MARKET SEGMENT EVALUATION AND SELECTION BASED
ON APPLICATION OF FUZZY AHP AND COPRAS-G METHODS
Mohammad Hasan Aghdaie
1
, Sarfaraz Hashemkhani Zolfani
2
,
Edmundas Kazimieras Zavadskas
3
1, 2
Department of Industrial Engineering, Shomal University, P.O. Box 731,
Amol, Mazandaran, Iran
1, 2, 3
Institute of Internet and Intelligent Technologies, Vilnius Gediminas Technical University,
Sauletekio al. 11, 10223 Vilnius, Lithuania
E-mails:
1
2
3
[email protected] (corresponding author)
Received 02 February 2012; accepted 13 August 2012
Abstract. Market segment evaluation and selection is one of the critical marketing prob-
lems of all companies. This paper presents a novel approach which integrates fuzzy ana-
lytic hierarchy process (FAHP) and COPRAS-G method for market segment evaluation
and selection. Fuzzy AHP is used to calculate the weight of each criterion, and COPRAS-
G method is proposed to prioritize market segments from the best to the worst ones. The
application of fuzzy set theory allows incorporating the vague and imprecise linguistic
terms into the decision process. This study can be used as a pattern for market segment
selection and future researches. A case study on a chair manufacturing company is put
forward to illustrate the performance of the proposed methodology.
Keywords: market segmentation, market segment evaluation, market segment selection,
Fuzzy AHP, COPRAS-G method.
Reference to this paper should be made as follows: Aghdaie, M. H.; Hashemkhani Zol-
fani, S.; Zavadskas, E. K. 2013. Market segment evaluation and selection based on ap-
plication of fuzzy AHP and Copras-G methods, Journal of Business Economics and Man-
agement 14(1): 213–233.
JEL Classication: C02, C44, D40, D46, D81.
1. Introduction
Market segmentation becomes an essential element of marketing in industrialized coun-
tries and in living of any business (Wedel, Kamakura 2000). Market segmentation is
dened as the partitioning of a market into distinct subsets of customers and any subset
could be possibly selected as a target market to be reached with a distinct marketing
mix (Kotler 1999). In other words, market segmentation makes it possible to nd ho-
mogeneous smaller markets by this means, helping marketers to recognize marketing
opportunities and to develop products and services in a more tailor-made manner (Jang
et al. 2002).
Journal of Business Economics and Management
ISSN 1611-1699 print / ISSN 2029-4433 online
2013 Volume 14(1): 213–233
doi:10.3846/16111699.2012.721392
Copyright © 2013 Vilnius Gediminas Technical University (VGTU) Press Technika
http://www.tandfonline.com/TBEM
214
Although market segmentation was introduced into the academic marketing literature
by Smith (1956), market segmentation continues to be an important focal point of ongo-
ing research and marketing practices (Chaturvedi et al. 1997; Hanazadeh, Mirzazadeh
2011). Maybe mass marketing will no longer exist in the coming century or it will
become vanished (Kuo et al. 2002).There are a lot of advantages of market segmenta-
tion over mass marketing. Firstly, it repeatedly helps every company to nd a good
chance to expand its own market by better satisfying the wants of customers. Secondly,
it increases the protability or effectiveness of the organization to the extent that the
economic benets provided for consumers exceed the costs of the segmentation process
(Chiu et al. 2009). Thirdly, the importance of doing marketing segmentation analysis
includes better perception of the market to truly position of a product in the marketplace,
choosing the appropriate segments for target marketing, discovering opportunities in
existing markets, and gaining competitive advantage through product differentiation
(Kotler 1980).
There are many market segmentation bases in the literature that were used to divide a
market into segments such as geographic, demographic, life style and product benets
(Kazemzadeh et al. 2009). Besides, there are numerous market segmentation methods
such as factor analysis, clustering, conjoint, regression, and discriminate analysis. Also
recently, using or integrating other elds including data mining, multivariate statistical
analysis, fuzzy logic, articial neural networks, and genetic algorithm becomes a com-
mon tool for market segmentation.
After market segmentation, every company needs to evaluate and select target market
or markets, and then Market segmentation evaluation is a critical management decision
because all other components of a marketing strategy follow it (Wind, Thomas 1994).
Also, Market segment evaluation can help in targeting markets, thus it is very important
for improving the probability of success in competitive market.
Although much of the marketing literature has proposed various market segmentation
techniques, but a review of academic research reveals that existing studies have rela-
tively neglected segment evaluation and selection (Sarabia 1996; Ou et al. 2009). Also
most existing studies suggest some general criteria for evaluation of attractiveness of a
segment and merely present a model or method for evaluation.
Selecting an appropriate market segment based on evaluation of segments is one of
the most complicated and time consuming problems for many companies, due to many
feasible alternatives, conicting objectives and variety of factors (Aghdaie et al. 2011).
Market segment evaluation and selection decisions are sophisticated by the fact that
the decision-making process must consider various criteria. Therefore market segment
evaluation and selection can be viewed as a multiple criteria decision- making (MCDM)
problem. Hence, this study has the main objective of proposing a mechanism for market
segment evaluation and selection.
The MCDM methods deal with the process of making decisions for nding the optimum
alternative in the presence of multiple, usually conicting, decision criteria.
