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262 Int. J. Operational Research, Vol. 24, No. 3, 2015
Copyright © 2015 Inderscience Enterprises Ltd.
Target market selection based on market segment
evaluation: a multiple attribute decision making
approach
Mohammad Hasan Aghdaie*
Department of Industrial Engineering,
Shomal University,
P.O. Box 731, Amol, Mazandaran, Iran
Email: mh_aghdaie@yahoo.com
*Corresponding author
Maryam Alimardani
Department of Industrial Engineering,
College of Engineering,
University of Tehran,
P.O. Box: 11155-4563, Tehran, Iran
Fax: +9821-88013102
Email: m.alimardani@ut.ac.ir
Abstract: Target market selection is one of the most important marketing
decisions for many companies. Besides, many other decisions of an
organisation such as market mix, procurement and distribution channels are
affected by this decision. An appropriate target market selection is performed
upon market segment evaluation results and considering many factors such as
segment size, number of competitors, risk and profitability. Moreover,
multiple-attribute decision making (MADM) tools are used as a natural
approach for evaluating alternatives with respect to conflict criterion, and target
market selection can be considered as an MADM problem. To this end, a novel
hybrid MADM method including AHP and TOPSIS is proposed to elicit a
suitable target market. More precisely, AHP is derived to calculate each
criterion’s weight and TOPSIS is applied to rank target market alternatives
from the best to the worst ones. Additionally, a case study is presented to
validate the developed framework.
Keywords: marketing; market segmentation; target market selection; market
segment evaluation and selection; multiple-attribute decision making; MADM;
analytic hierarchy process; AHP; technique for order preference by similarity to
ideal solution; TOPSIS.
Reference to this paper should be made as follows: Aghdaie, M.H. and
Alimardani, M. (2015) ‘Target market selection based on market segment
evaluation: a multiple attribute decision making approach’, Int. J. Operational
Research, Vol. 24, No. 3, pp.262–278.
Biographical notes: Mohammad Hasan Aghdaie received his Bachelor’s and
Master’s in Industrial Engineering from Shomal University, in Amol. He is the
author of more than 21 scientific papers in international conferences and
international journals which were published, accepted or under review. His
Target market selection based on market segment evaluation 263
current research interests include operations research, decision analysis,
multiple criteria decision analysis, operations research interfaces with other
fields, especially marketing, market segmentation, marketing research and
modelling, market design and engineering, pricing, data mining, data science,
application of fuzzy sets and systems, creative thinking and problem solving.
He has published in journals such as Journal of Business Economics and
Management, International Journal of Business Innovation and Research, The
Baltic Journal of Road and Bridge Engineering, Quarterly Journal of Research
and Planning in Higher Education, Engineering Economics, Expert systems
with Applications, Technological and Economic Development of Economy and
several others.
Maryam Alimardani received her BSc in Industrial Engineering from Shomal
University, Amol, Iran in 2009. She received her MSc in Industrial Engineering
from University of Tehran, Tehran, Iran in 2012. Her major research interests
include production planning and control, supply chain management, supply
chain inventory management, and multi-criteria decision-making. Her papers
have appeared in journals such as International Journal of Production
Research and International Journal of Civil and Structural Engineering.
1 Introduction
Target market is the heart of many other marketing decisions such as market mix
selection, procurement, supply chain, distribution channels, etc. Market targeting
involves some steps, so that the attractiveness of each market segment is firstly assessed
and then one (more) segment is selected to enter (Kotler, 2011). In other words, target
market selection is the output of market segment evaluation and selection decision.
Hence, market segment evaluation is used as a natural approach for target market
selection in several organisations.
Market segmentation process is considered to partition a market into distinct subsets
of customers and a subset could be possibly selected as a target market to be reached with
a distinct marketing mix (Kotler and Keller, 2005). After market segmentation process
was performed, the companies assess their segments and elicit the target market(s) upon
evaluating the identified market segments. It is introduced as a critical management
decision, since a lot of components of a marketing strategy follow the market
segmentation problem.
In many companies, an appropriate target market selection upon evaluating the
supposed segments is identified as one of the most complicated and time consuming
problems due to many feasible alternatives, conflicting objectives and variety of factors.
Therefore, target market selection can be viewed as a multiple-attribute decision making
(MADM) problem.
