Visualizing market segmentation using self-organizing maps and ...

Expert Systems with Applications 38 (2011) 198–205

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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method – ADSL market of a telecommunication company Payam Hanafizadeh *, Meysam Mirzazadeh Department of Industrial Management, Allameh Tabataba’i University, Teheran, Iran

a r t i c l e

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Keywords: Market segmentation Clustering Visualization Neural networks Self-organizing map

a b s t r a c t A common problem for marketing strategists is how to appropriately segment the market and select segment-specific marketing strategies. This paper presents a novel approach which integrates Fuzzy Delphi method, self-organizing maps (SOM) and a visualization technique to cluster customers according to their various characteristic variables and visualize segments by producing colorful market maps. These market maps not only help the managers to see fully visualized clusters of market but also reveal mutual non-linear correlations between different customers’ characteristic variables. In this research we studied ADSL service market of an Iranian Telecommunication Company. SOM algorithm and visualizing technique were implemented in MATLAB environment to produce market maps of data set. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Market segmentation refers to the identification of distinct subsets of customers, where any subset may be selected as a market target to be reached with a distinct marketing mix (Kotler, 1980). To identify these subsets consumers are put into homogeneous groups that marketing managers can select segment-specific marketing mixes and use them for effective targeting and predicting potential customers. The value of performing marketing segmentation analysis includes better understanding of the market to properly position a product in the marketplace, identifying the appropriate segments for target marketing, finding opportunities in existing markets, and gaining competitive advantage through product differentiation (Kotler, 1980). Although it was introduced into the academic marketing literature in the 50s, market segmentation continues to be an important focal point of ongoing research and marketing practices (e.g., Chaturvedi, Carroll, Green, & Rotondo, 1997). A common problem for marketing strategists is how to appropriately segment the market and package differentiated products and services for target segments. Segmentation is a methodological process of dividing a market into distinct groups that might require separate experiences or marketing service mixes (Venugopal & Baets, 1994). Customer clustering is one of the most important techniques used to identify these segments (Saarenvirta, 1998). Various clustering techniques are used for segment identification. These techniques generally take into consideration the identification and assessment of various customer characteristics (such as * Corresponding author. E-mail address: hanafi[email protected] (P. Hanafizadeh). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.06.045

demographics, socioeconomic factors, and geographic location) and product related behavioral characteristics (such as purchase behavior, consumption behavior, and attitudes towards and preference for attractions, experiences and services). Target marketing is a strategy that aims at grouping a destination’s markets into segments so as to aim at one or more of these by developing products and marketing programs tailored to each (Kotler, 2001). Although much of the marketing literature has proposed various market segmentation techniques, clustering techniques are frequently used in practice (Wedel & Kamakura, 2000). k-Means algorithm and artificial neural networks are some techniques widely considered in clustering problems (Hung & Tsai, 2008). k-Means clustering is the most frequently used market segmentation technique among the other clustering techniques (Gehrt & Shim, 1998; Kuo, Chang, & Chien, 2004). However, the major drawback of k-means clustering is that it often falls in local optima and the result largely depends on the initial cluster centers (Kim & Ahn, 2008). Prior studies pointed out this limitation and tried to integrate k-means clustering and global search techniques including genetic algorithms. For instance Kuo et al. integrated k-means, neural networks and genetic algorithm to analyze web browsing paths (Kuo, Liao, & Tu, 2005) or Kim and Ahn used GA and k-means for a recommender system (Kim & Ahn, 2008). Artificial neural networks (ANNs) have been recently applied to a wide variety of business areas (Vellido, Lisboa, & Vaughan, 1999), such as sales forecasting (Kuo & Xue, 1998), and bankruptcy prediction (Cadden, 1991). One type of unsupervised neural networks, the self-organizing maps (SOM), can project high-dimensional input space on a low-dimensional topology, allowing one to visually determine out the clusters (Lee, Slagle, & Blum, 1977; Pykett, 1978). Unlike the supervised learning methods, the SOM can be

