Global Journal of Management Studies and Researches, 3(3) 2016, Pages: 96-101
Academic Journals
Global Journal of Management Studies and Researches
ISSN
2345-6086
www.academicjournalscenter.org
An Integration of MCDM Methods for Marketing Strategy
Selection
Rohollah Yousefi
Masters of Executive Management, Sirjan Science and Research Branch, Islamic Azad University, Iran.
ARTICLE INFO
Keywords:
Marketing Strategy
AHP
TOPSIS
ABSTRACT
The main purpose of this paper is to propose a suitable model for determining
the appropriate marketing strategy based on MCDM. Our Proposed approach
is based on AHP and TOPSIS methods. AHP method is used in determining
the weights of the criteria by decision makers and then rankings of Strategies
are determined by TOPSIS method. A real case demonstrates the application
of the proposed method.
© 2014 Global Journal of Management Studies and Researches. All rights reserved for Academic Journals Center .
1. Introduction
Many researches show the relationship between applied strategies and company performance, suggested that performance is
influenced by adopted strategies (Cavusgil & Zou, 1994). Thus selecting an appropriate strategy can enhance corporate
performance and provide a sustainable competitive advantage for the company. On the other hand, various strategic choices
imply the need for a reasonable decision making framework (Wu, Lin, & Lee, 2010). Similarly in the marketing field, top
managers are constantly faced with the problem of how to trade off competing strategic marketing decisions. For example
should the company invest on advertising or loyalty programs, improve service quality or none of them? Such high-level
decisions need something other than decision makers' own experience and intuition (Rust, Lemon, & Zeithaml, 2004). A
marketing strategy decision can be classified as a multi- criteria decision-making (MCDM) problem. MCDM problem is a
problem in which the decision maker intends to choose one out of several alternatives on the basis of a set of criteria. It can help
users understand the results of integrated assessments, including tradeoffs among policy objectives, and use these results in a
systematic and defensible way to develop policy recommendations (Safari & Ebrahimi, 2014). In this regard, the purpose of the
current study is to model the marketing strategy decision-making problem as an MCDM problem and provide a three-step
decision support framework to carefully assess marketing strategies. About using Fuzzy AHP and VIKOR method the following
studies are cited: Chaghooshi et al (2014) applied fuzzy AHP and fuzzy VIKOR for supplier selection. In their paper, the
weights of each criterion are calculated using Fuzzy AHP method. After that, Fuzzy VIKOR is utilized to rank the alternatives.
Safari et al (2011) used fuzzy AHP and fuzzy TOPSIS for machine selection. Moradzadehfard et al (2011) applied GTMA and
fuzzy AHP for ranking of automobile companies in Tehran Stock Exchange. Safari et al (2011) applied fuzzy AHP for
formulation and ranking of Human Resource Management Strategies in Tehran Province gas Company.
Few studies consider determining the criteria for market strategy selection. The criteria which are chosen in this study are
adopted ebrahimi et al (2015).
Table 1. Criteria for marketing strategy selection (ebrahimi et al, 2015)
Criteria
Managerial capabilities (C1)
Reference
Wu et. al, 2010
An Integration of MCDM Methods for Marketing Strategy Selection …
Global Journal of Management Studies and Researches, 3( 3) 2016
Customer linking (C2)
Wu et. al, 2010
Market innovation (C3)
Wu et. al, 2010; Manu & Sriram, 1996
Human resource assets (C4)
Wu et. al, 2010
Reputational assets (C5)
Hooley et al., 1992
Corporate attitudes (C7)
Hooley et al., 1992
Performance measures (C8)
Hooley et al., 1992
Marketing channels (C9)
Paswan, Blankson, & Guzman, 2011
Marketing mix elements (C10)
Siraliova & Angelis, 2006
2. Research Methodology
In this paper, the weights of each criterion are calculated using AHP. After that, TOPSIS is utilized to rank the alternatives.
Finally, we rank the Strategies based on these results.
2.1. AHP
Saaty (2000) has evolved the AHP which can enable decision makers to represent the interaction of multiple factors in complex
situations. The process requires the decision makers to develop a hierarchical structure for the factors which are explicit in the
given problem and to provide judgments about the relative importance of each of these factors, specify a preference for each
decision alternative with respect to each factor. It provides a prioritized ranking order indicating the overall preference for each of
the decision alternatives. An advantage of the AHP over other multi criteria decision making methods is that AHP is designed to
incorporate tangible as well as non-tangible factors especially where the subjective judgments of different individuals constitute
an important part of the decision process.
