Description
The Border Between Business Intelligence And Psychology- Segmentation Based On Customer Behavior
THE BORDER BETWEEN BUSINESS INTELLIGENCE
AND PSYCHOLOGY- SEGMENTATION BASED ON
CUSTOMER BEHAVIOR
PhD Student Codrin POPA
Politehnica University of Bucharest
MD Iulia BERTEA
Academy of Economic Studies Bucharest
Abstract:
In today’s economy, marketers have been facing two challenging trends:
fierce competition between companies offering essentially similar products,
and dealing with customers that are increasingly informed and demanding,
but less and less loyal. Under these conditions, it has become imperative for
managers and for marketing professionals to invest in business intelligence
in order to find patterns in the consumers’ behavior that could predict their
future buying decisions. In this report we have presented how Decision
Support Systems, data analysis and customer segmentation can help
companies to know their customers better in order to predict (and influence)
their future actions. At the same time, we have argued that Business
Intelligence should meet psychology and neurology halfway, and accept that
there is a very high emotional subconscious component that produces a high
degree of unpredictability in consumers’ behavior.
Keywords: DSS, business intelligence, consumer behavior, segmentation,
buying decision process
Introduction
Mass marketing via mass media
typically produces relatively low returns
(response rates less than 1%), if they're
measured at all. Many firms have turned
to targeted marketing to increase
response rates. As the level of
marketing sophistication and "science"
increases in the marketing process,
response rates can rise dramatically.
Simple product marketing with basic
demographic information can increase
response rates from 3% to 4%.
Segment-driven marketing based on
customer value can help target high-
value customers, but doesn't increase
effectiveness tremendously. Needs-
based marketing involving the customer
life stage and lifestyle can improve
response rates up to 20% or better.
Customer-driven marketing based on
events triggered by customer
interactions/transactions can improve
response rates up to 30% or more.
Customer segmentation has
moved through numerous phases
categorized into three general
approaches.
Product-based - This approach
segments customers according to what
they’re buying.
Value-Based - In recent years,
enterprises have increasingly focused
on growing their understanding of
customer profitability, enabling the
creation of customer value-based
segments such as "gold" and
"platinum", or high-net-worth individuals.
This approach segments customers
according to who is buying.
Needs-Based - This is driving
growing interest in segments based on
customer needs, effectively creating
segments based on why customers buy
certain products.
From a needs-based point of view,
companies spend millions every year to
research consumers’ preferences and
buying intentions and, based on
research results, they spend other
millions to develop new products,
launch special offers or open new sales
outlets. And, more and more often, the
new products or special offers end in
failure – marketing studies have shown
that 80% of all new products and
services fail within 6 months or bring
much lower sales than the anticipated
profit (Zaltman, 2007, p.29). Why didn’t
the consumers buy the product about
which they had said in a focus group
that they would buy it if it existed? What
is influencing and determining people’s
buying behavior? Is the buying decision
process 100% rational, as we would like
to think because we wish to be able to
explain and predict it? Do managers
have to understand not only the
dynamics of clients’ conscious cognitive
process, but also and especially the
unconscious one? (Zaltman, 2007,
p.31)
To deepen the concepts behind
the customer behavior and
segmentation the intrinsic link between
the temporal evolution of the marketing
and information systems must be
emphasized.
Decision Support Systems
In the process of finding solutions
and taking decisions that will lead in a
reasonable manner to proper results,
the first place consists of the searching
process based on strategies put into
work in the voluntary selective attention
that involve restricting the "search
area", as the evolving of the appropriate
development of searching process.
Management decision is the choice of
action for one or more goals.
R.T. Clemen remarks that the
decision context is the framework of
events that determine the set of
objectives that actually matters (and
nothing more or less) for a decider at
the time of drafting the decision, even if
the values remain relatively unchanged
(Clemen, 1996).
Even if some simple patterns could
be achieved based on generic or
structured data, a depth and systematic
analysis that involves semi-structured or
unstructured data could show more
complex patterns and trends that
involve using of a specific class of
information systems – Decision Support
Systems (DSS), systems for collecting
and analyzing the information in order to
assist managerial decision.
The objective of a DSS is to
minimize the effects of the limitations
and constraints in solving of a large and
complex area of decisional problems by
implementing automatic processes of
decision support (Filip, 2004, p.5).