The MADM methods deal with the process of finding the best option from the all
feasible alternatives in the presence of multiple, usually conflicting, decision criteria.
Analytic hierarchy process (AHP) which date back to Saaty (1980) is one of the most
excellent MADM methods. The AHP can simplify a complex and ill-structured problem
by arranging the decision attributes and alternatives in a hierarchical structure with the
help of a series of pair-wise comparisons (Pirdashti et al., 2011). Furthermore, AHP is
one of the most desirable methods to determine the relevant weight of the factors and the
264 M.H. Aghdaie and M. Alimardani
total values of each alternative based on the weights in multi attribute problems. Besides,
the AHP calculates the inconsistency index which is the ratio of the DM’s inconsistency
(Önüt et al., 2008). Occasionally in some decision making problems a DM should
perform a large number of pairwise comparisons which cause impractical usage of the
AHP process. To cope with this problem, another MADM technique, namely Technique
for Order Preference by Similarity to Ideal Solution (TOPSIS) can be used to reduce the
number of pair-wise comparisons and to rank the alternatives.
In the present study, AHP is applied to calculate the weight of each criterion based on
decision maker (DM) judgments. Then, the obtained weights are utilised in TOPSIS
which was introduced by Hwang and Yoon in 1981 to rank the supposed alternatives. It is
noted, the TOPSIS computes the distance of each alternative to the positive and negative-
ideal solutions and the best option is identified based on these distances.
The roadmap of the paper is as organised as follows. Section 2 explains the literature
review of market segment evaluation and target market selection. Section 3 outlines the
proposed methodology combining AHP and TOPSIS. The paper follows in Section 4 on a
real case study to show the applicability of the proposed model and results are reported in
Section 5. Finally, conclusion and future researches directions are provided in Section 6.
2 Literature review
Since numerous researchers addressed different aspects of target market selection and
market segmentation problem, we provide only the studies which have been recently
published in order to conclude contributions of this paper in comparison with the body of
literature. Additionally, literature body of MADM methodology is briefly reviewed in
this section.
2.1 Target market selection and market segmentation
In the literature body, evaluation and selection of market segments are introduced as a
target market selection problem, since target market selection is structured upon
analysing the customer needs and the relative attractiveness of different customer
segments (McDonald and Dunbar, 2004). According to Weinstein (2004) companies
must carefully assess and weigh key discriminating criteria to find the ‘best’ market
segments.
McQueen and Miller (1985) suggested three criteria including profitability,
variability, and accessibility to evaluate the market attractiveness. Loker and Perdue
(1992) studied a systematic approach based on profitability, accessibility, reachability for
market segment evaluation. Besides, Simkin and Dibb (1998) introduced profitability,
market growth, and market size as the most important factors for target market selection.
It must be noted, some criteria such as identity-ability, substantiality, accessibility,
stability, responsiveness, and action-ability have been frequently put forward to
determine the effectiveness and profitability of market segment (Baker, 1988; Kotler,
1988). According to Kotler and Armstrong (2003), the market segments should be
consider upon five selection criteria including
1 measurable
2 accessible
Target market selection based on market segment evaluation 265
3 sustainable
4 differentiable
5 actionable to be viable.
Besides, Morrison (2009) augmented five more criteria including homogeneity,
defensibility, competitiveness, durability, and compatibility to Kotler and Armstrong’s
list leading to enhance the effectiveness of segmentation evaluation. Moreover, Jang et al.
(2002) incorporated the profitability and risk concepts in evaluating segment
attractiveness as more quantifiable and comprehensive profitability measures. In addition,
McDonald and Dunbar (2004) developed a comprehensive criteria list of 27 possible,
generalised segment attractiveness factors in five major areas including segment factors,
competition, financial and economic, technology, and sociopolitical factors. And, Lu
(2003) has evaluated international distribution centres based on the factors which are
related to distribution centres (i.e., cargo safety, cargo tracing service, inland
transportation, and customs clearance).
Ou et al. (2009) applied five forces analysis which is introduced by Porter (1979) to
evaluate and select market segments for international business using a strategy-aligned
fuzzy approach. Zandi et al. (2012) suggested an approach which combines bi-level
multi-objective optimisation with ROA and fuzzy cooperative n-person game theory for
market segment evaluation and selection. Taaffe et al. (2008) developed a profit
maximising model to address the firm’s integrated market selection, marketing effort, and
procurement decisions.