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used for clustering data without knowing the class memberships of the input data. Despite the fact that some previous research has used SOM algorithm for market segmentation, its potential power in visualization and knowledge acquisition in marketing field is not fully developed. The main contribution of this research is the integration of Delphi Fuzzy method, Kohonens algorithm and a visualization technique, and applying them for market segmentation problem. We were motivated to use SOM for market segmentation by the fact that this algorithm can be simultaneously used to reduce the amount of data by clustering and to project the data non-linearly onto a lower-dimensional subspace. The topological relationships between the features automatically extracted by SOM from multi-spectral images are formed into a neural network in a meaningful order. This approach is efficient for contemporary market segmentation approaches which consider all customer characteristics and utilize data warehouses with huge amount of data to identify market segments. Moreover the output maps generated by SOM algorithm not only help the market managers to easily recognize all the market segments precisely but also enables them to compare market maps over time and monitor market responses of every segment. Telecommunication industry is a growing industry in Iran which has an enormous potential market for different telecommunications services. However many telecommunication companies has not conducted considerable market research yet. Hence they neither follow clear strategies nor have market mixes. In this research, SOM is employed to visualize ADSL market segments of a reputable Iranian telecommunication company. Data saved in selling system database is utilized to form dataset or research. The rest of the paper is organized as follows: Section 2 reviews market segmentation literature and describe clustering problems, comparing its techniques for market segmentation. Section 3 presents SOM algorithm and describes its learning method and visualization technique. Empirical study and data analysis is presented in Section 4. Last but not least, in Section 5 conclusions and implications are considered.

2. Market segmentation The critical role of segmentation in choosing true marketing strategies has been widely argued (Wedel & Kamakura, 2000). Market segmentation involves a broad variety of approaches (see Wedel & Kamakura, 2000 for a review). Essentially, these approaches can be divided to two main groups. First group approaches are based on known characteristics and groups are selected from a population in advance and declared as ‘segments’

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(e.g. socio-demographic characteristics or band width of an ADSL service use). In contrast, in the second group approaches, the post-hoc methods, empirical investigation is conducted using multivariate analysis to identify segments (Anable, 2005). To identify segments, respondents are clustered according to their similarity on multivariate profiles on any number of combinations of variables. These approaches may include various mixtures of attitudinal, behavioral or personality characteristics. Among the post-hoc segmentation methods, clustering methods are relatively powerful and frequently used in practice (Dillon, Kumar, & Borrero, 1993; Wedel & Kamakura, 2000). The rest of section reviews common clustering techniques used for market segmentation problems and outlines their strengths and weaknesses for market segmentation.

2.1. Clustering techniques Cluster analysis is an effective tool in scientific or managerial inquiry. It groups a set of data in d-dimensional feature space to maximize the similarity within the clusters and minimize the difference between two different clusters. Clustering methods can be roughly classified into hierarchical method and partitioning method (Witten & Frank, 2000). Partitioning clustering can be divided into exclusive and overlapping methods depending on which set theory the algorithm is built on. Exclusive clustering (e.g., k-means, SOM) is built on classic set theory where an element is an exclusive member of a set. Overlapping clustering (e.g., Fuzzy c-means) is based on fuzzy set theory where an element can be a member of one or more sets (Hwang & Thill, 2007). The k-means (MacQueen, 1966) clustering method is probably the most well known clustering technique. The accuracy of the kmeans procedure is very dependent upon the choice of the initial seeds (Milligan & Cooper, 1980) and it often falls in local optima. This can be a major disadvantage for post-hoc market segmentation cases because accurate designation of market clusters is not possible for market managers. In addition, k-means poverty in data visualization makes it comparatively an improper method for market segmentation. Fuzzy c-means (Bezdek, 1981; Roubens, 1982) is an overlapping clustering method based on fuzzy set theory. Contrary to the kmeans method the fuzzy c-means is more flexible because it shows those objects that have some interface with more than one cluster in the partition. However it has the same major drawbacks as the k-means for market segmentation problem. Kohonen algorithm and its variants have been used for different clustering problems. Kiang, Hu, and Fisher (2006) proposed an extended self-organizing map network to forecast market segment