It is important to notice that even if
the decision support systems have an
important role in the processes of
decision the manager should initiate the
process by choosing the appropriate set
of questions, only in this way problems
are identified and resolved.
Business intelligence is a term
used to describe an integrated set of
methods, applications and processes
used to capture, collect, integrate and
analyze data to present information
used for management decision; is
referring to modern data driven decision
support systems using leading edge
information technology like data
warehouse or data mart, ETL-extract,
transform and load tools, data query
and analysis tools, data presentation
and visualization tools.
From a historical perspective P.
Keen and S. Morton allege that the
concept of decision support systems
evolved from the theoretical studies of
organizational decision making done at
the Carnegie Institute of Technology
during the late 1950s and early'60s and
the technical work on interactive
computer systems, mainly carried out at
the Massachusetts Institute of
S109
Technology in the 1960s (Keen and
Morton, 1978).
O'Brien defines DSS as those IT
systems which are based on the use of
analytical models, specialized
databases, judgment and intuition of the
decider and a process of interactive
modeling which supports the semi-
structured or unstructured decisions
took by managers (O’Brien, 2004). The
main objective of the decision support
systems is to improve modalities for the
decision adoption or creating a
preparatory study for taking the best
decision where the activities to be
developed for this purpose are not
programmable.
Howard Dresner notice the
fundamental importance of a business
intelligence system: Doing business is
information-intensive. Enterprises are
being pushed to share information with
increasingly more audiences. The
business intelligence imperative insists
we elevate BI to a strategic initiative
now, or risk disaster! (Dresner, 2001)
The dichotomy between
transactional systems and business
intelligence systems is shown by the
data types existing in such systems.
While the transactional systems load
current transactions and keep a relative
small log, a data warehouse works with
large volumes of historical data which
are then summarized.
The architecture of a business
intelligence framework involves two
environments: data warehouse itself
and analysis component.
Data is extracted from the
source system using extract, transform
and load tools. It is loaded into a
Staging intermediary database, to allow
transformations to be performed before
all the detail data is loaded into the Data
Warehouse (DW).
The Data Warehouse Database
holds all the detailed information in the
system.
Analysis components of a data
warehouse system are used to support
tactical or strategically decisions by
performing data retrieval, data analysis
and data mining.
Analytical Environment – A
Customer Centric Approach
In the past, the individual customer
was not important but in the recent
year’s customer retention become one
of the highest priorities in any
organization strategy. To become
customer-centric, organizations must
use analysis and segmentation of
customer groups to allow a better
targeting in order to assure a better
marketing campaign.
Customer segmentation is the
process of dividing customers into
distinct subsets (segments or clusters)
that behave in the same way or have
similar needs. Because each segment
is fairly homogeneous in their behavior
and needs, they are likely to respond
similarly to a given marketing strategy.
In the marketing literature, market
segmentation approaches have often
been used to divide customers into
groups in order to implement different
strategies (Yinghui Yang, 2009, p.140).
Customer segmentation schemes
must be useful - information can be
collected to determine which customers
fit into different segments so that action
plans can be developed and effective-
customers within a segment must
behave in similar and predictable ways.
Segments must have clearly
understandable and different needs that
can be acted upon.
The actual trend is to consider
customers as individuals and
emphasizes the importance of
understanding and leveraging
customer-level data providing the
possibility of segmentation and profiling
of customers to improve target-
marketing efforts, thus obtaining a
comprehensive view that shows the
customer relationships with the
organization.
Nowadays, massive amount of
data is being collected for customers
reflecting their behavioral patterns, so
S110
the practice of analyzing such data to
identify behavioral patterns and using
the patterns discovered to facilitate
decision making is becoming more and
more popular (Yinghui Yang, 2009,
p.143).
For strategically and tactical
analysis the analytical environment as
part of the business intelligence
framework is based on three types of
analysis: report, analyze and predict.
Analysis and Reporting can be
done by the following applications:
a) classical reporting applications are
applications that involve static reports.