The main procedure of AHP using radical root method is as follows:
• Step 1: Determine the objective and the evaluation factors. Develop a hierarchical structure with a goal or objective at the top
level, the factors at the second level, and the alternatives at the third level.
• Step 2: Find out the relative importance of different factors with respect to the goal or objective:
◦ Construct a pair-wise comparison matrix using a scale of relative importance. The judgments are entered using the fundamental
scale of the AHP as given in Table 1. Assuming N factors, the pairwise comparison of factor i with factor j yields a square matrix
A1N×N where aij denotes the relative importance of factor I with respect to factor j. In the matrix, aij = 1 when i = j and aji = 1/aij
◦ Find the relative normalized weight (Wi) of each factor by calculating the geometric mean of ith row and normalizing the
geometric means of rows in the comparison matrix.
Table 2. Relative importance of factors
Description
Relative importance (aij )
1
Equal importance of i and j
3
Moderate importance of i over j
5
Strong importance of i over j
7
Very strong importance of i over j
9
Absolute importance of i over j
2,4,6,8
Intermediate values
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Global Journal of Management Studies and Researches, 3( 3) 2016
1/𝑁
GMi = (∏𝑁
𝑗=1 𝑎𝑖𝑗 )
(1)
and
𝐺𝑀𝑖
Wi = ∑𝑁
𝑗=1 𝐺𝑀𝑖
(2)
◦ Calculate matrix A3 and A4 such that A3 = A1 × A2 and A4 = A3/A2
where A2 = [W1,W2,Wi, . . . ,WN]T
◦ Find out the maximum eigen value λmax which is the average of matrix A4.
◦ Calculate the consistency index, CI = (λmax−N) / (N − 1). The smaller the value of CI, the smaller is the deviation from the
consistency.
◦ Obtain the random index (RI) for the number of factors used in decision making. Table 2 helps the users for this purpose (Saaty,
2000).
◦ Calculate the consistency ratio, CR = CI / RI. Usually, a CR of 0.1 or less is considered as acceptable as it reflects an informed
judgment which could be attributed to the knowledge of the analyst about the problem under study.
• Step 3: The next step is to compare the candidate alternatives pairwise with respect to how much better (more dominant) in
satisfying each of the factors. It is nothing but ascertaining how well each candidate alternative serves each factor. If there are M
numbers of candidate alternatives, then there will be N number of M ×M matrices of judgments since there are N factors.
Construct pairwise comparison matrices using a scale of relative importance. The judgments are entered using the fundamental
scale N of the AHP (Saaty, 2000) .The steps are same as that suggested under Step 2. In the AHP model, both the relative and
absolute modes of comparison can be performed. The relative mode can be used when users have prior knowledge of the factors
for different alternatives to be used, or when quantitative data of the factors for different alternatives to be evaluated is not
available. The absolute mode is used when data of the factors for different alternatives to be evaluated are readily available. In
the absolute mode CI is always equal to 0 and complete consistency exists, since the exact values are used in the comparison
matrices.
Table 3. Random index
RI
Number of factors
1
0.00
2
0.00
3
0.58
4
0.90
5
1.12
6
1.24
7
1.32
8
1.41
9
1.45
• Step 4: The next step is to obtain the composite weights for the alternatives by multiplying the relative normalized weight (Wi)
of each factor (obtained in Step 2) with its corresponding normalized weight value for each alternative (obtained in Step 3) and
making summation over all the factors for each alternative.
2.2. TOPSIS
The TOPSIS method is proposed in Chen and Hwang (1992), with reference to Hwang and Yoon (1981). The basic principle is
that the chosen alternative should have the shortest distance from the ideal solution that maximizes the benefit and also
minimizes the total cost, and the farthest distance from the negative-ideal solution that minimizes the benefit and also maximizes
the total cost (Opricovic and Tzeng, 2003).
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Global Journal of Management Studies and Researches, 3( 3) 2016
The TOPSIS method consists of the following steps:
Step 1: Calculate the normalized decision matrix. The normalized value r ij is calculated as
rij = 𝑋𝑖𝑗 ⁄√∑𝑛𝑖=1 𝑋𝑖𝑗2 , ∀𝑖, 𝑗
(3)
Step 2: Calculate the weighted normalized decision matrix. The weighted normalized value vij is calculated as
vi j= wjrij, ∀i,j
(4)
Where wj is the weight of the jth criterion, and
∑𝑚
𝑖=1 𝑤𝑗 =1
Step 3: Determine the ideal and negative-ideal solution.