In this research a hybrid MCDM model encompassing fuzzy analytic hierarchy process
(FAHP) and the complex proportional assessment of alternatives with grey relations
M. H. Aghdaie et al. Market segment evaluation and selection based on application ...
215
Journal of Business Economics and Management, 2013, 14(1): 213–233
(COPRAS-G method) are used for market segment evaluation and selection. Speci-
cally, FAHP is initially used for calculating the weight of each criterion and COPRAS-G
method is used for ranking and selecting the best location.
The remainder of this paper is organized as follows. The related studies are summarized
in Section 2. The third section presents the methodology including FAHP and COPRAS-
G method. In Section 4, a real-world case study is given to prove the applicability of
the proposed method on a large- sized manufacturing enterprise in Iran. In Section 4,
the results are discussed. In Section 5, nally, the article will be concluded.
2. Literature review
Market segment evaluation and selection is one of the important problems for every
company. The major part of the related literature concentrates on the important features
for doing this evaluation and very little research has been done on the evaluation of seg-
ment attractiveness and market segment selection. The enormous majority of decision-
making methods identied apply to the nal stage of market segment evaluation and
selection. Also, it is remarkable that segmentation itself has many limitations in terms
of product, segment size, protability/yield, attainability with promotion mix and sup-
ply, doubled expenses for marketing mix, industry, etc. Generally, expert efforts have
focused on evaluating different segmentation methods and techniques (Bonoma, Shapiro
1983; Christen 1987; Elrod, Winner 1982; Morrison 1973; Novak et al. 1992; Wildt
1976). Even general studies of market segmentation have paid little or no attention to
the evaluation and selection stages (Beane, Ennis 1987; Weinstein 1987; Wind 1978).
Authors generally limit themselves to analyzing how to evaluate segment stability (Bet-
tman 1971; Calentone, Sawyer 1978; Lehmann et al. 1982; MacLachlan, Johansson
1981), congruence (Green 1977), internal homogeneity and protability (Eckrich 1984;
Van Auken, Lonial 1984; Beik, Buzby 1973), to mention only the most relevant.
Some general criteria such as identity ability, substantiality, accessibility, stability, re-
sponsiveness, action ability have been frequently put forward as determining the ef-
fectiveness and protability of market segment (Frank et al. 1972; Loudon, Della Bitta
1984; Baker 1988; Kotler 1988). Based on research of the United Kingdom’s Times Top
1000 companies, Simkin and Dibb (1998) found that the three most important factors for
selecting target markets were protability, market growth, and market size. McQueen
and Miller (1985) recommended the assessment of market attractiveness based upon
protability, variability, and accessibility. In the same way, Loker and Perdue (1992)
proposed a systematic approach to evaluating segments using a ranking procedure. They
assessed segment attractiveness in terms of protability, accessibility, and reachability
by ranking each segment on its relative performance according to the three evaluation
criteria. Based on Kotler and Armstrong (2003) the market segments should meet ve
selection criteria including: (1) measurable, (2) accessible, (3) sustainable, (4) differenti-
able, and (5) actionable to be viable. Also, Morrison (2002) added ve more criteria in
Kotler and Armstrong’s list for effective segmentation, including: homogeneity, defen-
sibility, competitiveness, durability, and compatibility. These theoretically fundamental
216
criteria provide marketers with useful guidelines for targeting markets (Lee et al. 2006).
Bock and Uncles (2002) suggested that, when preparing a segmentation strategy, prof-
itability must be considered as one of the main selection criteria. Jang et al. (2002)
incorporated the protability and risk concepts in evaluating segment attractiveness
as more quantiable and comprehensive protability measures. Most of these studies,
propose different schemes for market segmentation, however, they have concentrated
on evaluation and therefore have only taken into account very specic criteria. Ou et al.
(2009) incorporated the famous model that was developed by Porter (1980) to evaluate
each potential segment. Companies must carefully assess and weigh key discriminating
criteria to nd the “best” market segments (Weinstein 2004).
McDonald and Dunbar (2004) prepared one of the comprehensive criteria list for market
segment evaluation. They also provide a list of twenty-seven possible, generalized seg-
ment attractiveness factors in ve major areas: segment factors, competition, nancial
and economic factors, technology, and sociopolitical factors. McDonald and Dunbar
add segment attractiveness factors be weighted based on the particular requirements of
an organization.
This study uses the McDonald and Dunbar’s (2004) criteria list as the basis for market
segment evaluation. This criteria list is depicted in Table 1.
Table 1. The segment attractiveness criteria
Criteria Sub-criteria
Segment factors Size (money, units or both)
Growth rate per year
Sensitivity to price, service features and external factors
Cyclicality
Seasonality
Bargaining power of upstream suppliers
Bargaining power of downstream suppliers
Competition Types of competitors
Degree of concentration
Changes in type and mix
Entries and exits
Changes in share
Substitution by new technology
Degrees and types of integration
Financial and economic factors Contribution margins
Leveraging factors, such as economies of scale
and experience
M. H. Aghdaie et al. Market segment evaluation and selection based on application ...
217
Criteria Sub-criteria
Financial and economic factors Barriers to entry or exit (nancial and non-nancial)
Capacity utilization
Technological factors Maturity and volatility
Complexity
Differentiation
Patents and copyrights
Manufacturing process technology required
Socio-political factors Social attitudes and trends
Laws and government agency regulations
Inuence with pressure groups and government
representatives
Human factors, such as unionization and community
acceptance
Source: adopted from McDonald and Dunbar (2004); modied from related research.