These kinds of applications that have
minimal analytical requirements are
based on relational databases and use
SQL language;
b) ad-hoc query and reporting
applications offer to the end users a
high level of interactivity by using the
techniques of navigation and selection
of data;
c) analytical applications
(multidimensional) can answer to more
complex questions other than those for
ad-hoc reporting and are based on
multidimensional techniques;
d) applications for extraction and
planning (data mining) - are designed in
such a manner so the end users can
discover new patterns based on
historical data stored in the database.
Because they are based on
historical data, structured into the data
warehouse framework and supporting
customer analysis and segmentation,
business intelligence tools and methods
could indicate trends based on
customer behavior with results in better
targeting during marketing campaigns
increasing the profitability and revenue.
Organizations will increasingly
need to use analytical techniques for
segmentation to support broader
marketing, sales and service initiatives
at the customer level.
Segmentation based on
customer behavior – the
psychological approach
As presented above, predicting
consumer behavior based on current
patterns is one of the main objectives of
Business Intelligence. Consumer
behavior can be defined as a
multidimensional concept par
excellence, representing all the
decisional acts made by individuals or
by groups, directly related to acquiring
and using goods and services to meet
their current and future needs, including
the decision-making processes that
precede and determine these acts.
(Catoiu and Teodorescu, 2004, p.15)
The consumer behavior theory
tells us that, in the mind of the
consumer, a decision-making rational
and conscious process unfolds, which
has several stages that follow each
other in a linear fashion and derive from
one another: (Catoiu and Teodorescu,
2004, p.34):
1. emergence of an unmet need
raising the consumers’ awareness;
2. establishing alternatives based
on information about the nature of the
product and consumer characteristics,
obtained by internal and external
search;
3. mental evaluation of
alternatives;
4. determining the evaluation
resultant (the decision to purchase);
5. post-purchase evaluation.
From the cognitive-procedural
perspective, thinking is the intellectual
activity for logic-criteria processing of
the information provided by perception
or memory, for understanding,
explaining and interpreting the
phenomena of the universe (nature and
society), to solve different types of
problematic situations, development of
various projects and plans for creative
activity, the development and adoption
of decisions best action (Golu, 2000).
However, the latest neurological
research has found that people do not
think in a linear, fragmented,
S111
hierarchical way, but always view the
final product as a whole. When
consumers evaluate a product, they use
a complex system, composed of mind,
brain, body and outer world, which
influence one another in a fluid and
dynamic way (Zaltman, 2007,
p.41). Buyers do not think in separate
compartments, as companies and
universities are organized. In order to
truly understand consumers we can not
"dismantle" them and study them by
pieces and we must not focus solely on
one of the four aspects, but on the
interaction between them. Renowned
companies such as Citibank, Kraft,
Coca-Cola, Unilever, Hallmark and
Disney are beginning to base their
research on areas that previously were
not considered to belong to the field of
marketing, such as musicology,
neurology and philosophy, besides
those considered tangential to
marketing, such as psychology,
sociology or anthropology. More and
more research studies, even those
made by proponents of rational thinking,
have revealed that the rational model of
explaining consumers’ decision making
process is more an exception than a
rule. It seems that the selection process
is relatively automatic and is based on
the skills and other strengths of the
unconscious, being largely influenced
by the social and physical context of the
consumer. (Zaltman, 2007, p.36).
Although the human brain has separate
modules for processing emotions and
supporting logical reasoning, the two
systems communicate with each other
and influence our behavior in
tandem. The emotional system, which is
older than the rational one from an
evolutionary perspective, typically
exerts the first influence upon our
thinking and behavior. More important is
the fact that emotions contribute to and
play a key role in making correct
decisions. (Zaltman, 2007, p.36) Recent
studies on the effects of brain injuries
have demonstrated that when the
neurological structures that have a role
either in the appearance of emotions or
in supporting reasoning suffer injuries,
the affected individuals lose their ability
to make the right decisions that enable
them to live a normal life. (Zaltman,
2007, p.37)
From neurological studies, we also
find out that generally people do not
think in words. For example, EEG scans
demonstrate that the activation of the
connections between our nerve cells
(neurons) precedes the moment we are
aware of a thought, as well as the
activity of brain areas involving verbal
language. These neural areas are
activated only later, after the person in
question has chosen to represent the
unconscious thoughts to themselves or
others using verbal language (Zaltman,
2007, p.44).