∗ } = {(max 𝑣 |𝑗𝜖𝐶 ) , (min 𝑣 |𝑗𝜖𝐶 )}
A* ={𝑣1∗ , … , 𝑣𝑚
𝑖𝑗
ℎ
𝑖𝑗
𝑐
𝑖
𝑖
(5)
− } = {(min 𝑣 |𝑗𝜖𝐶 ) , (max 𝑣 |𝑗𝜖𝐶 )}
A- ={𝑣1− , … , 𝑣𝑚
𝑖𝑗
ℎ
𝑖𝑗
𝑐
𝑖
𝑖
(6)
where Cb is associated with benefit criteria and Cc is associated with cost criteria.
Step 4: Calculate the separation measures, using the m-dimensional Euclidean distance. The separation of each alternative from
the ideal solution is given as
∗
𝑆𝑖∗ = √∑𝑚
𝑗=1(𝑣𝑖𝑗 − 𝑣𝑗 )
2
, ∀𝑖
(7)
Similarity, the separation from the negative-ideal solution is given as
−
𝑆𝑖− = √∑𝑚
𝑗=1(𝑣𝑖𝑗 − 𝑣𝑗 )
2
, ∀𝑖
(8)
Step 5: Calculate the relative closeness to the ideal solution. The relative closeness of the alternative Ai with respect to A* is
defined as
𝑅𝐶𝐼∗ =
𝑆𝑖−
𝑆𝑖∗ +𝑆𝑖−
, ∀𝑖
(9)
Step 6: Rank the preference order.
The index values of 𝑅𝐶𝐼∗ lie between 0 and 1. The larger index value means the closer to ideal solution for alternatives.
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Global Journal of Management Studies and Researches, 3( 3) 2016
3. A Numerical Application of Proposed Approach
This paper, the proposed methodology that may be applied to a wide range of Strategies selection problems.
AHP Method:
First of all we form pair-wise comparison matrix, after that weights of all criteria are determined by the help of AHP. The priority
of weights respect to main goal is calculated as (0.161, 0.085, 0.096, 0.137, 0.075, 0.231, 0.110, 0.043, 0.046, 0.012).
TOPSIS:
The weights of criteria are calculated by AHP up to now, and then these values can be used in TOPSIS. According to TOPSIS
methodology, we obtained weighted normalized decision matrix. After that by following TOPSIS procedure steps and
calculations, the ranking of suppliers are gained. The results and final ranking are shown in Table 4.
Table 4. Final evaluation of the alternatives
Rank
alternatives
4
A1
2
A2
5
A3
1
A4
3
A5
𝑅𝐶𝐼∗
0.322
0.456
0.314
0.589
0.412
According to Table 4, A4 is the best alternative among others.
5. Conclusions
Proper and strong marketing strategy is essential for the survival and success of any business in the increasing complex,
competitive environment of organizations. The purpose of this paper is to propose a suitable model for determining the
appropriate marketing strategy. This paper illustrates an application of AHP along with TOPSIS in selecting best marketing
strategy. A two-step AHP and TOPSIS methodology is structured here that TOPSIS uses AHP result weights as input weights.
Then a real case study is presented to show applicability and performance of the methodology.
Acknowledgement
The authors wish to thank an anonymous referee for the valuable suggestions which considerably improve the quality of the
paper.
References
1.
2.
3.
Avazpour, R, Ebrahimi, E, & Fathi, M.R (2013). A 360 Degree Feedback Model for Performance Appraisal Based on
Fuzzy AHP and TOPSIS, International Journal of Economy, Management and Social Sciences, 2 (11), 969-976.
Buckley, JJ .(1985). Fuzzy hierarchical analysis. Fuzzy Sets Syst 17:233–247.
Cavusgil, S., & Zou, S. (1994). Marketing strategy-performance relationship: An investigation of the empirical link in
export market ventures. Journal of Marketing, 58, 1-21.
100
An Integration of MCDM Methods for Marketing Strategy Selection …
Global Journal of Management Studies and Researches, 3( 3) 2016
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
Chaghooshi, A, Safari, H, & Fathi, M.R. (2012). Integration of Fuzzy AHP and Fuzzy GTMA for Location Selection of
Gas Pressure Reducing Stations: A Case Study, Journal of Management Research 4 (3), 152-169.