3. Methodology
Over the past decades the complexity of economic decisions has increased rapidly, thus
highlighting the importance of developing and implementing sophisticated and efcient
quantitative analysis techniques for supporting and aiding economic decision-making
(Zavadskas, Turskis 2011). Multiple criteria decision making (MCDM) is an advanced
eld of operations research, provides decision- makers and analysts with a wide range
of methodologies, which are overviewed and well suited to the complexity of economic
decision problems (Hwang, Yoon 1981; Zopounidis, Doumpos 2002; Figueira et al.
2005). In this paper, we proposed a combined fuzzy AHP and COPRAS-G method ap-
proach to market segment evaluation and selection. The evaluation criteria for market
segment evaluation and selection are based on McDonald and Dunbar’s (2004) criteria
list. According to these criteria, the required data utilized in the comparisons are col-
lected from the related decision makers (DMs). After constructing the evaluation criteria
hierarchy, the criteria weights are calculated by applying the fuzzy AHP method. Finally
COPRAS-G method is employed to achieve the nal ranking results. The detailed de-
scriptions of the major steps are elaborated in the following subsections.
Fuzzy AHP
AHP is developed by Saaty (1980), maybe it is one of the famous, dazzling and most
widely used models in decision making. With the extension of this method in fuzzy set
theory, fuzzy AHP was developed. In the proposed methodology, AHP with its fuzzy
extension, namely fuzzy AHP, is applied to obtain more decisive judgments by pri-
oritizing the market segment selection criteria and weighting them in the presence of
End of Table 1
Journal of Business Economics and Management, 2013, 14(1): 213–233
218
vagueness. There are numerous fuzzy AHP applications in the literature that propose
systematic approaches for selection of alternatives and justication of problem by using
fuzzy set theory and hierarchical structure analysis (Efendigil et al. 2008; Önüt et al.
2010). DMs usually nd it more convenient to express interval judgments than xed
value judgments due to the fuzzy nature of the comparison process (Bozdag et al. 2003).
This study concentrates on a fuzzy AHP approach introduced by Chang (1992), in which
triangular fuzzy numbers are preferred for pairwise comparison scale. Extent analysis
method is selected for the synthetic extent values of the pairwise comparisons. Some
papers published used the fuzzy AHP procedure based on extent analysis method and
showed how it can be applied to selection problems (Cebeci, Ruan 2007; Kahraman
et al. 2003, 2004). The outlines of the fuzzy sets and extent analysis method for fuzzy
AHP are given below.
A fuzzy number is a special fuzzy set
( )
( )
{ }
,,
F
F x x xR= ∈
µ
, where x takes its values
on the real line,
:Rx−∞ ≤ ≤ ∞
and
( )
F
x
µ
is a continuous mapping from R to the closed
interval [0,1]. A triangular fuzzy number (TFN) expresses the relative strength of each
pair of elements in the same hierarchy, mand can be denoted as M = (l, m, u), where l ≤
m ≤ u. The parameters l, m, u indicate the smallest possible value, the most promising
value, and the largest possible value respectively in a fuzzy event. The recent applica-
tions of fuzzy AHP method, in short, are listed below:
– Keršulienė and Turskis (2011) used fuzzy AHP and ARAS for architect selection.
– Fouladgar et al. (2011) used fuzzy AHP and fuzzy TOPSIS for prioritizing strate-
gies of the Iranian mining sector.
– Lin et al. (2011) used fuzzy Delphi method, fuzzy AHP and fuzzy theory to develop
an evaluation system of knowledge management performance.
– Nepal et al. (2010) used fuzzy AHP approach to prioritization of CS attributes in
target planning for automotive product development.
– Heo et al. (2010) used fuzzy AHP for analysis of the assessment factors for renew-
able energy dissemination program evaluation.
– Haghighi et al. (2010) applied fuzzy AHP to e-banking development in Iran.
– Tiryaki and Ahlatcioglu (2009) used fuzzy AHP for Fuzzy portfolio selection.
– Gungor et al. (2009) used fuzzy AHP approach to personnel selection problem.
Triangular type membership function of M fuzzy number can be described as in Equa-
tion 1.
( )
( ) ( )
( ) ( )
0
0
.
M
xl
xl ml
lxm
x
mxu
ux um
xu
−−
≤≤
≤≤
−−
=
µ
(1)
A linguistic variable is a variable whose values are expressed in linguistic terms (Önüt
et al. 2008). The concept of a linguistic variable is very useful in dealing with situations,
which are too complex or not well dened to be reasonably described in conventional
quantitative expressions (Zadeh 1965; Zimmermann 1991; Kaufmann, Gupta 1991).
In this study, the linguistic variables that are utilized in the model can be expressed in
positive TFNs for each criterion as in Figure 1.
M. H. Aghdaie et al. Market segment evaluation and selection based on application ...
219
The linguistic variables matching TFNs and the corresponding membership functions
are provided in Table 2. Proposed methodology employs a Likert Scale of fuzzy num-
bers starting from
1
to
9
, symbolized with tilde (~) for the fuzzy AHP approach. Ta-
ble 2 depicts AHP and fuzzy AHP comparison scale considering the linguistic variables
that describes the importance of criteria and alternatives to improve the scaling scheme
for the judgment matrices.