Also, marketers overestimate the
power of advertising communication,
assuming that when consumers are
faced with information about a brand or
with stories about a company, they
receive these messages passively and
in the exact form they were
transmitted. On the contrary, studies
show that consumers create their own
meaning, combining the information
provided by companies with their own
memories, with other stimuli present at
those moments and with the metaphors
that are evoked in their mind when they
think about the message of that
company (Zaltman, 2007, p.45).
Furthermore, human memory is
much more creative and more flexible
than we imagine, permanently
combining and recreating memories, so
that marketers cannot rely on the
assumption that the experience that a
buyer remembers today is the same as
the experience that he will remember in
a few weeks. For example, a large
European retailer discovered that the
experience described by people
answering a questionnaire was different
depending on the order in which
questions were addressed and even
depending on the color of the paper
S112
they were printed on (Zaltman, 2007,
p. 43).
Another important note is that
despite what marketers think,
consumers have limited access to their
own mental activities. 90% of thought
processes take place on the
unconscious level, and instead of
guiding or controlling our behavior, the
conscious mental activity rather seems
to explain the behavior after it has
occurred. (Zaltman, 2007, p.39) An
example would be that of a producer of
chemicals who could not understand
why some companies were willing to
pay other suppliers a higher price for
similar products. The first reasons
invoked by the buyers were traditional
and logic, such as the desire to not rely
on a single supplier. However, a closer
analysis revealed a much more
important feeling - self-esteem - which
appeared among procurement
agents. The company managed
afterwards to strengthen the relationship
with the procurement agents by
adjusting the feeling of recognition of
self-esteem during sales calls (Zaltman,
2007, p.40).
The neurology and psychology
facts summarized above indicate that
the decision making process in
consumer behavior is determined more
by unconscious thoughts and feelings
than by conscious ones, although
conscious thoughts and feelings are, of
course, very important. But the
unconscious forces, such as memories,
images or feelings, influence decisions
and behavior more clearly and are in
perpetual change and
interaction (Zaltman, 2007, p.46).
Conclusions
Business intelligence, component
of DSS systems, has become integral to
day-to-day operations, propelling the
technology into mission-critical status.
At the same time, a number of factors
have increased the complexity and
changes of business environments. The
global business requirements reveal the
need for many unique and interlinked
key business attributes based on main
criteria’s like relevance, impact and
feasibility. Organizations must adopt an
enhanced, integrated and flexible
business intelligence enterprise
model that considers various business,
functional and operational requirements
and linkage between all the data
warehouse components. However,
decision support systems aiming to
predict consumer behavior should not
leave out the psychological aspect. No
matter how close consumers are to a
brand or a company, no matter how
often professional research studies are
conducted, no matter how many loyalty
systems or promotional actions get
implemented, customer behavior may at
any time surprises marketers and it is
the task of business intelligence to take
into account this element of
unpredictability and minimize its effects
when designing marketing programs.
REFERENCES
Catoiu, I and N.Teodorescu, N. (2004), Comportamentul consumatorului, Bucuresti:
Editura Uranus.
Clemen, R.T. (1996), Making Hard Decisions.An Introduction to Decision Analysis.
2
nd
Edition.Belmont
uxbury Press.
Dresner, H. (2001), “ Why enterprises must make business intelligence an
imperative” ,http://www.bizforum.org/whitepapers/microsoft-2.htm.
Filip, F.G. (2004), “ Sisteme Suport Pentru Decizii:O Incercare de Istorie” , Revista
Informatica Economica, vol 29, no1.
S113
Golu, M. (2000), Fundamentele psihologiei, Bucure?ti: Editura Funda?iei „România
de Mâine”.
Keen, P. and Morton, S. (1978), Decision Support Systems: An Organizational
Perspective, Reading MA :Addison-Wesley Publishing Company.
O’Brien, J.A. (2004), Management Information System, Sixth Edition, Boston:The
McGraw-Hill Companies.
Yang, Y. (2009), “Behavioral Pattern-Based Customer Segmentation”,
Encyclopedia of data warehousing and data mining, John Wang, Hershey PA:
Information Science Reference.
Zaltman, G. (2008), Cum gandesc consumatorii, Bucuresti: Editura Polirom.