However there are a wide variety of researches in the related subject, some
general criteria are considered for evaluation and a few of them developed a
mathematical/conceptual model. On the other hand, general market segmentation studies
have paid little or no attention to the evaluation and selection process (Beane and Ennis,
1987; Weinstein, 1987).
2.2 Multiple attribute decision making
MADM is one the many sub-disciplines of operations research/management science.
Nowadays, the several number of publications in the field of MADM has been grown
rapidly in many fields for decision making such as tourism (Cheng et al., 2010), supplier
selection (Kumar et al., 2008; Raut et al., 2012), advanced manufacturing (Nagar and
Raj, 2012), supply chain management (Chaabane et al., 2010), air forces (Tavana et al.,
2008), investments (Tang and Beynon, 2009) and so on.
Based on the nature of the target market selection problem, multiple and sometimes
conflict criteria should be considered in the decision making process. Therefore, target
market selection can be viewed as a MADM problem. The MADM methods deal with the
process of making decisions for finding the optimum alternative in the presence of
multiple, usually conflicting, decision criteria. Hence, the aim of the paper is to consider
different criteria and to organise a hierarchical target market selection model in order to
overcome the shortcomings of the previous papers. Despite of the literature body, the
authors propose a novel hybrid decision making approach to rank and select the most
suitable target market among the potential target markets.
266 M.H. Aghdaie and M. Alimardani
3 Proposed target market selection framework
This section presents a novel hybrid MADM method including AHP and TOPSIS
methods. AHP method is supposed to calculate the relevant weight of the factors and the
total values of each alternative by arranging the decision attribute and alternatives in a
hierarchical structure based on DM judgments. Next, the obtained weights of criteria
from AHP are utilised as the primary information about the relative importance of the
criteria in the TOPSIS method. Having the developed model applied, the alternatives are
efficiently evaluated and the most suitable one is selected. The procedures of the
proposed method are also described as follows.
3.1 Hybridisation of AHP and TOPSIS for target market selection
3.1.1 The AHP methodology
AHP is developed by Saaty (1990) and it can be considered as one the most outstanding
MADM tools. The main point behind this technique is how to determine the relative
importance of a set of activities in a multi-criteria decision problem (Aghdaie et al.,
2012). According to Badri (2001) in AHP, a DM could incorporate and translate
judgments on intangible qualitative criteria alongside tangible quantitative criteria (Badri,
2001). This technique not only helps with the analysis of arriving at the best decision but
also provides a clear rational orientation to the made choices, involves the principles of
decomposition, pair-wise comparisons and the generation and synthesis of priority
vectors (Zolfani et al., 2012). This technique is base on three steps: first, structure of the
model; second, comparative judgment of the alternatives and the criteria; third, synthesis
of the priorities (Dağdeviren, 2008). The procedure of applying the AHP consists of the
following steps:
In the first step, a sophisticated decision problem is structured as a hierarchy. This
method breaks down a sophisticated decision making problem into hierarchy of
objectives, criteria, and alternatives.
These decision elements make a hierarchy of structure including goal of the problem
at the top, criteria in the middle and the alternatives at the bottom of this hierarchy.
In the second step, the comparisons of the alternatives and criteria are happen. In
AHP, comparisons are happen based on a standard nine point scale (Table 1).
Table 1 Nine-point intensity of importance scale and its description
Definition Intensity of importance
Equally important 1
Moderately more important 3
Strongly more important 5
Very strongly more important 7
Extremely more important 9
Intermediate values 2, 4, 6, 8
Source: Saaty (1990)
Target market selection based on market segment evaluation 267
Let C = {Cj | j = 1,2,…, n} be the set of criteria. The result of the pairwise comparison on
n criteria can be summarised in an (n × n) evaluation matrix A in which every element
aij(i, j = 1,2,…, n) is the quotient of weights of the criteria, as shown in equation (1):
11 12 1
21 22 2
12
,1,1,0
n
n
ii ji ij ij
nn nn
aa a
aa a
Aaaaa
aa a
⎡⎤
⎢⎥
⎢⎥
===≠
⎢⎥
⎢⎥
⎣⎦
…
…
…
(1)
At the third step, the mathematical process commences to normalise and find the relative
weights for each matrix. The relative weights are given by the right eigenvector (w)
corresponding to the largest eigenvalue (λmax), as:
max .Aw
λ
w= (2)
If the pairwise comparisons are completely consistent, the matrix A has rank 1 and
λmax = n.
