which games were broadcast on television. This
knowledge has been used, thus increasing their sale.
The concept of general intelligence and MKTI in
particular has evolved, being seen as a driver for strategy and
market success [1]. A MKTI system is a set of procedures and
sources used by marketers to get their daily information on
relevant developments in the environment in which they
operate [14]. Another definition, MKTI is a system to capture
the information needed for decision making in marketing
[15]. The fundamental purpose of MKTI is to help marketing
managers to take the decisions they face every day in their
various areas of responsibility, including pricing. Huster [16]
define MKTI as the ability to understand, analyze and
evaluate data from internal and external environment, related
to the organization, customers, competitors, markets and
companies to improve decision-making tactical and strategic,
and the integration of competitive intelligence, marketing
research, market analysis and analysis of business and
financial information. The MKTI is a complex process,
whose efficiency affects the quality of marketing decisions,
including pricing [8].
For some authors, the MKTI can be defined as existing
knowledge and prior knowledge about the external operating
environment, obtained by concern opinions, attitudes,
behavior and needs of individuals and organizations within
the context of economic activities, environmental, social and
everyday [21]-[22]. The ultimate goal of every process of
intelligence is to facilitate the decision making that leads to
action [22]. Is the key intelligence that motivates some
authors to introduce the term as a synonym for competitive
intelligence MKTI [16]-[23]. The American Marketing
Association defines marketing [24] as the set of activities and
processes for creating, communicating, delivering and
exchanging offerings with value for customers, partners and
society in general.
Although market research focus often in response to
specific information need or set of needs, intelligence is
indicated as a continuous process of developing a holistic
view of the operating environment, including competitors,
customers and markets. An intelligence process effectively
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contributes to the knowledge base of the organization and
leads to a cumulative organizational learning. Market
research is a well-defined discipline with a long history of
application in the business world, taking many forms and
with the goal of increasing understanding that the company
has in the market and its customers, and this is not MKTI
[16]-[25].
In the future, efforts should be aimed at measuring the
demands of not satisfied consumer, through their behavior,
measuring consumer response to marketing activities, and
analysis customer’s feedback. Allowing identify trends in
consumer tastes, and points of friction between the
organization and customers. With modern technology can be
done on a larger scale, with lower cost [17]. MKTI seeks to
transform data into information, and information into
intelligence. The data are the basis of all structure, from
which we perceive and record a given reality [11].
D. Database Marketing
The use of databases in marketing has given rise to the
concept Database Marketing (DBM) in the late '70s. A
limitation of the interaction of actors, technicians and
marketers of databases, the concept became hostage since
neither the marketers had knowledge in technology, nor the
technical know Database's business.
Another limitation to the proliferation of DBM, is the fact
that many of the projects being conducted within private
organizations, which may explain in part the abundance of
scientific articles published in academic literature dealing
with issues of DBM [26]-[28].
The DBM is now an essential part of marketing in many
organizations. The basic principle of DBM, which is at least
part of the organizations communication with customers, is
direct (consumers) [29]. From this simple beginning has
grown a new discipline, but without the maturity expected by
some authors [30, 31]. Currently the DBM is mainly
addressed by classical statistical inference, which may fail
when data are complex, multidimensional, and incomplete.
The DBM refers to the use of database technology for
supporting marketing activities. Being a marketing
process-driven information technology and managed by the
database, which allows marketers to develop and implement
better programs and marketing strategies.
There are different definitions of DBM with different
approaches or perspectives, showing some improvement
over the concepts [32]. In marketing perspective, "the DBM
is an interactive approach to marketing communications,
which uses addressable media" [31]-[33], or "a strategy that
is based on the premise that not all customers or prospects are
equal, and that the collection, maintenance and analysis of
detailed information about customers and prospects,
marketers can modify their marketing strategies"[34].
Statistical approaches have been introduced, "database
marketing is the application of statistical analysis and
modeling techniques to process individual data sets” [35],
emphasizing some data types. “Put simply, the DBM
involves gathering information about past, current and
potential customers, to build a database that improve the
marketing effort. The information includes: demographics,
what the consumer likes and dislikes, tastes, purchasing
behavior and lifestyle [33]-[36].”