S114
doc_263656937.pdf
The Border Between Business Intelligence And Psychology- Segmentation Based On Customer Behavior
THE BORDER BETWEEN BUSINESS INTELLIGENCE
AND PSYCHOLOGY- SEGMENTATION BASED ON
CUSTOMER BEHAVIOR
PhD Student Codrin POPA
Politehnica University of Bucharest
MD Iulia BERTEA
Academy of Economic Studies Bucharest
Abstract:
In today’s economy, marketers have been facing two challenging trends:
fierce competition between companies offering essentially similar products,
and dealing with customers that are increasingly informed and demanding,
but less and less loyal. Under these conditions, it has become imperative for
managers and for marketing professionals to invest in business intelligence
in order to find patterns in the consumers’ behavior that could predict their
future buying decisions. In this report we have presented how Decision
Support Systems, data analysis and customer segmentation can help
companies to know their customers better in order to predict (and influence)
their future actions. At the same time, we have argued that Business
Intelligence should meet psychology and neurology halfway, and accept that
there is a very high emotional subconscious component that produces a high
degree of unpredictability in consumers’ behavior.
Keywords: DSS, business intelligence, consumer behavior, segmentation,
buying decision process
Introduction
Mass marketing via mass media
typically produces relatively low returns
(response rates less than 1%), if they're
measured at all. Many firms have turned
to targeted marketing to increase
response rates. As the level of
marketing sophistication and "science"
increases in the marketing process,
response rates can rise dramatically.
Simple product marketing with basic
demographic information can increase
response rates from 3% to 4%.
Segment-driven marketing based on
customer value can help target high-
value customers, but doesn't increase
effectiveness tremendously. Needs-
based marketing involving the customer
life stage and lifestyle can improve
response rates up to 20% or better.
Customer-driven marketing based on
events triggered by customer
interactions/transactions can improve
response rates up to 30% or more.
Customer segmentation has
moved through numerous phases
categorized into three general
approaches.
Product-based - This approach
segments customers according to what
they’re buying.
Value-Based - In recent years,
enterprises have increasingly focused
on growing their understanding of
customer profitability, enabling the
creation of customer value-based
segments such as "gold" and
"platinum", or high-net-worth individuals.
This approach segments customers
according to who is buying.
Needs-Based - This is driving
growing interest in segments based on
customer needs, effectively creating
segments based on why customers buy
certain products.
From a needs-based point of view,
companies spend millions every year to
research consumers’ preferences and
buying intentions and, based on
research results, they spend other
millions to develop new products,
launch special offers or open new sales
outlets. And, more and more often, the
new products or special offers end in
failure – marketing studies have shown
that 80% of all new products and
services fail within 6 months or bring
much lower sales than the anticipated
profit (Zaltman, 2007, p.29). Why didn’t
the consumers buy the product about
which they had said in a focus group
that they would buy it if it existed? What
is influencing and determining people’s
buying behavior? Is the buying decision
process 100% rational, as we would like
to think because we wish to be able to
explain and predict it? Do managers
have to understand not only the
dynamics of clients’ conscious cognitive
process, but also and especially the
unconscious one? (Zaltman, 2007,
p.31)
To deepen the concepts behind
the customer behavior and
segmentation the intrinsic link between
the temporal evolution of the marketing
and information systems must be
emphasized.
Decision Support Systems
In the process of finding solutions
and taking decisions that will lead in a
reasonable manner to proper results,
the first place consists of the searching
process based on strategies put into
work in the voluntary selective attention
that involve restricting the "search
area", as the evolving of the appropriate
development of searching process.
Management decision is the choice of
action for one or more goals.
R.T. Clemen remarks that the
decision context is the framework of
events that determine the set of
objectives that actually matters (and
nothing more or less) for a decider at
the time of drafting the decision, even if
the values remain relatively unchanged
(Clemen, 1996).
Even if some simple patterns could
be achieved based on generic or
structured data, a depth and systematic
analysis that involves semi-structured or
unstructured data could show more
complex patterns and trends that
involve using of a specific class of
information systems – Decision Support
Systems (DSS), systems for collecting
and analyzing the information in order to
assist managerial decision.