In this case, weights can be obtained by normalising any of the rows or columns of A
(Wang and Yang, 2007). The quality of the output of the AHP is strictly related to the
consistency of the pairwise comparison judgments (Dağdeviren, 2008). The consistency
is defined by the relation between the entries of A: aij × ajk = aik. The consistency index
(CI) is
()()
max
CI = 1λnn−−
(3)
The final consistency ratio (CR), using which one can conclude whether the evaluations
are sufficiently consistent, is calculated as the ratio of the CI and the random index (RI),
as indicated in equation (4).
CR CI / RI= (4)
Average consistency values of these matrices are given by Saaty and Vargas (1991) as
provided in Table 2.
Table 2 Values for R.I.
n 2 3 4 5 6 7 8
R.I. 0.00 0.52 0.90 1.12 1.24 1.32 1.41
Source: Saaty and Vargas (1991)
The CR index should be lower than 0.10 to accept the AHP results as consistent (Isiklar
and Büyüközkan, 2007). If the final consistency ratio exceeds this value, the evaluation
procedure has to be repeated to improve consistency (Dağdeviren, 2008). The CR index
could be used to calculate the consistency of DMs as well as the consistency of all the
hierarchy (Wang and Yang, 2007).
3.1.2 TOPSIS
TOPSIS as a widely accepted MADM method dates back to the one by Hwang and Yoon
(1981). The best alternative in TOPSIS should have the shortest distance from the ideal
solution that maximises the benefit and also minimises the total cost, and the farthest
268 M.H. Aghdaie and M. Alimardani
distance from the negative-ideal solution that minimises the benefit and also maximises
the total cost (Opricovic and Tzeng, 2003). TOPSIS has some advantageous including
sound logic, simultaneous consideration of the ideal and the anti-ideal solutions, and
easily programmable computation procedure (Önüt et al., 2008; Dağdeviren et al., 2009).
The TOPSIS procedure consists of the following steps:
Step 1 In the first step normalised decision matrix is calculated. The normalised value
{rij} is calculated by equation (5).
2
1
, 1,,; 1,,.
ij
ij J
ij
j
f
rjJin
f
=
===
∑
…… (5)
Step 2 Calculate the weighted normalised decision matrix. The weighted normalised
value vij is yield applying equation (6).
,1,,;1,,,
ij i ij
vwrj Ji n=× = =…… (6)
where wi is the weight of the ith criterion, and
1
1.
n
i
i
w
=
=
∑
Step 3 In the next step, the ideal and negative-ideal solutions are determined through
equation (7) and equation (8).
{
}
()
(
)
{}
**
1
*,, max ',min ".
nij ij
j
j
Av v viI viI==∈ ∈… (7)
{
}
()
(
)
{}
1,, min ',max ".
nij ij
jj
Av v viI viI
−− −
==∈ ∈… (8)
where I′ is associated with benefit criteria, and I″ is associated with cost criteria.
Step 4 In this step, the separation measures are calculated using the n-dimensional
Euclidean distance. The separation of each alternative from the ideal solution is
given as in equation (9).
()
2
**
1
,1,,.
n
jiji
i
DvvjJ
=
=−=
∑… (9)
Similarity, the separation from the negative-ideal solution is given as follows:
()
2
*
1
,1,,.
n
jiji
i
DvvjJ
−
=
=−=
∑… (10)
Step 5 Calculate the relative closeness to the ideal solution. The relative closeness of
the alternative aj with respect to A* is defined as equation (11).
()
*
*,1,,.
j
j
jj
D
CjJ
DD
−
−
==
+… (11)
Target market selection based on market segment evaluation 269
Step 6 Rank the preference order. The index values of *
j
C lie between 0 and 1. The
larger index value means the closer to ideal solution for alternatives.
