# Strategy-aligned fuzzy approach for market segment evaluation and selection: a modular decision support system by dynamic network process (DNP)

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## Abstract

In competitive markets, market segmentation is a critical point of business, and it can be used as a generic strategy. In each segment, strategies lead companies to their targets; thus, segment selection and the application of the appropriate strategies over time are very important to achieve successful business. This paper aims to model a strategy-aligned fuzzy approach to market segment evaluation and selection. A modular decision support system (DSS) is developed to select an optimum segment with its appropriate strategies. The suggested DSS has two main modules. The first one is SPACE matrix which indicates the risk of each segment. Also, it determines the long-term strategies. The second module finds the most preferred segment-strategies over time. Dynamic network process is applied to prioritize segment-strategies according to five competitive force factors. There is vagueness in pairwise comparisons, and this vagueness has been modeled using fuzzy concepts. To clarify, an example is illustrated by a case study in Iran's coffee market. The results show that success possibility of segments could be different, and choosing the best ones could help companies to be sure in developing their business. Moreover, changing the priority of strategies over time indicates the importance of long-term planning. This fact has been supported by a case study on strategic priority difference in short- and long-term consideration.

## Keywords

Market segmentation Decision support system (DSS) Dynamic network process Fuzzy logic Risk## Background

Porter 1980) described a category scheme including three general types of strategies: Cost leadership, differentiation, and market segmentation which are commonly used by various businesses to achieve and maintain competitive advantages. These three generic strategies are defined along two dimensions: strategic scope and strategic strength. Strategic scope is a demand-side dimension and looks at the size and composition of the market you intend to target. Strategic strength is a supply-side dimension and looks at the strength or core competency of the firm. Market segmentation is narrow in scope when both cost leadership and differentiation are relatively broad in market scope. Market segmentation divides the market into homogeneous groups of individual markets with similar purchasing response as a number of smaller markets have differences based on geography, demographics, firm graphics, behavior, decision-making processes, purchasing approaches, situation factors, personality, lifestyle, psychographics, and product usage (Aaker 1995; Bonoma and Shapiro 1983; Dickson 1993; Kotler 1997; Bock and Uncles 2002; Nakip 1999; File and Prince 1996). The results of segmentation could be improved considerably if information on competitors is considered in the process of market segmentation (Söllner and Rese 2001). Market segmentation allows the marketing program to focus on a special part of the market to increase its competitiveness by applying various strategies. These strategies can be new products development, differentiated marketing communications, advertisements creation, different customer services development, prospects targeting with the greatest potential profits, and multi-channel distribution development. Many researchers developed the evaluation and selection of market segmentation methods to achieve more customer satisfaction by focusing on marketing programs designed to satisfy customer requirements efficiently. The vast majority of decision-making methods have focused on evaluating the different segmentation methods and techniques (Kuo et al. 2002; Lu 2003; Coughlan and Soberman 2005; Liu and Serfes 2007; Ou et al. 2009; Phillips et al. 2010; Tsai et al. 2011a, 2011b). In the market segment evaluation and selection, there are four stages or procedures that were introduced by Montoya-Weiss and Calentone (2001): problem structuring, segment formation, segment evaluation and selection, and description of segment strategy.

Distinction of segmentation at a strategic or at an operational level has been made by several authors such as Goller et al. (2002) and Sausen et al. (2005). The general assumption behind the dimension is that there is a fundamental difference in how the firm is affected by the segmentation (Clarke and Freytag 2008). At a strategic level, the consideration is on the top management level and concerns the creation of missions and strategic intent, and can become closely linked to the capabilities and nature of the organization (Jenkins and McDonald 1997). At the operational level, there is a concern for planning and operational schemes for reaching target segments with an effectively adjusted offering as well as monitoring the performance (Albert 2003). In a competitive market, strategies are critical points of business, which lead the companies towards their vision as their final destination. Strategy description and selection is an important part of strategic management process. Many approaches, techniques, and tools can be used to analyze strategic cases in this process (Dincer 2004). Ray (2000) applied strategic segmentation where, prior to price competition, each firm targets the information to specific consumers who are informed by a firm that they can buy from it.

Among the strategic tools, SPACE matrix (Rowe et al. 1982) is a common method. It is used as a strategy description and success evaluation technique that includes two dimensions: internal perspectives (financial strength (FS) and competitive advantage (CA)) and external perspectives (environmental stability (ES) and industry strength (IS)).

**Market segment evaluation and selection of literatures**

Article | Segmentation by competitive factors | Strategy-aligned approach | Uncertainty issues | Interdependency | Risk analysis | Time-dependent decision making |
---|---|---|---|---|---|---|

Ou et al. (2009) | ✓ | ✓ | ✓ | - | - | - |

Liu et al. (2010) | ✓ | - | - | - | - | - |

Ren et al. (2010) | ✓ | ✓ | - | - | - | - |

Tsai et al. (2011a) | ✓ | ✓ | - | - | - | - |

Tsai et al. (2011b) | ✓ | - | - | ✓ | - | - |

Xia (2011) | ✓ | ✓ | - | - | - | - |

Aghdaie et al. (2011) | ✓ | - | ✓ | - | - | - |

Shani et al. (2012) | ✓ | - | - | - | ✓ | - |

Proposed model | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |

As it is observed in the above table, recent researches have considered the important factors of this problem, but none of them provides a comprehensive model. Also, time-dependent decision making is an affective item that is provided in this paper, which was not considered in previous works.