Table 2.1 Comparison and review of clustering methods used for market segmentation. Work and year

Clustering algorithm

Application area

Pros and Cons

1

Kim and Ahn (2008)

GA k-means

Online shopping market

2

Kiang, Hu, and Fisher (2007)

Extended SOM

Telecommunication

3 4

Fuzzy clustering SOM and GA k-means

Urban housing Fright transport industry

5

Hwang and Thill (2007) Kuo, An, Wang, and Chung (2006) Bloom (2004)

Weak visualization – using GA to identify initial seed – non-overlapping clustering Non-visualized – non-overlapping clustering – independent from sample size Non-visualized – overlapping clustering – predefined cluster numbers Non-overlapping clustering – non-visualized

Tourist market

Non-overlapping clustering – non-visualized

6

Cheung (2003)

SOM and Back propagation ANN k*-Means

7

Jang, Morrison, and O’Leary (2002) Hruschka and Natter (1999)

k-Means

Gaussian distribution dataset Japanese travel market

k-Means

Household cleaners

Non-overlapping clustering – visualized algorithm – no cluster number predefinition Predefined cluster numbers – non-overlapping clustering – weak visualization Predefined cluster numbers – non-overlapping clustering – weak visualization

8

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membership. The extended SOM network was used to group the nodes on the output map into a user specified number of clusters. Hung and Tsai (2008) used a hierarchical SOM segmentation model for the market of multimedia on demand. Table 2.1 reviews and compares different clustering methods used for market segmentation. 3. Self-organizing maps SOM algorithm was introduced for the first time by Kohonen (Kohonen, 1981) in 1981 and it was practically used in 1984 for voice recognition (Kohonen, Mäkisara, & Saramäki, 1984). Although SOM is frequently used in data mining (Vesanto, 1997), complex spaces display (Vesanto, 1999), clustering the spaces with high dimensions and particularly in image processing, process control, project management, financial analysis and industrial detections and medical diagnoses, comparatively it is less used in managerial and business administration related fields(Oja, Kaski, & Kohonen, 2002). An extensive list of engineering usages of SOM is presented by Kohonen, Oja, Simula, Visa, and Kangas (1996). The basis of SOM is to map spaces with high dimensions into two- or three-dimension space in a way that minimum information is lost and the hidden information in relations among the data can be discovered and showed. This method can show the correlation between data, information and their mutual effects on each other. The SOM network typically has two layers of nodes, the input layer and the map layer. Each map includes a set of neurons that are put together in a two-dimensional grid which is fully connected to the input layer. The lattice of grid is either hexagonal or rectangular. Fig. 3.1 presents topology of traditional self-organizing map with hexagonal lattice grid. Each neuron of the map layer is corresponding to an information vector with the dimension numbers equal to the dimension number of the information space under analysis. In other words, each neuron is the representative of one part of the information space. 3.1. SOM algorithm The SOM basic algorithm is based on a competitive unsupervised learning algorithm known as ‘‘winner takes all”. During the training process, input data are fed to the network through the nodes in the input layer. As the training process proceeds, the neu-

Fig. 3.2. Updating the best matching unit (BMU) and its neighbors towards the input sample marked with x. the black and gray circles correspond to situation before and after updating, respectively. The lines show neighborhood relations (Vesanto, 2000).

rons adjust their weights values according to the topological relations in the input data. The neuron with the minimum distance is the winner called best matching unit (BMU) and adjusts its weights to be closer to the value of the input pattern. Updating BMU and its neighbors is illustrated in Fig. 3.2. The network undergoes a self-organization process through a number of training cycles, starting with randomly chosen weights for the neurons in the map layer. During each training cycle, every input vector is considered in turn and the winner neuron is determined. The weight vectors of the BMU and the nodes in the neighborhood are updated using a weight adaptation function. SOM training algorithm includes the following steps (Kohonen, 2001): 1. Selecting map parameters such as the dimensions and the initialization weight vector corresponding to each neuron. 2. The network is fed with the data under analysis to find the best matching unit for each input data vector. Each record such as X includes quantitative values of n attributes that are shown as follows:

X ¼ ½X 1 ; X 2 ; . . . ; X n  2 Rn If weight vector of ith neuron is defined as bellow:

mi ¼ ½mi1 ; mi2 ; . . . ; min  2 Rn

Input Layer

ð3-1Þ

ð3-2Þ

Xj

Then, corresponding to each input record, the best matching unit or winner neuron is identified based on Eq. (3-3)

mij

c ¼ arg minfdðX; mi Þg i

ð3-3Þ

In which c indicates the winner neuron and d(X, mt) is the Euclidian distance between the record and the weight vector of ith neuron that is calculated by Eq. (3-4)

dðX; YÞ ¼ kX  Yk

ð3-4Þ

3. Updating the weight vector corresponding to each neuron using Eq. (3-5)

mi ðt þ 1Þ ¼ mi ðtÞ þ aðtÞhci ðtÞ½XðtÞ  mi ðtÞ

Map Layer Fig. 3.1. View of traditional SOM topology with hexagonal lattice grid.

ð3-5Þ

In which, 0 < a < 1 is the learning rate and hci(t) indicates the neighborhood rate of ith neuron with the cth neuron (winner neuron).

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The neighborhood rate of ith neuron with the winner neuron is obtained from Eq. (3-6) which is Gaussian function

hct ¼ e

krc ri k2 2r2 ðtÞ



ð3-6Þ

In Eq. (3-6), r is the controller of the function domain and is eventually decreased during the training process. Also ri and rc, as it is presented in Fig. 3.3, are respectively the positions of ith and cth neurons in SOM grid (Kohonen, 2001). Since the SOM training algorithm uses Euclidian distance to identify the BMU, the data of each dimension of the input vector should be normalized and standardized separately before feeding to the network. At the end of SOM training stage, a neuron map is obtained that is, in fact, a summary of the space under analysis of the network. 3.2. SOM visualization technique Visualization technique used in this research was firstly introduced by Ultsch and Siemon (1990). In this method relating to each attribute’s value in the weight vector, a RGB (Red–Green–Blue) vector and consequently a color is considered in a way that all values can be shown in a colored spectrum from dark blue (for lowest values) to dark red (for highest values). In this way, for each attribute, the color of each neuron is identified and the map related to that attribute is obtained. With the attribute’s maps, it is then possible to evaluate the mutual relation between them (correlation test). The same color of the corresponding parts of two maps indicates the correlation of the corresponding attributes in those maps. The intensity of color’s difference or similarity between maps can

1 2 3 Fig. 3.3. An example of topological neighborhood sets (at the radius 1, 2 and 3) (Kohonen, 2001).