With the advancement of information technology,
processing speed, storage space, and the data flow in
organizations has grown exponentially, suggesting different
approaches to the DBM. “Generally, it is the art of using the
data already collected, to create new ideas to make money”
[37]-[38] or “... save this response, and add other user
information (style life, transaction history, etc..) on an
electronic database, and use it as the basis for customer
loyalty programs in the long term, to facilitate future contacts
and to enable planning of all marketing.” [33], [34], [20] and
“... the DBM can be set to collect, store and use the maximum
of useful knowledge about customers and prospects, to their
benefit and profit [36]-[39].”
Some authors have referred to the DBM as a "marketing
tool oriented databases, being increasingly, the focus of the
strategies of organizations" [40]-[42] (Swift, 2001;
Greenberg, 2002; Cross & Janet, 2004).
All definitions have in common a main idea, the DBM is
the process that uses the data stored in database marketing, in
order to extract relevant information to support marketing
decisions and activities by understanding customers' which
will satisfy their needs and anticipate their desires.
III. CONCEPTUAL FRAMEWORK PROPOSAL
The MKTI process is a complex approach, being important
find a structured way for information processing, and being
crucial that it are available in time for the decision-making
process. It is also essential that the right people have access to
information throughout the process of collection and analysis,
provide feedback to the intelligence (information analysis)
required. So, as it is vital to know where the information
should be collected and how it should be organized. Which
leads to the formulation of the following problem: How can
the process MKTI are organized in a systematic way to
improve the process of decision making?
To solve the problem, using a top-down approach, this will
be fragmented into three problems:
1) How to organize the flow of information and intelligence
within the organization in order to facilitate the process
of decision making?
2) How must be organized and managed internally MKTI?
3) How can contribute to the process MKTI customers,
information sources (internal and external), stakeholders
and competition?
A process of satisfying information needs for
decision-making is complex and is compound by different
activities to be exploited. Our challenge is to propose a
conceptual framework to guide the development of activities
of Marketing Intelligence based in data modeling, as an
add-on to PBI. Pervasive business intelligence provide
support to managers decision-making with the tactical and
strategic information they need for understanding, managing,
and coordinating the processes in organizations [18].
It is intended evaluate, structure the process MKTI to
improve decision making. The benchmarking process MKTI
is measured either by the ability of logical abstraction on the
environment and internal factors in order to identify and
collect relevant data for their analysis. The process MKTI is a
set of procedures and methods planned for collecting,
analyzing and representation of information for use in
making marketing decisions [20]. The design process MKTI
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457

considers the type of information stored in the system, and
how decision-makers want to receive the information.
Managers can have direct access to this information (reports),
through their computer terminals.
The MKTI framework assessment process is measured
either by the ability to identify and collect data relevant to
analysis, and extracting relevant knowledge to support
decision making in marketing. The MKTI process comprises
two primary activities: data in and data out, and a set of
procedures and methods for collecting, analysis and
representation of information for use in making marketing
decisions (see Fig. 1). Getting data in, usually is referred as
data warehousing, and includes the flow of data from a set of
source systems and its integration into a data warehouse. The
source systems represent internal and external data. We can
say that getting data in is the most challenging aspect of BI,
and it can requires about 80 percent of the time and effort and
generating [19]. Getting data in delivers (data out) is the
focus of attention of organizations, and consists of business
users and applications accessing data from the data
warehouse to perform enterprise reporting, OLAP, querying,
and predictive analytics [19].
The MKTI process consists of a set of procedures and
methods planned for collecting, analysis and representation
of information for use in making marketing decisions [20].
We propose five basic processes for MKTI:
1) Planning,
2) Collection,
3) Analysis,
4) Representing,
5) Projections.
Fig. 1. Marketing intelligence conceptual framework
In planning process are defined the objectives and the
necessary information for marketers marketing decisions.
Then, the collection process, extract, transforms and load
organization internal and external data sources, that include
CRM, prospects data, market data and competition. The
analysis process is the more complex and difficult, all
activities should be developed in order to analyze the data,
looking for patterns, and loaded organized and coded
information on marketing data mart, as subset of the data
warehouse. The representing process, access mart data and
apply marketing metadata models for representing
information from marketing perspective. In the projection
process, results will be distributed to marketers for review
and posterior feedback if needed.
In MKTI conceptual framework one of the components
presented is the metadata. The metadata model describes
fields, values, sizes, ranges, field’s definitions, data owners,
latency, and transformation processes. Metadata provides
transparency as data moves from sources to the warehouse to
end users [19].