Table 2. Linguistic variables describing weights of the criteria and values of ratings
Linguistic scale for
importance
Fuzzy numbers
for fuzzy AHP
Membership function Domain Triangular fuzzy
scale (l, m, u)
Just equal
(1.0, 1.0, 1.0)
Equal importance
1
m
M
(x) = (3 – x)/(3 – 1) l ≤ x ≤ 3 (1.0, 1.0, 3.0)
Weak importance
of one over another
3
m
M
(x) = (x – 1)/(3 – 1) l ≤ x ≤ 3 (1.0, 3.0, 5.0)
m
M
(x) = (5 – x)/(5 – 3) 3 ≤ x ≤ 5
Essential or strong
importance
5
m
M
(x) = (x – 3)/(5 – 3) 3 ≤ x ≤ 5 (3.0, 5.0, 7.0)
m
M
(x) = (7 – x)/(7 – 5) 5 ≤ x ≤ 7
Very strong
importance
7
m
M
(x) = (x – 5)/(7 – 5) 5 ≤ x ≤ 7 (5.0, 7.0, 9.0)
m
M
(x) = (9 – x)/(9 – 7) 7 ≤ x ≤ 9
Extremely
preferred
9
m
M
(x) = (x – 7)/(9 – 7) 7 ≤ x ≤ 9 (7.0, 9.0, 9.0)
If factor i has one of the above numbers assigned
to it when compared to factor j, then j has the reciprocal
value when compared with i
Reciprocals of above
( )
1
1
1 11
1 ,1 ,1M
uml
−
≈
By using TFNs via pairwise comparison, the fuzzy judgment matrix
( )
ij
A
a
can be
expressed mathematically as in Equation 2:
Fig. 1. Linguistic variables for the importance weight of each criterion
Equally Moderately
Strongly Very Strongly
Extremely
13
57
9
0
0.5
1
M
Journal of Business Economics and Management, 2013, 14(1): 213–233
220
( ) ( )
( )
( )
( )
( )
( )
11
13
1
12
21 23
21 2
11 12
13
1
12
1
3
1
1
1
1
.
n
n
nn
nn
n
nn
nn
nn
n
a
a
a
a
aa
aa
A
aa
a
a
aa
a
a
−
−
−−
−
−
−
=
(2)
The judgment matrix
A
is a
nn×
fuzzy matrix containing fuzzy numbers
ij
a
.
11
11
1
1,
1,3,5,7,9 or
.
,,,,,
57
39
1
ij
ij
a
ij
−−
−−
−
=
=
≠
(3)
Let
{ }
12
, , ...,
n
X
x
xx
=
be an object set, whereas
{ }
12
, , ...,
n
U
u
uu
=
is a goal set. Ac-
cording to fuzzy extent analysis, the method can be performed with respect to each
object for each corresponding goal, g
i
, resulting in m extent analysis values for each
object, given as
12
, , ..., , 1,2, ...,
n
gi gi gi
MM M i n=
where all the
( )
1,2, ...,
j
gi
Mj m=
are
TFNs representing the performance of the object x
i
with regard to each goal u
j
. The
steps of Chang’s extent analysis (1992) can be detailed as follows (Kahraman et al.
2003, 2004; Bozbura 2007):
Step 1: The fuzzy synthetic extent value with respect to the i th object is dened as:
1
11
1
.
nm
j
i
gi
ij
m
j
gi
j
S
MM
−
= =
=
=
⊗
∑∑
∑
(4)
To obtain
1
m
j
gi
j
M
=
∑
, perform the fuzzy addition operation m extent analysis such that
operation m extent analysis values for a particular matrix will be as follows:
11 1
1
,, ,
m
mm m
j
j jj
gi
jj j
j
lmu
M
= = =
=
=
∑∑ ∑
∑
(5)
then obtain
1
11
nm
j
gi
ij
M
−
= =
∑∑
, perform the fuzzy addition operation of
( )
1, 2, ...,
j
gi
jm
M
=
values as shown below:
1 11 1
1
,, ,
n nn n
j
i ii
gi
i ii i
j
m
lmu
M
= = = =
=
=
∑ ∑∑ ∑
∑
(6)
then compute the inverse of the vector in Equation 6 as follows:
1
11
11 1
111
,,.
nm
j
gi
nn n
ij
i ii
ii i
M
uml
−
= =
= = =
=
∑∑
∑∑ ∑
(7)
M. H. Aghdaie et al. Market segment evaluation and selection based on application ...
221
Step 2: The degree of possibility of M
2
≥ M
1
is dened as:
( ) ( ) ( )
( )
12
21
sup min ,
MM
yx
V xy
MM
≥
≥=
µµ
,
(8)
and it can be equivalently expressed as follows:
( ) ( ) ( )
( )
( )
( ) ( )
2
21
21 1 2
12
12
2 2 11
1,
0,
,
hgt ,
, otherwise,
M
if
if
mm
Vd
lu
MM MM
lu
m u ml
≥
≥= ∩= = ≥
µ
−
−−−
(9)
where d is the ordinate of the highest intersection point D between
1
M
µ
and
2
M
µ
(see
Figure 2). To compare M
1
and M
2
, both the values of V (M
1
≥ M
2
) and V (M
2
≥ M
1
)
are required.