In this paper, a strategy-aligned fuzzy approach is developed to select the best segment-strategy in market segment evaluation and selection problem. A modular decision-making process is implemented in two stages:

The rest of this paper is organized as follows: In the ‘Methods’ section, the dynamic network process is shown including the explanation of its applications in the next section. The ‘Fuzzy fundamental’ section presents a brief overview of the fuzzy concepts. In section ‘Fuzzy dynamic network process’, the fuzzy DNP calculation method is presented. In the ‘Results and discussion’ section, a procedure for segment-strategy selection is introduced, including how to select an optimum solution. A case study with its computational results is also presented for the proposed model. The final section gives the conclusions and future works.

## Methods

### Time-dependent analytic network process

*n*. In ANP, the problem is to obtain the limiting result of powers of the super-matrix with dynamic priorities. Because its size will increase in the near future, the super-matrix would have to be solved numerically (Saaty 2007). In the numerical solution, the best fitting curves for the components of the eigenvector were obtained by plotting the principal eigenvector for the indicated values of

*t.*In the analytical solution for the pairwise comparison judgments in dynamic conditions, Saaty (2007) purposed some functions for the dynamic judgments, which are given in Table 2.

**Mathematician's formulation of a dynamic judgment scale**

Time dependent importance intensity | Description | Explanation |
---|---|---|

| Constant for all | No change in relative standing |

| Linear relation in | Steady increase in value of one activity over another |

| Logarithmic growth up to a certain point and constant thereafter | Rapid increase (decrease) followed by slow increase (decrease) |

${c}_{1}{e}^{{c}_{2}t}+{c}_{3}$ | Exponential growth (or decay if | Slow increase (decrease) followed by rapid increase (decrease) |

| A parabola giving a maximum or minimum (depending on | Increase (decrease) to maximum (minimum) and then decrease (increase) |

| Oscillatory | Oscillates depending on |

Catastrophes | Discontinuities indicated | Sudden changes in intensity |

To solve the problem and to obtain the time-dependent principal eigenvector, Saaty (2007) introduced the numerical approach by simulation, in which at first, the judgments express functionally but then derives the eigenvector from the judgments for a fixed instant of time, substitutes the numerical values of the eigenvectors obtained for that instant in a super-matrix, solves the super-matrix problem, and derives the priorities for the alternatives. This process is repeated for different values of time, which generates a curve for the priorities of the alternatives and then approximates these values by curves with a functional form for each component of the eigenvector. This procedure is used in this paper to obtain the priorities of the alternatives in fuzzy dynamic network process (FDNP).

#### Why dynamic network process?

In a decision-making process of selecting market segments, priorities are calculated based on competitive factors with respect to some important criteria such as the effects of the interdependency among the factors and the trend of segment-strategy priorities in various time horizons. Dynamic network process as a powerful decision-making method can cover these important criteria by considering interdependency in networks in a dynamic decision-making process. Thus, DNP is a more useful method that can be applied to prioritize the alternatives in comparison with other decision-making processes.

### Fuzzy fundamental

Fuzzy set theory was introduced by Zadeh (1965) to deal with the uncertainty caused by imprecision and vagueness in real world conditions. A fuzzy set is a class of objects with a continuum of grades of membership, which assigns to each object a grade of membership ranging between zero and one (Kahraman et al. 2003).

*l*,

*m*,

*u*), where the triplet (

*l*,

*m*,

*u*) are crisp numbers and

*l*≤

*m*≤

*u*. These parameters

*l*,

*m*and

*u*denote the smallest possible value, the most promising value, and the largest possible value, respectively. The triplet (

*l*,

*m*,

*u*) as a TFN has a membership function with following form:

#### Fuzzy operations for TFNs

- 1)Addition of two fuzzy numbers:$\tilde{A}\oplus \tilde{B}=\left({l}_{\mathrm{A}}+{l}_{\mathrm{B}},{m}_{\mathrm{A}}+{m}_{\mathrm{B}},{u}_{\mathrm{A}}+{u}_{\mathrm{B}}\right)$
- 2)Multiplication of two fuzzy numbers:$\tilde{A}\otimes \tilde{B}=\left({l}_{\mathrm{A}}{l}_{\mathrm{B}},{m}_{\mathrm{A}}{m}_{\mathrm{B}},{u}_{\mathrm{A}}{u}_{\mathrm{B}}\right),\mathrm{where}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{A}}\phantom{\rule{0.25em}{0ex}}\mathrm{and}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{B}}\ge 0$
- 3)Multiplication of a crisp number
*k*and a fuzzy number:$k.\tilde{A}=\left(k{l}_{\mathrm{A}},k{m}_{\mathrm{A}},k{u}_{\mathrm{A}}\right),\mathrm{where}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{A}}\phantom{\rule{0.25em}{0ex}}\mathrm{and}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{B}}\ge 0$ - 4)Division of two fuzzy numbers:$\tilde{A}\mathrm{\Delta}\tilde{B}=\left({l}_{\mathrm{A}}/{u}_{\mathrm{B}},{m}_{\mathrm{A}}/{m}_{\mathrm{B}},{u}_{\mathrm{A}}/{l}_{\mathrm{B}}\right),\mathrm{where}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{A}}\phantom{\rule{0.25em}{0ex}}\mathrm{and}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{B}}\ge 0$
- 5)Addition of two fuzzy numbers:$\tilde{A}\oplus \tilde{B}=\left({l}_{\mathrm{A}}+{l}_{\mathrm{B}},{m}_{\mathrm{A}}+{m}_{\mathrm{B}},{u}_{\mathrm{A}}+{u}_{\mathrm{B}}\right)$
- 6)Multiplication of two fuzzy numbers:$\tilde{A}\otimes \tilde{B}=\left({l}_{\mathrm{A}}{l}_{\mathrm{B}},{m}_{\mathrm{A}}{m}_{\mathrm{B}},{u}_{\mathrm{A}}{u}_{\mathrm{B}}\right),\mathrm{where}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{A}}\phantom{\rule{0.25em}{0ex}}\mathrm{and}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{B}}\ge 0$
- 7)Multiplication of a crisp number
*k*and a fuzzy number:$k.\tilde{A}=\left(k{l}_{\mathrm{A}},k{m}_{\mathrm{A}},k{u}_{\mathrm{A}}\right),\mathrm{where}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{A}}\phantom{\rule{0.25em}{0ex}}\mathrm{and}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{B}}\ge 0$ - 8)Division of two fuzzy numbers:$\tilde{A}\phantom{\rule{0.12em}{0ex}}\mathrm{\Delta}\phantom{\rule{0.12em}{0ex}}\tilde{B}=\left({l}_{\mathrm{A}}/{u}_{\mathrm{B}},{m}_{\mathrm{A}}/{m}_{\mathrm{B}},{u}_{\mathrm{A}}/{l}_{\mathrm{B}}\right),\mathrm{where}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{A}}\phantom{\rule{0.25em}{0ex}}\mathrm{and}\phantom{\rule{0.25em}{0ex}}{l}_{\mathrm{B}}\ge 0$

#### Linguistic variables and fuzzy numbers

**Linguistic scales for difficulty and importance**

Linguistic scale for difficulty | Linguistic scale for importance | Triangular fuzzy scale | Triangular fuzzy reciprocal scale |
---|---|---|---|

Just equal | Just equal | (1, 1, 1) | (1, 1, 1) |

Equally difficult | Equally important | (1/2, 1, 3/2) | (2/3, 1, 2) |

Weakly more difficult | Weakly more important | (1, 3/2, 2) | (1/2, 2/3, 1) |

Strongly more difficult | Strongly more important | (3/2, 2, 5/2) | (2/5, 1/2, 2/3) |

Very strongly more difficult | Very strongly more important | (2, 5/2, 3) | (1/3, 2/5, 1/2) |

Absolutely more difficult | Absolutely more important | (5/2, 3, 7/2) | (2/7, 1/3, 2/5) |

**Linguistic values and mean of fuzzy numbers**

Linguistic values for negative sub-factors | Linguistic values for positive sub-factors | The mean of fuzzy numbers |
---|---|---|

Very low | Very high | 1 |

Low | High | 0.75 |

Medium | Medium | 0.5 |

High | Low | 0.25 |

Very high | Very low | 0 |

#### Why fuzzy logic?

In most of cases, pairwise comparisons are vague because every decision has its special specifications. Using fuzzy numbers is a powerful tool to overcome the uncertainty and vagueness of data. On the other hand, pairwise comparisons with linguistic variables are easier for experts. Fuzzy set theory was introduced by Zadeh (1965) to deal with uncertainty due to imprecision and vagueness; since then, many applications have been developed in fuzzy decision-making processes. For computational efficiency, trapezoidal or triangular fuzzy numbers are usually used to represent fuzzy numbers (Klir and Yuan 1995). In this paper, TFNs are used to make the mathematics manageable and easy to understand, and to facilitate presentation of the case.