The objective of a DSS is to
minimize the effects of the limitations
and constraints in solving of a large and
complex area of decisional problems by
implementing automatic processes of
decision support (Filip, 2004, p.5).
It is important to notice that even if
the decision support systems have an
important role in the processes of
decision the manager should initiate the
process by choosing the appropriate set
of questions, only in this way problems
are identified and resolved.
Business intelligence is a term
used to describe an integrated set of
methods, applications and processes
used to capture, collect, integrate and
analyze data to present information
used for management decision; is
referring to modern data driven decision
support systems using leading edge
information technology like data
warehouse or data mart, ETL-extract,
transform and load tools, data query
and analysis tools, data presentation
and visualization tools.
From a historical perspective P.
Keen and S. Morton allege that the
concept of decision support systems
evolved from the theoretical studies of
organizational decision making done at
the Carnegie Institute of Technology
during the late 1950s and early'60s and
the technical work on interactive
computer systems, mainly carried out at
the Massachusetts Institute of
S109
Technology in the 1960s (Keen and
Morton, 1978).
O'Brien defines DSS as those IT
systems which are based on the use of
analytical models, specialized
databases, judgment and intuition of the
decider and a process of interactive
modeling which supports the semi-
structured or unstructured decisions
took by managers (O’Brien, 2004). The
main objective of the decision support
systems is to improve modalities for the
decision adoption or creating a
preparatory study for taking the best
decision where the activities to be
developed for this purpose are not
programmable.
Howard Dresner notice the
fundamental importance of a business
intelligence system: Doing business is
information-intensive. Enterprises are
being pushed to share information with
increasingly more audiences. The
business intelligence imperative insists
we elevate BI to a strategic initiative
now, or risk disaster! (Dresner, 2001)
The dichotomy between
transactional systems and business
intelligence systems is shown by the
data types existing in such systems.
While the transactional systems load
current transactions and keep a relative
small log, a data warehouse works with
large volumes of historical data which
are then summarized.
The architecture of a business
intelligence framework involves two
environments: data warehouse itself
and analysis component.
Data is extracted from the
source system using extract, transform
and load tools. It is loaded into a
Staging intermediary database, to allow
transformations to be performed before
all the detail data is loaded into the Data
Warehouse (DW).
The Data Warehouse Database
holds all the detailed information in the
system.
Analysis components of a data
warehouse system are used to support
tactical or strategically decisions by
performing data retrieval, data analysis
and data mining.
Analytical Environment – A
Customer Centric Approach
In the past, the individual customer
was not important but in the recent
year’s customer retention become one
of the highest priorities in any
organization strategy. To become
customer-centric, organizations must
use analysis and segmentation of
customer groups to allow a better
targeting in order to assure a better
marketing campaign.
Customer segmentation is the
process of dividing customers into
distinct subsets (segments or clusters)
that behave in the same way or have
similar needs. Because each segment
is fairly homogeneous in their behavior
and needs, they are likely to respond
similarly to a given marketing strategy.
In the marketing literature, market
segmentation approaches have often
been used to divide customers into
groups in order to implement different
strategies (Yinghui Yang, 2009, p.140).
Customer segmentation schemes
must be useful - information can be
collected to determine which customers
fit into different segments so that action
plans can be developed and effective-
customers within a segment must
behave in similar and predictable ways.
Segments must have clearly
understandable and different needs that
can be acted upon.
The actual trend is to consider
customers as individuals and
emphasizes the importance of
understanding and leveraging
customer-level data providing the
possibility of segmentation and profiling
of customers to improve target-
marketing efforts, thus obtaining a
comprehensive view that shows the
customer relationships with the
organization.
Nowadays, massive amount of
data is being collected for customers
reflecting their behavioral patterns, so
S110
the practice of analyzing such data to
identify behavioral patterns and using
the patterns discovered to facilitate
decision making is becoming more and
more popular (Yinghui Yang, 2009,
p.143).
For strategically and tactical
analysis the analytical environment as
part of the business intelligence
framework is based on three types of
analysis: report, analyze and predict.
Analysis and Reporting can be
done by the following applications:
a) classical reporting applications are
applications that involve static reports.