Step 3: The degree of possibility of a convex fuzzy number to be greater than k convex
fuzzy numbers M
i
(i = 1, 2, ..., k) can be dened by Equation 10.
( )
[ ] [ ] [ ] [ ]
( )
12 1 2
, ,..., and, and... and, min , 1,2,...,
K ki
M MM M M
V V V V V ik
MM M M M M M
≥ =≥ ≥ ≥= ≥ =
( )
[ ] [ ] [ ] [ ]
( )
12 1 2
, ,..., and, and... and, min , 1,2,...,
K ki
M MM M M
V V V V V ik
MM M M M M M
≥ =≥ ≥ ≥= ≥ =
.
(10)
Assume that:
( ) ( )
min ,
i
ik
d
SS
A
′
= ≥
(11)
where: k = 1, 2, ..., n; k ≠ i. Then, the weight vector is given by as in Equation 12:
( ) ( ) ( )
( )
12
, ,, ,
T
n
Wd d d
AA A
′′ ′ ′
=
(12)
Where A
i
(i = 1, 2, …, n)
has n
elements.
Step 4: The normalized weight vectors are dened as:
( ) ( ) ( )
( )
12
, ,, ,
T
n
Wd d d
AA A
=
(13)
where W is a non fuzzy number.
Fig. 2. Intersection point “d” between two fuzzy numbers M
1
and M
2
1
V
()MM
21
l
2
m
2
l
1
m
1
u
2
u
1
d
Journal of Business Economics and Management, 2013, 14(1): 213–233
222
COPRAS-G METHOD
In order to evaluate the overall efciency of an alternative, it is necessary to identify
selection criteria, to assess information relating to these criteria, and to develop methods
for evaluating the criteria to meet the participant’s needs. Decision analysis is concerned
with the situation in which a decision-maker (DM) has to choose among several alter-
natives by considering a particular set of, usually conicting criteria. For this reason
Complex proportional assessment (COPRAS) method that was developed by Zavadskas
and Kaklauskas (1996) can be applied. This method was applied to the solution of vari-
ous problems in construction (Tupenaite et al. 2010; Ginevičius et al. 2008; Kaklauskas
et al. 2010; Zavadskas et al. 2010). The most of alternatives under development always
deal with vague future, and values of criteria cannot be expressed exactly. This MCDM
problem should be determined not by exact criteria values, but by fuzzy values or by
values in some intervals. Zavadskas et al. (2008) presented the main ideas of complex
proportional assessment method with grey interval numbers (COPRAS-G) method. The
idea of COPRAS-G method with criterion values expressed in intervals is based on the
real conditions of decision making and applications of the Grey systems theory (Deng
1982; Deng 1988). The COPRAS-G method uses a stepwise ranking and evaluating
procedure of the alternatives in terms of signicance and utility degree.
The recent developments of decision making models based on COPRAS methods are
listed below:
– Uzsilaityte and Martinaitis (2010) investigated and compared different alternatives
for the renovation of buildings taking into account energy, economic and environ-
mental criteria while evaluating impact of renovation measures during their life
cycle;
– Chatterjee et al. (2011) presented materials selection model based on COPRAS
and EVAMIX methods;
– Zavadskas et al. (2011) presented assessment of the indoor environment;
– Podvezko (2011) presented comparative analysis of MCDM methods (SAW and
COPRAS);
– Hashemkhani Zolfani et al. (2011) presented forest roads locating using COPRAS-
G method;
– Hashemkhani Zolfani et al. (2012) carried out research on quality control manager
selection applying COPRAS-G method;
– Chatterjee and Chakraborty (2012) presented materials selection using COPRAS-G
method.
The procedure of applying the COPRAS-G method consists of the following steps
(Zavadskas et al. 2009):
1. Selecting the set of the most important criteria, describing the alternatives.
2. Constructing the decision-making matrix
X⊗
:
M. H. Aghdaie et al. Market segment evaluation and selection based on application ...
223
[ ]
[ ]
[ ]
[ ]
[ ] [ ]
1
1
11 12
11 12
11
1
21
22
2
2
22
21 21
2
1
12
12
;
;;
;;
;
;
;; ;
m
m
m
m
m
m
n nm
n n nm
n n nm
x
xx
x
xx
xx
xx
x
x
xx
x
x
X
xx
xx x
xx x
⊗⊗
⊗⊗
⊗= =
⊗⊗
,
1, , 1,j ni m= =
, (14)
Here
ji
x⊗
is determined by
ji
x⊗
(the smallest value, the lower limit) and
ji
x
(the big-
gest value, the upper limit).
3. Determining signicances of the criteria q
i
.
4. Normalizing the decision-making matrix ⊗X:
(
)
1
11 11 11
2
2
,.
11
22
ji ji ji ji
n
nn nn nn
ji
ji ji ji
ji
ji ji ji
j
jj jj jj
xx
xx
xx
x
x
xx x
xx x
=
= = = = = =
= = = =
+
++ +
∑
∑∑ ∑∑ ∑∑
1, ; 1,j ni m= =
, (15)
In formula (15)
ji
x
is the lower value of the criterion i in the alternative j of the solu-
tion;
ji
x
is the upper value of the criterion i in the alternative j of the solution; m is the
number of criteria; n is the number of the alternatives, compared. Then, the decision-
making matrix is normalized:
1
12
1
11 12
11
21
21 22
1
22
2
12
12
;
;
;
;;
;
.