### Fuzzy dynamic network process

- 1)
Statement of the problem

- 2)
Assumptions of the fuzzy prioritization method

- 3)
Solving the fuzzy prioritization problem that has survived as follows:

*n*elements, decision maker provides a set of $F=\left\{{\tilde{a}}_{\mathit{ij}}\right\}$ of

*m*≤

*n*(

*n*- 1) / 2 pairwise comparison judgments, where

*i*= 1, 2, …,

*n*- 1,

*j*= 2, 3, …,

*n*,

*j*>

*i*, represented as triangular fuzzy numbers ${\tilde{a}}_{\mathit{ij}}=\left({l}_{\mathit{ij}},{m}_{\mathit{ij}},{u}_{\mathit{ij}}\right)$. A crisp priority vector

*w*= (

*w*

_{1},

*w*

_{2}, …,

*w*

_{ n }) could reach from the problem with the fuzzy condition as follows:

*P*on the (

*n*- 1) dimensional simplex

*Q*

^{n - 1}:

The non-linear problem (7) will be optimized where *λ* = *λ*^{*} and *W* = *W*^{*}, and the fuzzy judgment will be satisfied if the *λ*^{*} is positive. Also, it can be applied as the consistency measure of the initial set of fuzzy judgments. When the value of *λ*^{*} is negative, the solution ratios approximately satisfy all double-side inequalities (1), that means, the fuzzy judgments are inconsistent. To obtain the time-dependent principal eigenvector, *W*^{*} should be calculated for different values of time *N*_{t}. These eigenvectors (*W*^{*}) are used to generate a curve that shows the alternative priority in each period. The alternative curves are gathered in a graph that could help DMs to select the best option.

## Results and discussion

### Procedure of segment-strategy selection

In this section, a procedure for segment-strategy selection is developed in ten steps to select the best potential segment with its strategies by considering an acceptable risk and in five competitive forces factors which have been developed by Porter (1980). According to this procedure, the market segments and strategies are selected in two main modules of a decision support system. In the first step, the risk amount is assigned by SPACE matrix method, and the segments are filtered based on special acceptable risk level which has been defined by DMs. In the second step, there are some segments which come from the first step. For every segment, some strategies are defined according to their position on SPACE matrix. DNP method in fuzzy environment has been applied to rank the segment-strategies.

Regarding this model, DM will be able to select the segments that have more chance of success according to their risk amount and to select proper strategies in each segment with competitive conditions. These steps are defined as follows:

- 1
Develop appropriate factors based on SPACE dimensions including internal perspectives (FS and CA) and external perspectives (ES and IS)

- 2
Assign relevant scores for each factor of segments and compute the total score in each dimension (internal and external); then, trace the position of each segment on SPACE graph

- 3
Assign a proper risk level for each segment and omit the segments which are out of the acceptable risk level (ARL)

- 4
Define feasible strategies for each segment and make a list of segment-strategy

- 1
Develop proper factors to choose the best segment-strategy, considering the vision statement

- 2
Compare factors for each alternative by considering the time variation and determine the effect of factors on each other

- 3
Calculate the score by FDNP for each segment-strategy according to the five competitive force factors

- 4
Make a discussion based on the score and choose the best segment-strategy

A case study is illustrated to select an optimum segment-strategy for a special coffee product in Iran market with regard to the procedure that was introduced before. While coffee is not technically a commodity, coffee is bought and sold by roasters, investors, and price speculators as a tradable commodity insofar as coffee has been described by many, including historian Pendergrast (1999), as the world's ‘second most legally traded commodity’. Decaffeination is the act of removing caffeine from coffee beans. As of 2009, progress towards growing coffee beans that do not contain caffeine is still continuing (Mazzafera et al. 2009). Consumption of decaffeinated coffee appears to be as beneficial as caffeine-containing coffee in regard to all-cause mortality, according to a large prospective cohort study (Brown et al. 1993). Decaffeinated products are produced in a coffee firm in Iran as a special product that can be put into the narrow markets from a demand perspective, particularly in the Middle East area. In Middle East, tea is a more popular beverage than coffee. This decreases the demand of coffee as a substitute product (especially decaffeinated coffee which has not existed before) in comparison with tea.