These kinds of applications that have
minimal analytical requirements are
based on relational databases and use
SQL language;
b) ad-hoc query and reporting
applications offer to the end users a
high level of interactivity by using the
techniques of navigation and selection
of data;
c) analytical applications
(multidimensional) can answer to more
complex questions other than those for
ad-hoc reporting and are based on
multidimensional techniques;
d) applications for extraction and
planning (data mining) - are designed in
such a manner so the end users can
discover new patterns based on
historical data stored in the database.
Because they are based on
historical data, structured into the data
warehouse framework and supporting
customer analysis and segmentation,
business intelligence tools and methods
could indicate trends based on
customer behavior with results in better
targeting during marketing campaigns
increasing the profitability and revenue.
Organizations will increasingly
need to use analytical techniques for
segmentation to support broader
marketing, sales and service initiatives
at the customer level.
Segmentation based on
customer behavior – the
psychological approach
As presented above, predicting
consumer behavior based on current
patterns is one of the main objectives of
Business Intelligence. Consumer
behavior can be defined as a
multidimensional concept par
excellence, representing all the
decisional acts made by individuals or
by groups, directly related to acquiring
and using goods and services to meet
their current and future needs, including
the decision-making processes that
precede and determine these acts.
(Catoiu and Teodorescu, 2004, p.15)
The consumer behavior theory
tells us that, in the mind of the
consumer, a decision-making rational
and conscious process unfolds, which
has several stages that follow each
other in a linear fashion and derive from
one another: (Catoiu and Teodorescu,
2004, p.34):
1. emergence of an unmet need
raising the consumers’ awareness;
2. establishing alternatives based
on information about the nature of the
product and consumer characteristics,
obtained by internal and external
search;
3. mental evaluation of
alternatives;
4. determining the evaluation
resultant (the decision to purchase);
5. post-purchase evaluation.
From the cognitive-procedural
perspective, thinking is the intellectual
activity for logic-criteria processing of
the information provided by perception
or memory, for understanding,
explaining and interpreting the
phenomena of the universe (nature and
society), to solve different types of
problematic situations, development of
various projects and plans for creative
activity, the development and adoption
of decisions best action (Golu, 2000).
However, the latest neurological
research has found that people do not
think in a linear, fragmented,
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hierarchical way, but always view the
final product as a whole. When
consumers evaluate a product, they use
a complex system, composed of mind,
brain, body and outer world, which
influence one another in a fluid and
dynamic way (Zaltman, 2007,
p.41). Buyers do not think in separate
compartments, as companies and
universities are organized. In order to
truly understand consumers we can not
"dismantle" them and study them by
pieces and we must not focus solely on
one of the four aspects, but on the
interaction between them. Renowned
companies such as Citibank, Kraft,
Coca-Cola, Unilever, Hallmark and
Disney are beginning to base their
research on areas that previously were
not considered to belong to the field of
marketing, such as musicology,
neurology and philosophy, besides
those considered tangential to
marketing, such as psychology,
sociology or anthropology. More and
more research studies, even those
made by proponents of rational thinking,
have revealed that the rational model of
explaining consumers’ decision making
process is more an exception than a
rule. It seems that the selection process
is relatively automatic and is based on
the skills and other strengths of the
unconscious, being largely influenced
by the social and physical context of the
consumer. (Zaltman, 2007, p.36).
Although the human brain has separate
modules for processing emotions and
supporting logical reasoning, the two
systems communicate with each other
and influence our behavior in
tandem. The emotional system, which is
older than the rational one from an
evolutionary perspective, typically
exerts the first influence upon our
thinking and behavior. More important is
the fact that emotions contribute to and
play a key role in making correct
decisions. (Zaltman, 2007, p.36) Recent
studies on the effects of brain injuries
have demonstrated that when the
neurological structures that have a role
either in the appearance of emotions or
in supporting reasoning suffer injuries,
the affected individuals lose their ability
to make the right decisions that enable
them to live a normal life. (Zaltman,
2007, p.37)
From neurological studies, we also
find out that generally people do not
think in words. For example, EEG scans
demonstrate that the activation of the
connections between our nerve cells
(neurons) precedes the moment we are
aware of a thought, as well as the
activity of brain areas involving verbal
language. These neural areas are
activated only later, after the person in
question has chosen to represent the
unconscious thoughts to themselves or
others using verbal language (Zaltman,
2007, p.44).