;; ;
m
m
m
m
n n nm
n n nm
x
x
x
xx
x
x
x
x
xx
x
X
xx x
xx x
⊗=
(16)
5. Calculating the weighted normalized decision matrix
ˆ
X⊗
. The weighted normalized
values
ˆ
ji
x⊗
are calculated as follows:
.
ˆ
ji
ji
q
x
x
⊗=⊗
or
.
ˆ
i
ji ji
q
xx
=
and
.
ˆ
i
ji ji
q
xx
=
, (17)
In formula (17), q
i
is the signicance of the i-th criterion.
Then, the normalized decision-making matrix is:
[ ]
[ ]
[ ]
[ ]
[ ]
[ ]
[ ] [ ] [ ]
1
11 12
1
11 12
11 12 1
21 21 2
21 22
2
21 22
2
12
12
12
;
;;
ˆ
ˆˆ
ˆ
ˆˆ
ˆˆ ˆ
;;
ˆˆ ˆ
;
ˆˆ
ˆ
ˆˆ
ˆ
ˆ
ˆˆ ˆ
;; ;
ˆˆ ˆ
ˆˆ ˆ
m
m
m
m
m
m
n n nm
n n nm
n n nm
x
xx
x
xx
xx x
xx x
xx
x
xx
x
X
xx x
xx x
xx x
⊗⊗ ⊗
⊗⊗ ⊗
⊗= =
⊗⊗ ⊗
.
(18)
Journal of Business Economics and Management, 2013, 14(1): 213–233
224
6. Calculating the sums P
j
of criterion values, whose larger values are more preferable:
(
)
1
1
.
ˆ
ˆ
2
k
j
ji
ji
i
P
x
x
=
= +
∑
(19)
7. Calculating the sums R
j
of criterion values, whose smaller values are more preferable:
(
)
1
1
;
ˆ
ˆ
2
m
j
ji
ji
ik
R
x
x
= +
= +
∑
,.i km=
(20)
In formula (20), (m–k) is the number of criteria which must be minimized.
8. Determining the minimal value of R
j
as follows:
min
min ;
j
j
RR=
1, .
jn
=
(21)
9. Calculating the relative signicance of each alternative Q
j
the expression is obtained:
1
1
.
1
n
j
j
jj
n
j
j
j
R
QP
R
R
=
=
= +
∑
∑
(22)
10. Determining the optimal criterion K by the formula:
max ;
j
j
KQ=
1, .jn=
(23)
11. Determining the priority order of the alternatives.
12. Calculating the utility degree of each alternative by the formula:
max
100%.
j
j
Q
N
Q
= ×
(24)
Here Q
j
and Q
max
are the signicances of the alternatives obtained from equation (22).
4. Case study
A real world case problem is selected in chair manufacturing company to illustrate the
application of the proposed approach. The selected company is Nilper Company, which
is one of the well-known brands in chair manufacturing industry in Iran. Nilper Com-
pany is a large- sized manufacturing enterprise, which is a recognized leader in chair
manufacturing industry in Iran. Nilper Company currently offers more than 50 models
of managerial, administrative, and clinical chairs based on customer needs and ergo-
nomic standards. In recent years, there has been a steady growth in demand for many
models of ofce chairs. Therefore, it was a matter of company’s policy to undertake
marketing research in order to improve its design process based on the main custom-
ers’ wants for ofce chairs. Recently, this market research project was done and three
segments were dened, which are denoted as SEG1, SEG 2 and SEG 3, respectively.
Also, this company needs to evaluate and select obtained market segments for doing
M. H. Aghdaie et al. Market segment evaluation and selection based on application ...
225
other marketing activities. Consequently, the project team including R&D Manager,
Marketing Manager, Sales Manager and two industrial engineers working for the com-
pany was constructed. At this point, the company needs to evaluate segments and select
only one segment from them. So, the rst criteria list based on McDonald and Dunbar
(2004) for the market segment evaluation and selection was prepared. The number of
criteria was very high and it was very difcult to evaluate all of them. So project team
decided to choose some number of criteria for evaluating. Besides, they had to consider
their company conditions, future plans, competitors, etc. For reducing the number of
criteria and in order to select the most reasonable criteria, a questionnaire including all
the rst list criteria was designed. Then, the project team have been asked to give a rate
to each of the criterion containing “not important at all”, “not very important”, “impor-
tant”, “quite important” and “very important” which are the verbal representation of the
1–5 numeric scale respectively. Next, rank of each criterion was selected based on the
geometric mean of each criterion in all questionnaires. In the end and based on these
ranks, nine criteria were determined to perform the analysis. The nine criteria are: De-
gree of concentration, Laws and government agency regulations, Types of competitors,
Contribution margins, Manufacturing process technology required, Complexity, Growth
rate per year, Size, and Leveraging factors which are denoted as X
1
, X
2
, X
3
, X
4
, X
5
,
X
6
, X
7
, X
8
, and X
9
, respectively. Furthermore, project team decided about kind of each
criterion based on situations of Iran market. After determining all selection criteria and
alternatives, the paired comparisons for criteria list (see Table 3) were made by using
the TFNs to tackle the ambiguities involved in the process of the linguistic assessment
of the data. The project team lled this table, formed by reaching general agreement on
questions related to the importance of the criteria and alternatives via Delphi technique
as a group decision- making tool.