IV. CONCLUSIONS
BI is a management concept that refers to a set of programs
and technologies that provide features \ capabilities for
collecting, analyzing and accessing data on processes of
organizations. In any organization, the main objective of BI
is to assist in decision making, timely and at all levels of the
organization.
With the intensification of competition between
companies in open markets, organizations must learn about
themselves and to the market, through the collection and
analysis of data.
The strategic information is seen as a key resource for
success in the business, this being provided by Marketing
Intelligence.
The MKTI is a complex process that goes from the
collection of data from the organizations environment, until
the generated quality information to assist marketing and
strategic decision making. Organizations must to avoid
invade customers with the highest rankings, and the
marketers must remember that customers with low rankings
should not be neglected, but instead should be cultivated to
become better customers.
The management of the future means not only being able
to anticipate what will happen outside the organization, but
also be able to represent the events through their own actions.
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Teresa Guarda was born in Leiria, Portugal in 1966.
She is a professor of Professor of management
information systems, project management, project
coordination, and technical design and interpretation
projects in ISLA - Superior Institute of Leiria,
Portugal. Graduate in informatics management by Isla
–Superior Institute of Lisbon (1990), and master in
systems and information technology by University of
Coimbra (1999). At present, she is a PhD student in the
Department of Information Systems at University of Minho, Guimarães,
Portugal.Her research interests include pervasive business intelligence,
marketing intelligence, data mining, process mining and knowledge
discovery.
Manuel Filipe Santos received his Ph.D. in Computer
Science (Artificial Intelligence) from the University of
Minho (UMinho), Portugal, in 2000. He is associate
professor at the Department of Information Systems,
UMinho, teaching undergraduate and graduate classes
of Business Intelligence and Decision Support
Systems. He his also researcher at the Business
Intelligence Group (big.dsi.uminho.pt) of the R&D
Algoritmi Centre, with the current research interests:
Business Intelligence and Decision Support Systems; Data Mining and
Machine Learning (Learning Classifier Systems); Grid Data Mining.
He participated in various R&D projects, being Principal Investigator of 2
projects,namely: Intcare – Intelligent Decision Support System for Intensive
Care Medicine, Gridclass – Learning Classifier Systems for Grid Data
Mining. He supervised 13 MSc theses and 2 PhD theses. Currently he is
supervising 9 PhD students. Co-organized the EPIA 2007 – 13th Portuguese
Conference on Artificial Intelligence. Reviewer of several conferences (e.g.
AAMAS, EPIA, ICEIS, ICAART,MEDI) and journals (e.g. European
Journal of Operational Research, Intelligent Decision Making Support
Systems); Co-organizer of the Knowledge Discovery and Business
Intelligence - KDBI 2009 and 2011 thematic track of EPIA; WISA/CISTI
2011 and Intelligent Systems/ESM 2011.
Filipe Mota Pinto received his PhD in Database
Marketing and MSc in Information Systems, he held
positions at Repsol and Cepsa in Portuguese
(Responsible area cards). Currently integrates the
Director of the Technological Park of Óbidos
(representative IPL) and is a lecturer in the
Department of Computer Engineering at ESTG IPL. It
is also Coordinator of Graduate Web Marketing.
Research field: Computer and Information Science
(Database Marketing, Ontologies and Data mining).
Carlos Silva was born in Leiria in 1970, Phd student
in Management (Strategy) since 2009 at Universidade
Aberta, master in International Economics (1996) by
ISEG (Lisbon, Portugal) and graduate in International
Relations (Economic) in 1993 by Lusiada University
(Lisbon). He is the Dean of ISLA – Leiria (higher
education institute), teacher of Organizations
Management and Marketing and the Coordinator of
the Management department. Dr. Silva is a member of
the Wade World Trade, a member of Talentus and has is also a member of
OSMTH International.
João Lourenço degree in Business Administration in
IT Management and the School of Scientific
Organization of Work of the School of Languages and
Administration Lisbon.; PhD student in Innovation
Management and Planning at the Faculty of
Economics, University of Algarve. Professor of
Superior Institute of Leiria, in Management and
Business Strategy.
Advisory Board Member of the DARE Program AIMinho - Minho Industrial
Association; Senior Consultant in Management and Business Strategy,
Senior Partner of J. Lourenco Borges - Technical Consultants Associates,
Ltd., Trainer Consultant Training Programs-Action IAPMEI - Institute for
Support to Small and Medium Enterprises and Innovation and the AIP -
Portuguese Industrial Association.
International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 2, No. 6, December 2012
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