Chaghooshi , A, Fathi ,M.R, Avazpour, R, & Ebrahimi, E. (2014). A Combined Approach for Supplier Selection: Fuzzy
AHP and Fuzzy VIKOR, International Journal of Engineering Sciences 3 (8), 67-74.
Chang, DY .(1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research,
95:649–655.
El-ansary, A. (2006). Marketing strategy: taxonomy and frameworks. European Business Review, 18(4), 266-293.
Hooley, G., Lynch, J., & Jobber, D. (1992). Generic marketing strategies. Intern. J. of Research in Marketing, 9, 75-89.
Jain, S. (2000). Marketing, Planning and Strategy Case Book. Boston: Cengage Learning.
Jain, S., & Punj, G. (1987). Developing marketing strategy: a framework. Marketing Intelligence & Planning, 5(1), 34-9.
Kahraman, C., Cebeci, U., Ulukan, Z. (2003). Multi-criteria supplier selection using fuzzy AHP. Logist Inf Manag
16(6):382–394.
Karsak, E. E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives.
International Journal of Production Research 40(13), 3167–3181.
Kaufmann, A., and Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science.
Amsterdam: North-Holland.
Kotler, P. (1994). Marketing Management: Analysis, Planning, Implementation, and Control. New Jersey: Prentice-Hall.
Li, S., Kinman, R., Duan, Y., & Edwards, J. (2000). Computer-based support for marketing strategy development. Europian
Journal of Marketing, 34(5), 551-575.
Manu, F., & Sriram, V. (1996). Innovation, marketing strategy, environment and performance. Journal of Business
Research, 35, 79-91.
Miles, R., & Snow, C. (1978). Organizational strategy, structure, and process. New York: McGraw-Hill.
Moradzadehfard, M, Fathi, M.R, Faghih, A, & Omidian, A. (2011). Ranking of Automobile Companies in Tehran Stock
Exchange Using of GTMA and fuzzy AHP Methodology, American Journal of Scientific Research, 33-45.
Opricovic. (1998). “Multi-criteria optimization of civil engineering systems, “Faculty of Civil Engineering, Belgrade.
Opricovic, S., & Tzeng, G. H. (2002). Multi criteria planning of post earthquake sustainable reconstruction, ComputerAided Civil and Infrastructure Engineering, no.17, pp. 211–220.
Paswan, A., Blankson, C., & Guzman, F. (2011). Relationalism in marketing channels and marketing strategy. European
Journal of Marketing, 45(3), 311-333.
Porter, M. (1980). Competitive strategy: Techniques for analyzing industries and competitors. New York: The Free Press.
Rust, R., Lemon, K., & Zeithaml, V. (2004). Return on marketing: Using customer equity to focus marketing strategy.
Journal of Marketing, 68, 109–127.
Safari, H., & Ebrahimi, E. (2014). Using Modified Similarity Multiple Criteria Decision Making technique to rank
countries in terms of Human Development Index. Journal of Industrial Engineering and Management, 7(1), 254-275.
Safari, H, Fathi, M.R, & Faghih, A. (2011). Applying fuzzy AHP and fuzzy TOPSIS to Machine Selection, Journal of
American Science, 7 (9), 755-765.
Safari, H, Fathi, M.R, Omidian, A, & Zarchi, M.K. (2011). Formulation of Human Resource Management Strategies by
Integration of SWOT and ANP with Fuzzy AHP Methodology (Case Study: Tehran Province Gas Company), American
Journal of Scientific Research 29, 41-58.
Siraliova, J., & Angelis, J. (2006). Marketing strategy in the Baltics: Standardise or adapt? Baltic Journal of Management,
1(2), 169-187.
Slater, S., & Olson, E. (2001). Marketing’s contribution to the implementation of business strategy: an empirical analysis.
Strategic Management Journal, 22(11), 1055-67.
Vaidya, OS, Kumar, S .(2006). Analytic hierarchy process: an overview of applications. European Journal of Operational
Research, 169:1–29.
Van Laarhoven, PJM, Pedrcyz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11:229–241.
Wang, YJ, Lee, HS. (2007). Generalizing TOPSIS for fuzzy multicriteria group decision making. Comput Math Appl
53:1762–1772.
Wu, C., Lin, C., & Lee, C. (2010). Optimal marketing strategy: A decision-making with ANP and TOPSIS. Int. J.
Production Economics, 190–196.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information
Sciences, 8(3), 199–249.
Saaty, T.L. (2000). Fundamentals of Decision Making with the Analytic Hierarchy Process, RWS Publications, Pittsburgh.
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