Also, marketers overestimate the
power of advertising communication,
assuming that when consumers are
faced with information about a brand or
with stories about a company, they
receive these messages passively and
in the exact form they were
transmitted. On the contrary, studies
show that consumers create their own
meaning, combining the information
provided by companies with their own
memories, with other stimuli present at
those moments and with the metaphors
that are evoked in their mind when they
think about the message of that
company (Zaltman, 2007, p.45).
Furthermore, human memory is
much more creative and more flexible
than we imagine, permanently
combining and recreating memories, so
that marketers cannot rely on the
assumption that the experience that a
buyer remembers today is the same as
the experience that he will remember in
a few weeks. For example, a large
European retailer discovered that the
experience described by people
answering a questionnaire was different
depending on the order in which
questions were addressed and even
depending on the color of the paper
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they were printed on (Zaltman, 2007,
p. 43).
Another important note is that
despite what marketers think,
consumers have limited access to their
own mental activities. 90% of thought
processes take place on the
unconscious level, and instead of
guiding or controlling our behavior, the
conscious mental activity rather seems
to explain the behavior after it has
occurred. (Zaltman, 2007, p.39) An
example would be that of a producer of
chemicals who could not understand
why some companies were willing to
pay other suppliers a higher price for
similar products. The first reasons
invoked by the buyers were traditional
and logic, such as the desire to not rely
on a single supplier. However, a closer
analysis revealed a much more
important feeling - self-esteem - which
appeared among procurement
agents. The company managed
afterwards to strengthen the relationship
with the procurement agents by
adjusting the feeling of recognition of
self-esteem during sales calls (Zaltman,
2007, p.40).
The neurology and psychology
facts summarized above indicate that
the decision making process in
consumer behavior is determined more
by unconscious thoughts and feelings
than by conscious ones, although
conscious thoughts and feelings are, of
course, very important. But the
unconscious forces, such as memories,
images or feelings, influence decisions
and behavior more clearly and are in
perpetual change and
interaction (Zaltman, 2007, p.46).
Conclusions
Business intelligence, component
of DSS systems, has become integral to
day-to-day operations, propelling the
technology into mission-critical status.
At the same time, a number of factors
have increased the complexity and
changes of business environments. The
global business requirements reveal the
need for many unique and interlinked
key business attributes based on main
criteria’s like relevance, impact and
feasibility. Organizations must adopt an
enhanced, integrated and flexible
business intelligence enterprise
model that considers various business,
functional and operational requirements
and linkage between all the data
warehouse components. However,
decision support systems aiming to
predict consumer behavior should not
leave out the psychological aspect. No
matter how close consumers are to a
brand or a company, no matter how
often professional research studies are
conducted, no matter how many loyalty
systems or promotional actions get
implemented, customer behavior may at
any time surprises marketers and it is
the task of business intelligence to take
into account this element of
unpredictability and minimize its effects
when designing marketing programs.
REFERENCES
Catoiu, I and N.Teodorescu, N. (2004), Comportamentul consumatorului, Bucuresti:
Editura Uranus.
Clemen, R.T. (1996), Making Hard Decisions.An Introduction to Decision Analysis.
2
nd
Edition.Belmont
uxbury Press.Dresner, H. (2001), “ Why enterprises must make business intelligence an
imperative” ,http://www.bizforum.org/whitepapers/microsoft-2.htm.
Filip, F.G. (2004), “ Sisteme Suport Pentru Decizii:O Incercare de Istorie” , Revista
Informatica Economica, vol 29, no1.
S113
Golu, M. (2000), Fundamentele psihologiei, Bucure?ti: Editura Funda?iei „România
de Mâine”.
Keen, P. and Morton, S. (1978), Decision Support Systems: An Organizational
Perspective, Reading MA :Addison-Wesley Publishing Company.
O’Brien, J.A. (2004), Management Information System, Sixth Edition, Boston:The
McGraw-Hill Companies.
Yang, Y. (2009), “Behavioral Pattern-Based Customer Segmentation”,
Encyclopedia of data warehousing and data mining, John Wang, Hershey PA:
Information Science Reference.
Zaltman, G. (2008), Cum gandesc consumatorii, Bucuresti: Editura Polirom.
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