**Segments in the coffee market**

Segments | Remarks |
---|---|

S | Supplies Iran market with branded products |

S | Exports branded products to the Middle East |

S | Supplies Iran market with bulk products |

S | Exports products in bulk to the Middle East |

S | Produces branded products for other brands |

### Segment filtering based on risk amount

#### Definition of segment positions

**Factors and sub-factors of the risk definition model**

Factors | Sub-factors | S1 | S2 | S3 | S4 | S5 |
---|---|---|---|---|---|---|

Environmental stability (ES) | Demand variation (ES | 5 | 4 | 4 | 3 | 5 |

Competitor prices (ES | 3 | 3 | 2 | 2 | 4 | |

Inflation rate (ES | 5 | 3 | 5 | 3 | 4 | |

Technology improvement rate (ES | 3 | 4 | 3 | 3 | 3 | |

Elasticity of demand (ES | 4 | 3 | 5 | 3 | 3 | |

Industry strength (IS) | Supply chain management (IS | 2 | 3 | 4 | 3 | 5 |

Potential growth ability (IS | 3 | 4 | 3 | 3 | 3 | |

Profitability (IS | 6 | 4 | 3 | 2 | 3 | |

Optimal resources consumption (IS | 4 | 4 | 4 | 4 | 4 | |

Optimal capacity usage (IS | 3 | 4 | 3 | 4 | 3 | |

Financial strength (FS) | Liquidity power (FS | 2 | 3 | 3 | 4 | 3 |

Investment returns (FS | 6 | 4 | 3 | 2 | 3 | |

Working capital (FS | 5 | 6 | 3 | 3 | 2 | |

Cash flow (FS | 5 | 4 | 4 | 3 | 4 | |

Ease of leaving the market (FS | 3 | 3 | 4 | 3 | 2 | |

Competitive advantage (CA) | Market share (CA | 3 | 4 | 2 | 3 | 2 |

Product quality (CA | 4 | 5 | 4 | 5 | 3 | |

Customer loyalty (CA | 3 | 4 | 3 | 3 | 4 | |

Technology (CA | 2 | 3 | 2 | 3 | 2 | |

Product distribution power (CA | 4 | 3 | 2 | 3 | 1 |

The scores should be between 0 to 6, where 6 indicates the best condition and 0 indicates the worst for positive factors (financial strength and industry strength) and vice versa for negative factors (environmental stability and competitive advantage). For example, the amount of *product distribution power* (CA_{5}) which is a sub-factor of *competitive advantage* as a negative factor is 1 in S_{5}, which means there are suitable conditions to distribute the products in S_{5} in comparison with the competitors. On the other hand, the amount of *Profitability* (IS_{3}) as a positive factor of *industry strength* is 6 in S_{1}, which means there are suitable conditions to produce the product with high profitability in S_{1} in comparison with other products in the other segments. According to these scores, total scores are calculated in each dimension of SPACE matrix using (8) and (9). The position of each segment is traced on SPACE graph according to the obtained pairs. It could assign a proper risk amount to each segment. Segment filtering will be done according to the assigned risk amounts and by a certain acceptable risk level.

_{ x }

^{ j },

*y*

^{ j }) shows the position of segment

*j*, where

*x*and

*y*are horizontal and vertical dimensions of the SPACE matrix, respectively. These pairs are calculated based on the sub-factor scores in two dimensions, where

*x*is calculated by (8) and

*y*by (9).

*S*

_{1}is (2,1), and in this way, other positions are calculated as follows:

#### Definition of risk levels

Different points on SPACE matrix show the success possibility of each segment that is considered as risk amount of segments. The most possibility occurs when financial strength and industry strength get the most score as positive factors, and environmental stability and competitive advantage get the lowest score as negative factors. So, the pair (6, 6) has the most success possibility with lowest risk amount in the SPACE matrix, and the pair (-6,-6) has the most risk amount. Risk of other points is defined based on their distance from (6, 6). The surface of the SPACE matrix is separated into five areas according to the distance from the best point. These areas are defined by radiuses which have been calculated based on fuzzy approach. It means that the Euclidean distance from the worst and the best points has been separated into five sections according to the linguistic values and the mean of fuzzy numbers (Table 3 and Figure 5). Thus, the Euclidean distance of each segment from the best point towards the worst point can show a level of risk.

*x*

^{ j }

*, y*

^{ j }) show the position of segment

*j*. Let (

*X*,

*Y*) show the best position, and (

*X*′,

*Y*′) show the worst position in a SPACE graph. The risk amount of (

*x*

^{ j }

*, y*

^{ j }) is defined based on its Euclidean distance proportion that is showed as follows:

**Risk levels based on Euclidean distance proportion**

Linguistic variables | Euclidean distance proportion |
---|---|

Very low | Between 0 and 1/5 |

Low | Between 1/5 and 2/5 |

Medium | Between 2/5 and 3/5 |

High | Between 3/5 and 4/5 |

Very high | Between 4/5 and 1 |

_{1}and S

_{2}are selected to define proper strategies, and S

_{3}, S

_{4}, and S

_{5}are rejected because of their high risk levels.

**Proper risks for each segment with regard to Euclidean distance**

Segments | Euclidean distance | Proper risks |
---|---|---|

S | 0.377 | Low |

S | 0.395 | Low |

S | 0.486 | Medium |

S | 0.507 | Medium |

S | 0.648 | High |

### Strategy definition

Strategy definition is done by SPACE matrix. Generic strategies of SPACE matrix are defensive, aggressive, conservative, and competitive which could be broken into the main strategies. In this case, aggressive and conservative strategies are suitable for S_{2}, and aggressive strategies for S_{1}. The two main strategies were defined for S_{2} from two different views: the first one is ‘putting decaffeinated coffee in old basket’ as conservative strategy, and the second one is ‘A new basket of decaffeinated coffee products with decaffeinated coffee stores development’ as aggressive strategy. The aggressive strategy that was defined for S_{1} is ‘A new basket of decaffeinated coffee and decaffeinated coffee stores development’.