According to the weights in Table 3, Size, Growth rate per year and Types of competitor
were three of the most important considered criteria.
5. Results
The aim of using fuzzy AHP is to determine importance weight of the criteria that will
be employed in COPRAS-G method. Table 3 depicts the pairwise comparison matrix set
by TFNs that matches linguistic statements of data. The fuzzy values of paired compari-
son were converted to crisp values via the Chang’s extent analysis as mentioned before.
First, the fuzzy synthetic extent values were calculated by using Equation 4 with the
help of Equations 5–7. Equations 8–9 were applied to express the degree of synthetic
extent values. To have a weight vector given by as in Equation12, Equations 10–11 were
applied by comparing the fuzzy numbers. After normalizing weight vector dened as
in Equation 13, the obtained priority weight vector of criteria is gured out in the last
column of Table 3. After this stage, project team evaluated each segment according to
each criterion and Table 4 was developed.
Journal of Business Economics and Management, 2013, 14(1): 213–233
226
Table 3. Pairwise comparisons of selection criteria via TFN
X
1
X
2
X
3
X
4
X
5
X
6
X
7
X
8
X
9
Priority
weight (W)
Degree of concentration (X
1
) 1,1,1 1,3,5 1,1,3 1/7,1/5,1/3 1/7,1/5,1/3 1,1,3 1/9,1/9,1/7 1/9,1/7,1/5 1/5,1/3,1 0.051
Laws and government
agency regulations (X
2
)
1/5,1/3,1 1,1,1 1/9,1/7,1/5 1/5,1/3,1 1,3,5 1,1,3 1,3,5 1/7,1/5,1/3 1,3,5 0.079
Types of competitor (X
3
) 1/3,1,1 5,7,9 1,1,1 1,3,5 3,5,7 1,1,3 1,3,5 1,1,3 1/3,1,1 0.135
Contribution margins (X
4
) 3,5,7 1,3,5 1/5,1/3,1 1,1,1 3,5,7 1/7,1/5,1/3 1/9,1/9,1/7 1/9,1/7,1/5 1/9,1/7,1/5 0.111
Manufacturing process
technology required (X
5
)
3,5,7 1/5,1/3,1 1/7,1/5,1/3 1/7,1/5,1/3 1,1,1 3,5,7 1/9,1/7,1/5 1/7,1/5,1/3 1/5,1/3,1 0.066
Complexity (X
6
) 1/3,1,1 1/3,1,1 1/3,1,1 3,5,7 1/7,1/5,1/3 1,1,1 1,1,3 1/5,1/3,1 1/3,1,1 0.108
Growth rate per year (X
7
) 7,9,9 1/5,1/3,1 1/5,1/3,1 7,9,9 5,7,9 1/3,1,1 1,1,1 1/7,1/5,1/3 1/5,1/3,1 0.146
Size (X
8
) 5,7,9 3,5,7 1/3,1,1 5,7,9 3,5,7 1,3,5 3,5,7 1,1,1 1,3,5 0.180
Leveraging factors (X
9
) 1,3,5 1/5,1/3,1 1,1,3 5,7,9 1,3,5 1,1,3 1,3,5 1/5,1/3,1 1,1,1 0.124
V (S
C1
≥ S
C2
, S
C3
, S
C4
, S
C5
, S
C6
, S
C7
, S
C8
, S
C9
) = 0.283
V (S
C2
≥ S
C1
, S
C3
, S
C4
, S
C5
, S
C6
, S
C7
, S
C8
, S
C9
) = 0.441;
V (S
C3
≥ S
C1
, S
C2
, S
C4
, S
C5
, S
C6
, S
C7
, S
C8
, S
C9
) = 0.748;
V (S
C4
≥ S
C1
, S
C2
, S
C3
, S
C5
, S
C6
, S
C7
, S
C8
, S
C9
) = 0.614;
V (S
C5
≥ S
C1
, S
C2
, S
C3
, S
C4
, S
C6
, S
C7
, S
C8
, S
C9
) = 0.368;
V (S
C6
≥ S
C1
, S
C2
, S
C3
, S
C4
, S
C5
, S
C7
, S
C8
, S
C9
) = 0.600;
V (S
C7
≥ S
C1
, S
C2
, S
C3
, S
C4
, S
C5
, S
C6
, S
C8
, S
C9
) = 0.809;
V (S
C8
≥ S
C1
, S
C2
, S
C3
, S
C4
, S
C5
, S
C6
, S
C7
, S
C9
) = 1.000;
V (S
C9
≥ S
C1
, S
C2
, S
C3
, S
C4
, S
C5
, S
C6
, S
C7
, S
C8
) = 0.689.
M. H. Aghdaie et al. Market segment evaluation and selection based on application ...
227
Table 4. Initial decision- making matrix with the criteria values described in intervals
1
x⊗
2
x⊗
3
x⊗
4
x⊗
5
x⊗
6
x⊗
7
x⊗
8
x⊗
9
x⊗
opt Min Min Min Max Min Min Max Max Max
q
i
0.051 0.079 0.135 0.111 0.066 0.108 0.146 0.180 0.124
11
,xx
22
,xx
33
,xx
44
,xx
55
,xx
66
,xx
77
,xx
88
,xx
99
,xx
SEG 1 40 60 40 60 80 90 70 80 20 30 60 70 80 90 60 70 50 60
SEG 2 70 80 50 60 60 70 80 90 40 50 70 80 90 95 50 60 60 70
SEG 3 50 60 70 80 60 70 60 70 30 40 60 70 80 90 70 80 60 70
It indicates the initial decision making matrix, with the criterion values described in
intervals. For the weight of criteria, we used weights of the last column of Table 3.