3.1.3 The evaluation procedure
In this paper we proposed a MADM approach based on combination of AHP and
TOPSIS for target market selection. For selecting the best target market, identified
segments were evaluated based on selected criteria from literature survey in depth. There
are some reasons for using MADM approaches. Firstly, MADM methods deal with the
process of selecting the best alternative among existing alternatives with respect to many
conflicting qualitative and quantitative multiple criteria based on DM judgments.
Secondly, determining and evaluating all these factors is a difficult task for DM.
This section describes a three phase approach which is used for and target market
selection. In this conceptual model two famous MADM methodologies have been
combined. Figure 1 describes the evaluation procedure of this study which consists of
three main phases:
Figure 1 The evaluation procedure
Construction of selection criteria and
problem structure
Criteria weights by AHP
Identification of selection criteria
Constructing decision making team
Determining qualitative and quantitative
criteria for evaluation process
Assigning evaluations via AHP
Select the best target market
Assigning evaluations for TOPSIS
computations
Ranking via TOPSIS
Phase I
Phase II
Phase III
270 M.H. Aghdaie and M. Alimardani
Table 3 Factors taken from the review of the related literature which are relevant to target
market selection
No. Criteria Sub-criteria Related literature source
C1 Segment related
C1–1 Growth rate per year Lee et al. (2006) and Simkin and
Dibb (1998)
C1–2 Size McDonald and Dunbar (2004) and
Simkin and Dibb (1998)
C1–3 Suppliers ability Morrison (2002) and McDonald
and Dunbar (2004)
C1–4 Homogeneity Kotler and Armstrong (2003) and
Loker and Perdue (1992)
C1–5 Accessible Morrison (2002)
C1–6 Sustainable Kotler and Armstrong (2003)
C1–7 Competition McDonald and Dunbar (2004) and
Ou et al. (2009)
C2 Financial and economic
C2–1 Profitability McQueen and Miller (1985), Loker
and Perdue (1992), Jang et al.
(2002) and Simkin and Dibb (1998)
C2–2 Risk Jang et al. (2002)
C3 Technological
C3–1 Knowledge, experience,
information, and manufacturing
process technology required
McDonald and Dunbar (2004) and
Kotler and Armstrong (2003)
C3–2 Complexity McDonald and Dunbar (2004)
Figure 2 Target market selection structure, criteria and alternatives
C
1
Alternative 2
Alternative 1
Target market selection
C
2
C
3
Alternative 3 Alternative 4
C
1-2
C
1-5
C
1-3
C
1-4
C
1-7
C
1-6
C
1-1
C
2-2
C
2-1
C
3-2
C
3-1
Alternatives
Objective
Criteria
Target market selection based on market segment evaluation 271
Table 4 The characteristics of the five decision-making experts
Gender Age Education level Experience (years) Job title Job responsibility
Decision-making
expert 1 (D1)
Male 54 Master’s in management > 30 Manager of a company In charge of the most important
decisions of the company.
Decision-making
expert 2 (D2)
Male 50 Master’s in industrial
engineering
> 25 Project manager and
supply chain analysis
Managing the engineering team, supply
chain, suppliers and new projects.
Decision-making
expert 3 (D3)
Male 53 Bachelor’s in statistics > 20 Quality control and
maintenance manager
Managing repair and maintenance
programmes, teams. Designing new
programmes for improving quality and
processes.
Decision-making
expert 4 (D4)
Male 48 Bachelor’s in marketing > 15 Advertising manager
and market
segmentation analyst
Designing new advertising projects and
market segmentation projects.
Decision-making
expert 5 (D5)
Female 49 Doctor of philosophy’s in
business administration
> 17 Marketing and sales
manager
Responsible for R&D, new products,
marketing research and pricing
decisions.
272 M.H. Aghdaie and M. Alimardani
Phase 1 After constructing decision making team, the most important criteria for target
market selection was identified. Next, the qualitative and quantitative criteria
were defined. The criteria listed in Table 3 are selected based on the literature
survey. Finally, the project team constructed the selection criteria and problem
structure (see Figure 2).
Phase 2 Criteria weights was calculated by applying AHP method and based on experts
‘evaluations.
Phase 3 In this stage, all alternatives were evaluated by project team and TOPSIS
method was applied to achieve the final ranking results.