### Select the best segment-strategy

**Factors and sub-factors of the five forces model**

Factors | Sub-factors |
---|---|

The bargaining power of supplier (F | Supplier concentration (F |

Importance of order volume to supplier (F | |

Presence of substitute inputs (F | |

Switching cost of firms in the industry (F | |

Differentiation of inputs (F | |

Threat of perceived level of product (F | |

The bargaining power of customer (F | Price sensitive (F |

Substitutes available (F | |

Buyer concentration (F | |

Product differentiation (F | |

Brand identification (F | |

The threat of substitute products (F | The quality of substitute products (F |

Buyer inclination to substitute (F | |

Relative price performance of substitute (F | |

The threat of new entrants (F | Brands (F |

Access to distribution (F | |

Access to input (F | |

Government policy (F | |

Capital requirement (F | |

The intensity of competitive rivalry (F | Exit barriers (F |

Fixed cost and value added (F | |

Number of competitors (F | |

Brand identification (F | |

Product differences (F | |

Switching cost (F |

**Pair-wise comparison matrix of factors with local weights**

Factor | F | F | F | F | F | Weight |
---|---|---|---|---|---|---|

The bargaining power of supplier (F | (1, 1, 1) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (2/5, 1/2, 2/3) | (1, 3/2, 2) | 0.1577 |

The bargaining power of customer (F | (1, 3/2, 2) | (1, 1, 1) | (1/2, 1, 3/2) | (1/2, 2/3, 1) | (3/2, 2, 5/2) | 0.2172 |

The threat of substitute products (F | (1, 3/2, 2) | (2/3, 1, 2) | (1, 1, 1) | (1/2, 2/3, 1) | (3/2, 2, 5/2) | 0.2172 |

The threat of new entrants (F | (3/2, 2, 5/2) | (1, 3/2, 2) | (1, 3/2, 2) | (1, 1, 1) | (2, 5/2, 3) | 0.2947 |

The intensity of competitive rivalry (F | (1/2, 2/3, 1) | (2/5, 1/2 ,2/3) | (2/5, 1/2, 2/3) | (1/3, 2/5, 1/2) | (1, 1, 1) | 0.1132 |

The non-linear programming presented as follows resulted from pairwise comparisons and was solved using the LINGO 11 (2008) software (Lindo Systems Inc., Chicago). The other weights were calculated using the same approach for each pairwise comparison matrix.

Maximize = *λ*

**The inner dependence matrix of the factors with respect to ‘F** _{ 4 } **’**

F | F | F | F | F | Weight |
---|---|---|---|---|---|

F | (1, 1, 1) | (1/2, 2/3, 1) | (2/5, 1/2, 2/3) | (2/3, 1, 2) | 0.1875 |

F | (1, 3/2, 2) | (1, 1, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | 0.2724 |

F | (3/2, 2, 5/2) | (1, 3/2, 2) | (1, 1, 1) | (1, 3/2, 2) | 0.3081 |

F | (1/2, 1, 3/2) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 1, 1) | 0.2320 |

**The inner dependence matrix of the factors with respect to ‘F** _{ 1 } **’**

F | F | F | Weight |
---|---|---|---|

F | (1, 1, 1) | (2/5, 1/2, 2/3) | 0.3333 |

F | (3/2, 2, 5/2) | (1, 1, 1) | 0.6667 |

**The inner dependence matrix of the factors with respect to ‘F** _{ 2 } **’**

F | F | F | Weight |
---|---|---|---|

F | (1, 1,1 ) | (3/2, 2, 5/2) | 0.6667 |

F | (2/5, 1/2, 2/3) | (1, 1, 1) | 0.3333 |

*w*

_{Factors}) are calculated by multiplying the normalized degree matrix (Table 14) with the local weight of the factors that had been calculated before in Table 10.

**Degree of relative impact for the factors**

Factor | F | F | F | F | F |
---|---|---|---|---|---|

F | 0.500 | 0.000 | 0.000 | 0.094 | 0.000 |

F | 0.000 | 0.500 | 0.000 | 0.136 | 0.000 |

F | 0.167 | 0.333 | 1.000 | 0.154 | 0.000 |

F | 0.000 | 0.000 | 0.000 | 0.500 | 0.000 |

F | 0.333 | 0.167 | 0.000 | 0.116 | 1.000 |

**Pair-wise comparison matrix of F** _{ 1 } **sub-factors with local weight**

F | F | F | F | F | F | F | Weight |
---|---|---|---|---|---|---|---|

F | (1, 1, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1/2, 1, 3/2) | (1, 3/2, 2) | (3/2, 2, 5/2) | 0.1962 |

F | (1, 3/2, 2) | (1, 1, 1) | (3/2, 2, 5/2) | (1, 3/2, 2) | (3/2, 2, 5/2) | (3/2, 2, 5/2) | 0.2385 |

F | (1/2, 2/3, 1) | (2/5, 1/2, 2/3) | (1, 1, 1) | (1/2, 2/3, 1) | (1/2, 1, 3/2) | (1,3/2, 2) | 0.1351 |

F | (2/3, 1, 2) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1, 1, 1) | (1, 3/2, 2) | (3/2, 2, 5/2) | 0.1884 |