The initial decision making matrix has been normalized rst as discussed in section
COPRAS-G method. The normalized decision-making matrix is presented in Table 5.
Table 5. Normalized weighted decision making matrix
1
ˆ
x⊗
2
ˆ
x⊗
3
ˆ
x⊗
4
ˆ
x⊗
1
PP P
YC Y C
KK K Y
−= −
6
ˆ
x⊗
7
ˆ
x⊗
8
ˆ
x⊗
9
ˆ
x⊗
Opt. Min Min Min Max Min Min Max Max Max
11
,xx
22
,xx
33
,xx
44
,xx
55
,xx
66
,xx
77
,xx
88
,xx
99
,xx
SEG 1
0.016 0.019 0.018 0.026 0.051 0.057 0.035 0.04 0.013 0.01 0.032 0.03 0.044 0.05 0.056 0.065 0.034 0.041
SEG 2
0.022 0.026 0.038 0.044 0.04 0.045 0.026 0.032 0.037 0.043 0.05 0.052 0.047 0.056 0.041 0.047
SEG 3
0.013 0.016 0.031 0.036 0.038 0.044 0.03 0.035 0.019 0.026 0.032 0.037 0.044 0.05 0.065 0.074 0.041 0.047
Table 6 summarizes the results. The higher degree means the better rank, so based on
the results of Table 6, the ranking of the three segments is “SEG 3>SEG 1 >SEG 2”.
Table 6. Evaluation of utility degree
Segment
P
j
R
j
Q
j
N
j
SEG 1 0.1825 0.1399 0.3359 98.52%
SEG 2 0.189 0.154 0.3284 96.38%
SEG 3 0.1937 0.146 0.3407 100%
P
j
hybrid approach results indicate that the best alternative with the highest degree is
the best segment for doing marketing activities. So, based on the proposed methodol-
ogy, SEG 3 could be selected as the best segment for the problem of market segment
evaluation and selection in the Nilper manufacturing company.
Journal of Business Economics and Management, 2013, 14(1): 213–233
228
6. Conclusion
Market environment becomes more and more competitive and companies should make
right decisions about marketing problems. One of the important problems is market
segment evaluation and selection. Market segment evaluation and selection is a criti-
cal managerial marketing activity for all the companies. It helps a company choose its
target segment or segments so that company can focus its competitive advantages, its
resources, its opportunities and marketing strategies on effectively satisfying custom-
ers’ needs and wants. In this paper, a hybrid MCDM methodology based on fuzzy AHP
and COPRAS-G method for selecting the most suitable market segment was proposed.
Fuzzy AHP is used to calculate the weight of each criterion, and COPRAS-G method
is proposed to prioritize market segments from the best to the worst ones. This appli-
cation has indicated that the model can be efciently used in evaluating and selecting
segments. Although the application of the model proposed in this study is specic to
market segment evaluation and selection, it can also be used with slight modications
in decision-making process.
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Mohammad Hasan AGHDAIE was born in 1986 in Iran. In 2009 he received a Bachelor of Industrial
Engineering – Industrial Production from Shomal University, in Amol. In 2011 he received a Master
of Industrial Engineering – Productivity and System Management from Shomal University. His current
research interests include Operations research, Decision analysis, Multiple Criteria Decision Analysis
and their applications, especially market related decisions, Market segmentation, Marketing research
and modeling, Market Design and Engineering, Data mining , Application of Fuzzy sets and systems,
Creative Thinking and Problem Solving and Pricing. He has published some papers in journals and
conference proceedings.
Sarfaraz HASHEMKHANI ZOLFANI got a BS in Industrial Management and MS in Industrial
Engineering- Productivity and System Management from Shomal University of Amol, Iran. He was
accepted in M.S. without national exam because he was ranked as the top student and regarding his
good GPA in B.S. He is the author of more than 40 scientic papers in International Conferences
and International Journals which were published, accepted or under reviewing. His research interests
include Performance Evaluation, Strategic Management, Decision-making Theory, Supply Chain Man-
agement, (Fuzzy) Multi Criteria Decision Making and Marketing.
Edmundas Kazimieras ZAVADSKAS. Prof, Head of the Department of Construction Technology and
Management at Vilnius Gediminas Technical University, Vilnius, Lithuania. He has a PhD in Building
Structures (1973) and Dr Sc. (1987) in Building Technology and Management. He is a member of
the Lithuanian and several foreign Academies of Sciences. He is Doctore Honoris Causa at Poznan,
Saint-Petersburg, and Kiev universities as well as a member of international organizations; he has been
a member of steering and programme committees at many international conferences. E. K. Zavadskas
is a member of editorial boards of several research journals. He is the author and co-author of more
than 400 papers and a number of monographs in Lithuanian, English, German and Russian. Research
interests are: building technology and management, decision-making theory, automation in design and
decision support systems.
Journal of Business Economics and Management, 2013, 14(1): 213–233