4 Case study
A real case study problem has been chosen to show the performance and application of
the model. The study was conducted by a well-known company in chair manufacturing
industry. This company is an average sized manufacturing company which has 200
employers. This company offers more than 50 models of managerial, administrative, and
clinical chairs based on customer needs and ergonomic standards. Recently, there has
been a regular increase in request for products of this company and company need to
conduct a new market research project. Also, other competitors start to change their
target markets, so the company has decided to carry out a market research project. For
these reasons, a market research project was done and four segments were defined as
alternatives, which are denoted as SEG 1 (segment 1), SEG 2 (segment 2), SEG 3
(segment 3) and SEG 4 (segment 4), respectively. Therefore manager of the company has
to choose target market based on this survey results for doing other marketing activities.
Consequently, the project team with background in management and chair manufacturing
industry was constructed. Information about experts is shown in Table 4. The project
team identified four potential target markets as alternatives for evaluation. The
alternatives denoted as SEG 1, SEG 2, SEG 3 and SEG 4, respectively. For receiving
general agreement in every step of this project, face to face interviews and Delphi method
were used. Finally, project team identified criteria for evaluation and they constructed
problem structure (see Figure 2). Then the project team accepted the criteria list that was
explored from the literature study (see Table 3). After finishing this phase it’s time to
start second phase. As above mentioned, in this paper AHP was used for calculating
criteria weights.
5 Results
After representing the case study, proposed model, selecting the project team, identifying
most important criteria for evaluating, representing the decision model and using
analytical techniques, the remaining part of the study will focus on the obtained
numerical results. The hierarchical structure of decision problem consists of four levels:
at the high level the objective of the problem is situated while in the second level, the
criteria, in the third level the sub-criteria are listed. The goal is selecting the most suitable
target market for the company. The evaluation criteria are C1, C2, and C3. The C1 has
seven sub-criteria including C1-1, C1-2, C1-3, C1-4, C1-5, C1-6 and C1-7 (see Figure 2). The C2
Target market selection based on market segment evaluation 273
and C3 have two sub-criteria including C2-1, C2-2 and C3-1, C3-2. Among all criteria C1-7,
C2-2, C3-1, and C3-2 are a cost criterion (the minimum amount of this criterion is desirable)
and others are benefit criteria. In this phase, AHP method was used to tackle the need for
evaluation of the data. The all pairwise comparisons and the weights of criteria are
showed in Table 5 to Table 8. Equations (1) to (4) were used for AHP calculations. The
last column was shown the weight of each criterion in every table. Table 5 shows the
weight of each criterion which is located in the second level of problem structure, based
on pairwise comparisons. Tables 6 to 8 show the criteria weight of the three sub-criteria
of the problem structure.
Table 5 Pairwise comparison matrix for criteria and weights
C1 C
2 C
3 Weights
C1 1 3 1/2 0.3
C2 1/3 1 1/6 0.6
C3 2 6 1 0.1
Table 6 Pairwise comparison matrix for segment related sub-criteria and their weighs
C1-1 C
1-2 C
1-3 C1-4 C
1-5 C1-6 C
1-7 Weights
C1-1 1 1/3 2 1 1/4 2 2 0.13
C1-2 3 1 3 1/2 1 2 1/3 0.16
C1-3 1/2 1/3 1 1/2 1 1/2 1/2 0.08
C1-4 1 2 2 1 1/2 3 2 0.18
C1-5 4 1 1 2 1 2 3 0.23
C1-6 1/2 1/2 2 1/3 1/2 1 1/2 0.08
C1-7 1/2 3 2 1/2 1/4 2 1 0.14
Table 7 Pairwise comparison matrix for financial and economic sub-criteria and their weighs
C2-1 C
2-2 Weights
C2-1 1 1/2 0.33
C2-2 2 1 0.67
Table 8 Pairwise comparison matrix for technological sub-criteria and their weighs
C3-1 C
3-2 Weights
C3-1 1 1/5 0.17
C3-2 5 1 0.83
Furthermore, Consistency ratio of the pairwise comparison matrix calculated for all of the
tables was lower than 0.1. So the weights are consistent and they are used in the selection
process.
In the third step, TOPSIS methodology was used for ranking alternatives. For creating
weighted normalised decision matrix, weight of each sub-criterion should be calculated
with respect to weight of upper criterion. So, weight of each sub-criterion is calculated by
multiplying weight of each sub-criterion and criteria. Figure 3 shows the weight of each
sub-criterion with respect to each other.