F | (1/2, 2/3, 1) | (2/5, 1/2, 2/3) | (2/3, 1, 2) | (1/2, 2/3, 1) | (1, 1, 1) | (1, 3/2, 2) | 0.1351 |

F | (2/5, 1/2, 2/3) | (2/5, 1/2, 2/3) | (1/2, 2/3, 1) | (2/5, 1/2, 2/3) | (1/2, 2/3, 1) | (1, 1, 1) | 0.1067 |

**Pair-wise comparison matrix of F** _{ 2 } **sub-factors with local weight**

F | F | F | F | F | F | Weight |
---|---|---|---|---|---|---|

F | (1, 1, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1, 3/2, 2) | (1/2, 2/3, 1) | 0.1964 |

F | (1, 3/2, 2) | (1, 1, 1) | (1, 3/2, 2) | (1, 3/2, 2) | (1/2, 2/3, 1) | 0.2339 |

F | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 1, 1) | (1/2, 2/3, 1) | (2/5, 1/2, 2/3) | 0.1329 |

F | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1, 1, 1) | (2/5, 1/2, 2/3) | 0.1583 |

F | (1, 3/2, 2) | (1, 3/2, 2) | (3/2, 2, 5/2) | (3/2, 2, 5/2) | (1, 1, 1) | 0.2785 |

**Pair-wise comparison matrix of F** _{ 3 } **sub-factors with local weight**

F | F | F | F | Weight |
---|---|---|---|---|

F | (1, 1, 1) | (2/5, 1/2, 2/3) | (1/2, 2/3, 1) | 0.2239 |

F | (3/2, 2, 5/2) | (1, 1, 1) | (1, 3/2, 2) | 0.4584 |

F | (1, 3/2, 2) | (1/2, 2/3, 1) | (1, 1, 1) | 0.3177 |

**Pair-wise comparison matrix of F** _{ 4 } **sub-factors with local weight**

F | F | F | F | F | F | Weight |
---|---|---|---|---|---|---|

F | (1, 1, 1) | (1, 3/2, 2) | (1, 3/2, 2) | (3/2, 2, 5/2) | (1, 3/2, 2) | 0.2854 |

F | (1/2, 2/3, 1) | (1, 1, 1) | (1, 3/2, 2) | (1, 3/2, 2) | (1, 3/2, 2) | 0.2327 |

F | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 1, 1) | (1, 3/2, 2) | (1/2, 2/3, 1) | 0.1610 |

F | (2/5, 1/2, 2/3) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 1, 1) | (1/2 ,2/3, 1) | 0.1312 |

F | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1, 3/2, 2) | (1, 1, 1) | 0.1897 |

**Pair-wise comparison matrix of F** _{ 5 } **sub-factors with local weight**

F | F | F | F | F | F | F | Weight |
---|---|---|---|---|---|---|---|

F | (1, 1, 1) | (2/5, 1/2, 2/3) | (2/5, 1/2, 2/3) | (2/5, 1/2, 2/3) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | 0.1030 |

F | (3/2, 2, 5/2) | (1, 1, 1) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1, 3/2, 2) | 0.1782 |

F | (3/2, 2, 5/2) | (1, 3/2, 2) | (1, 1, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1, 3/2, 2) | 0.2046 |

F | (3/2, 2, 5/2) | (1, 3/2, 2) | (1, 3/2, 2) | (1, 1, 1) | (1, 3/2, 2) | (1, 3/2, 2) | 0.2349 |

F | (1, 3/2, 2) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 1, 1) | (1/2, 2/3, 1) | 0.1300 |

F | (1, 3/2, 2) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1/2, 2/3, 1) | (1, 3/2, 2) | (1, 1, 1) | 0.1493 |

**Global weights for sub-factors and computed total weight of each**

Factor | Sub-factors | Local weight | Global weights |
---|---|---|---|

F | F | 0.1962 | 0.0209 |

F | 0.2385 | 0.0254 | |

F | 0.1351 | 0.0144 | |

F | 0.1884 | 0.0201 | |

F | 0.1351 | 0.0144 | |

F | 0.1067 | 0.0114 | |

F | F | 0.1964 | 0.0292 |

F | 0.2339 | 0.0348 | |

F | 0.1329 | 0.0198 | |

F | 0.1583 | 0.0235 | |

F | 0.2785 | 0.0414 | |

F | F | 0.2239 | 0.0809 |

F | 0.4584 | 0.1656 | |

F | 0.3177 | 0.1148 | |

F | F | 0.2854 | 0.0421 |

F | 0.2327 | 0.0343 | |

F | 0.1610 | 0.0237 | |

F | 0.1312 | 0.0193 | |

F | 0.1897 | 0.0280 | |

F | F | 0.1030 | 0.0243 |

F | 0.1782 | 0.0421 | |

F | 0.2046 | 0.0482 | |

F | 0.2349 | 0.0555 | |

F | 0.1300 | 0.0306 | |

F | 0.1493 | 0.0353 |

_{1}-startegy

_{1}is preferred over the others at first, although its priority is decreased during the planning horizon. In the end, segment

_{2}-startegy

_{2}becomes more interesting than the others. On the other hand, results show that the priority of segment

_{2}-startegy

_{2}is preferred almost after 1 year; thus, it could be selected for a long-term strategic planning.