274 M.H. Aghdaie and M. Alimardani
Figure 3 The normalised weights of the sub-criteria (see online version for colours)
The weighted normalised decision matrix was shown in Table 9. TOPSIS methodology is
used numbers of this table for calculating the distance of each alternative to the ideal
solution and negative-ideal solution.
Table 9 The weighted normalised decision matrix
C
1-1 C
1-2 C
1-3 C
1-4 C
1-5 C
1-6 C
1-7
SEG 1 0.018 0.109 0.005 0.021 0.021 0.008 0.021
SEG 2 0.035 0.435 0.016 0.004 0.004 0.001 0.008
SEG 3 0.013 0.118 0.011 0.019 0.019 0.010 0.017
SEG 4 0.022 0.217 0.002 0.041 0.041 0.015 0.029
C
2-1 C
2-2 C
3-1 C
3-2
SEG 1 0.047 0.333 0.018 0.040
SEG 2 0.158 0.067 0.002 0.024
SEG 3 0.063 0.167 0.004 0.032
SEG 4 0.095 0.133 0.007 0.056
Equations (7) and (8) were used for computing the ideal and negative-ideal solution of
each criterion. Distances of each alternative to ideal solution (D*) and for non-ideal
solution (D–) is calculated based on equations (9) and (10) and the results are shown in
Table 10. Finally, based on results of Table 10 and with application of equation (11), the
final ranking of alternatives was calculated. According to that last row of Table 10 and
Figure 4, SEG 2 is defined as the best alternative for target market among four
alternatives. The final performance value for SEG 2 is 0.905; while SEG 4, SEG 3 and
SEG 1 have 0.498, 0.331 and 0.062 final performance values, respectively. Hybrid
approach results indicate that SEG 2 is the best candidate with the highest degree and is
the best market segment alternative for selection.
Target market selection based on market segment evaluation 275
Table 10 The distances of each alternative to the ideal solution and the non-ideal solution
SEG 1 SEG 2 SEG 3 SEG 4
D* 0.437 0.046 0.348 0.240
D– 0.029 0.438 0.172 0.238
C* 0.062 0.905 0.331 0.498
Rank 4 1 3 2
6 Conclusions
Nowadays, marketing becomes tough and marketing decisions can influenced every
element of an organisation. Target market is the heart of many other decisions so it is a
critical decision and political activity of many companies. Besides, the cost of changing
target market is high and sometimes it is not practical to change it soon. In this paper, a
hybrid MADM methodology with three phases based on integrating two MADM
methods for selecting the most suitable target market was proposed. According to the
results of this study, DMs faced with critical factors that were found to influence an
organisation’s decisions about selecting a new target market. Based on this research, it
was shown that DMs has a great impact on decisions in an organisation so their decisions
could influence the final outcomes. Specifically, this study provides valuable view that
DMs have a high impact on the output results. In addition, AHP method was used as a
decision making tool for extracting weights of criteria which TOPSIS needed. Next,
TOPSIS used AHP result weights as input weights. Therefore, another significant
contribution to this study is the proposed AHP – TOPSIS integrated approach.
Figure 4 Ranking of the preference order of the target market alternatives (see online version
for colours)
In general, the findings of this study have contributed towards providing important and
advanced knowledge by various criteria and a simple, efficient method with which
managers of a company or DMs can increase their ability to choose an appropriate target
market in their efforts to develop the firms’ outputs’ qualities. As a result of the study, we
276 M.H. Aghdaie and M. Alimardani
found that the proposed approach is practical for ranking target market alternatives with
respect to multiple conflicting criteria.
This integration proposed as an analytical model for dealing with complicated
marketing decision making in conflict management situations. Thus, further research can
apply this method as an adaptable approach to other situations. Also, further research
could focus on using other MADM approaches and compared with the results of this
paper. Besides, another study can be designed by using fuzzy logic or grey relation
analysis into the decision model. In this study results show that decision criteria
significantly influence the choice of target market selection. However the most important
criteria were selected based on the in-depth literature survey; another study can be
designing a new structure with other criteria, sub-criteria and assessing alternatives with a
new structure.
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