**Global weights for each segment-strategy and computed total weight in each year**

Segment-strategy | Year (total weight) | ||||
---|---|---|---|---|---|

2013 | 2014 | 2015 | 2016 | 2017 | |

Seg | 0.487 | 0.383 | 0.318 | 0.295 | 0.294 |

Seg | 0.312 | 0.285 | 0.261 | 0.236 | 0.213 |

Seg | 0.201 | 0.332 | 0.421 | 0.469 | 0.493 |

## Conclusion

The purpose of the current study is to provide a modular decision support system to determine the best marketing strategy with an acceptable risk. This DSS helps companies to select appropriate segments to develop their business while they can care about their risk. Also, they can consider the effects of the strategies in their success based on priorities which may be changed over time. Two modules have been developed in this study: the first one used the SPACE matrix to allocate the risk to each segment, and the second one used FDNP method to monitor the segment-strategies over time and select the best one accordingly.

In the first module, segments have been evaluated based on the four main factors (and their sub-factors) of the SPACE matrix, and their risk have been calculated according to their success possibility. Then, the segments have been filtered with regard to their risk level which had been defined using the fuzzy approach. This method helps managers to take their acceptance risk level into consideration and leads DMs to select segments with their reasonable risk levels. Moreover, the SPACE matrix helps managers define proper strategies, too. Filtered segments help them have more suitable alternatives in the decision-making process.

In the second module, the five forces model of Porter (1980) has been developed in a decision-making process to select the best segment-strategy. Because of the changing conditions in the market and the decreasing or increasing attractiveness of the alternatives, the alternative priorities are changed over time, so the FDNP is developed to consider the variation of segment priorities. As it is clear in the numerical results, time variation could affect the DMs' decision. The priority of segment_{1}-startegy_{1} is more preferred over the others at first, although its priority decreased during the planning horizon. In the end, segment_{2}-startegy_{2} becomes more interesting than the others. On the other hand, results show that the priority of segment_{2}-startegy_{2} is more preferred almost after 1 year; thus, it could be selected for a long-term strategic plan.

Market segmentation is one of the most important issues in marketing process of industries such as food, dairy, beverage, home care, etc. In this process, risk consideration is very essential because it may have big effects on the expected results. The proposed method in this paper could mitigate this risk by bringing the risk into calculation, and it could be applied to mitigate risk consequences. Using this method, DM could filter its alternative and will not count on segments which are in high risk space. As a result, DM will not select strategies based on high risk segments, and the company could lead its investment to the most secure space. As shown in the results, segment 5 (S_{5}) has the maximum risk of selection because in this segment, environment stability is weaker than the other potential segments. Hence, disregarding risk factors and selecting S_{5} as a potential segment, the company will enter an unstable market. In this way, the other steps of strategy definition such as distribution channels, pricing, and long-term and short-term strategies will undergo selected market instability. So, disregarding the risk effects could lead a business to the spaces which can decrease the possibility of success.

On the other hand, for each segment, a special strategy could be developed while the importance of each segment-strategy has its special trend over time. Practically, when a company is going to invest on segment-strategy, it should have a serious attention on the long-term results of its decision. In this condition, having a good view on the trend of segment-strategy importance could help DMs make effective decisions over time. In considering this issue, the developed FDNP method of this paper could be applied. The application of this method in industries will be more significant when they have marketing strategies such as pricing, distribution channels, and promotion in their appropriate segment.

Considering the risk amount and competitive factors with their effects on each other will drive the company to be more successful. Analysis effects of these strategies to decrease risk amount could be helpful in making a better and more complete decision merits future research.

## Authors’ information

AMN received his MSc from the Industrial Engineering Department of K. N. Toosi University of Technology of Iran. He has work experience in automotive and food production companies. Now, he is working in the PSIG Co. as Business Development Manager. His research interests are project portfolio selection, strategic management, market segmentation and multi-attribute decision making, supply chain management, and risk management. MHH received his MSc from the Industrial Engineering Department of Islamic Azad University of Qazvin. He has work experience in automotive, food and FMCG production companies. Now, he is working in Unilever Company as a demand planner. His research interests are project portfolio selection, strategic management, market segmentation, and multi-attribute decision making. SE is an assistant professor of operation research in the Islamic Azad University, Karaj Branch. He received his PhD in Operation Research and Operation Management from Science and Research Branch of the Islamic Azad University (SRBIAU), MSc from Amirkabir University of Technology (Tehran Polytechnic), and BS from Iran University of Science and Technology. His research interests include risk management, construction projects selection, fuzzy MADM, scheduling, mathematical programming, shortest path networks, and supply chain management.

## Notes

### Acknowledgments

The authors are thankful for the support of the Multicafe Co. for the research process of Iran coffee market and segment-strategies definition. The anonymous reviewers are acknowledged for their constructive comments as well as the editorial changes suggested by the language editor, which certainly improved the presentation of the paper.

## Supplementary material

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