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show the correlation rate between two variables in different parts of the space. More over, quantitative criteria can be calculated for that. It is possible that the intensity and even the kind of correlation between two attribute s in various parts of the space are different and affected by the values of other attributes that all can be truly shown using SOM (Kohonen, 2001). Fig. 3.4 indicates a sample of SOM usage in analyzing complex models and exhibiting the simultaneous effects of different variables on each other. As it is clear in this figure, the space under analysis has five dimensions. Comparing the maps with each other, the following results can be inferred: 1. Variables V2 and V5 and also V1 and V4 have inverse correlation in their entire change domain. (When V2 is red (takes high values), V5 is blue (takes low values) and vise versa.) Although the correlation rate of V2 and V5 is fixed almost in all parts of the space, the same is not applicable to variables V1 and V4. 2. Variables V3 and V5 are inversely correlated but the correlation rate in different parts of the space is diverse and less than the correlation rate of V2 and V5. 3. Variables V2 and V3 are directly correlated but their correlation rate is dependent to the values of other variables. 4. The correlation of variables V1 and V4 with V5 is completely non-linear and its rate in various parts of the space is different. The important issue is that maximum and minimum values of V5 are probably occurred in medium values of V1 and V4. A popular way to visualize clustering is to compute the distance between adjacent map units and present the result as a U-matrix1 (Iivarinen, Kohonen, Kangas, & Kaski, 1994; Ultsch & Siemon, 1990). Clustering matrix and corresponding to that, the clustering map or U-matrix are the major outputs of SOM. The elements of U-matrix show the distance of weight vector of adjacent neurons. If the attributes of two parts of the space under analysis are similar, then the distance between the weight vectors of the neurons related to them is not too much and in other words, both neurons are in the same cluster of the space under analysis. On the other hand, the more the algebraic distance between the neighbor neurons, the more different their corresponding spaces will be. Thus, they can be categorized into two separate clusters (Kohonen, 2001). Fig. 3.5 indicates a U-matrix with clusters and sub-clusters from a 62-dimension space. 4. Empirical study In this research ADSL market of a reputable Iranian telecommunication company was studied. Fanava Co. is a holding company which provides various telecommunication services, but ADSL services involve a major volume of its market. Fanava offers three different types of ADSL services with different specifications to cover the various needs of its customers. These services differ in sharing type of bandwidth, total data transmission limitation and billing method. Our methodology for conducting this research and the case study has been reviewed in the following section. 4.1. Research methodology The research has been done in three stages is presented in Fig. 4.1: Stage 1: Model development A. Defining the problem, specifying research goals, scope, and methodology. B. Providing a list of demographic, psychological, behavioral and

Fig. 3.4. SOM usage in simultaneous analysis of non-linear relations between variables.

1

Unified distance matrix.

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Fig. 3.5. U-matrix. Clusters are named by capital letters.

geographic variables used for segmentation in marketing literature and selecting the most effective variables in our empirical case market structure using Fuzzy Delphi method Stage 2: SOM network development A. Model deployment, network construction and configuration of parameters. B. Making the dataset using obtained data from Fanava customers by means of an electronic survey and customers’ data available at the database of Fanava sales system. C. Data preprocessing by standardizing and normalizing the dataset. D. Setting initial weights and training the network using dataset. E. Generating U-matrix map and each variable map. F. Distinguish and label segments and sub-segments. Stage 3: Segments analysis A. Selecting a sample customer set from each segment. B. Investigating and calculating the loyalty of each sample customer set to each service of Fanava. C. Analysis of segments behavior.

4.2. Model development To find critical variables for segmentation, Fuzzy Delphi method was employed to identify the most effective factors in our marketing context. The Delphi method originated in a series of studies that the RAND Corporation conducted in the 1950s (Okoli & Pawlowski, 2004). The objective was to develop a technique to obtain the most reliable consensus of a group of experts (Dalkey & Helmer, 1963). Although we could select the critical variables through the traditional statistical analysis methods, Delphi method was used as a stronger methodology for to achieve consensus of our market managers on final variables set. Okoli and Pawlowski (2004) compared and contrasted the strengths and weaknesses of a Delphi study versus the traditional survey approach as a research

strategy (Okoli & Pawlowski, 2004). Considering this comparison, we selected the Delphi method for the following reasons: Firstly our study is an investigation of factors that would affect customer behavior in our market. This complex issue requires knowledge of people who understand the different aspects of our market structure. Thus, a Delphi study answers the study questions more appropriately. Secondly because we have a limited number of market experts, our sample size for an acceptable statistical analysis is small therefore, in these conditions, Delphi technique will lead to more exact results and finally among other group decision analysis methods Delphi is desirable in that it does not require the experts to meet physically, it save time and cost required for collecting experts opinions. The traditional Delphi method has always suffered from low convergence expert opinions, high execution cost, and the possibility that opinion organizers may filter out particular expert opinions (Murry, Pipino, & Gigch, 1985) thus proposed the concept of integrating the traditional Delphi method and the fuzzy theory to improve the vagueness and ambiguity of Delphi method. Hsu and Yang (2000) applied triangular fuzzy number to encompass expert opinions and establish the Fuzzy Delphi method. The max and min values of expert opinions are taken as the two terminal points of triangular fuzzy numbers, and the geometric mean is taken as the membership degree of triangular fuzzy numbers to derive the statistically unbiased effect and avoid the impact of extreme values. This method may create a better effect of criteria selection. It features the advantage of simplicity, and all the expert opinions can be encompassed in one investigation (Okoli & Pawlowski, 2004). The Fuzzy Delphi questionnaire contained a list of 19 variables frequently mentioned in market segmentation literature which are divided into four categories labeled demographic, psychological, behavioral and geographic. The questionnaire was distributed to seven managers of the sales and marketing unit. The respondent were asked to indicate on a five-point Likert scale to what extend each variable influences customer purchasing behavior, according to their contact with ADSL customers. To make triangular fuzzy number for each variable steps were followed:

sAj ¼ ðLAj ; MAj ; U Aj Þ

ð4-1Þ

LAj ¼ minðX Aij Þ

ð4-2Þ

U Aj ¼ maxðX Aij Þ qffiffiffiffiffiffiffiffiffiffi n MAj ¼ Pni¼1 X Aij

ð4-3Þ ð4-4Þ

Eq. (4-1) represents triangular fuzzy number of jth variable. LAj is the minimum value of Aj that is evaluated by the experts. UAj is the maximum value of Aj that is evaluated by the experts. MAj is geometric mean of expert evaluation from Aj and XAij is the evaluation of ith expert from ith variable. Geometric mean of each triangular number corresponds to consensus of experts on the value of variable. If the geometric mean is more than a predefined threshold the variable will be selected. For the threshold value r, the 80/20 rule was adopted (Kuo & Chen, 2007) with r set as 4. This indicated that among the factors for selection, ‘‘20% of the factors account for an 80% degree of importance of all the factors’’. According to fuzzy numbers, six variables identified as the most effective factors on the consensus of market experts. Table 4.1 presents the most effective variables in Fanava market segmentation context. 4.3. SOM network development 4.3.1. Dataset For the dataset, 195 data items were collected through a survey sent to 647 private customers of Fanava via email. The question-

Model Development

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Problem Definition

Variables Selection

Survey

NetworkConstruction

Segments Analysis

SOM Network Development

Customer Data Training The Network

Data Preparation Customer Survey

Generating Self Organizing Maps

Segments Identification

Customer Data

Segments Sampling

Segments Behavior Analysis

Fig. 4.1. Research methodology steps.

naire was pre-tested by interviewing a few customers to avoid probable misunderstanding. The questionnaire includes three dimensions, i.e. demographics, psychographics and geographical. Demographic dimension collects the respondents’ personal information. Given psychographic variables, each component is represented by five-point Likert’s rating scales with values ranging from 1 (strongly disagree) to 5 (strongly agree). This dimension attempts to find out information on respondents’ characteristics, their lifestyles, buying incentives, etc. 4.3.2. Data preprocessing Scaling variables is of special importance in the SOM network development since the SOM algorithm uses Euclidean metric to measure distances between vectors. If one variable has values in the range of [0. . .1000] and another in the range of [0. . .1] the for-

Table 4.1 most effective variables in Fanava market segmentation context. Variable category

Variable

Rank

Geographical

Location

1

Demographic

Age Education Annual income

6 3 5

Behavioral

Purpose of service usage

2

Psychographic

Life style

3

mer will almost completely dominate the map organization because of its greater impact on the distances measured. To this end, we normalized dataset and scaled all variables linearly so that their variances were equal to one. 4.3.3. Network construction and training The training dataset consisted of 195 six-dimensional vectors each corresponding to one customer data. A 15  15 sized SOM was trained with this data. The networked was developed and trained in MATLAB 7.0 environment. Initial weights of network were randomly assigned. Sequential training algorithm and Gaussian neighborhood function were used for training process. Training was performed in two phases. In the first phase, relatively large initial learning rate (a0) and neighborhood radius (r0) were used. In the second phase both learning rate and neighborhood radius was small right from the beginning. These phases correspond to first tuning the SOM approximately to the same space as the input data and then fine tuning the map. 4.3.4. Visualization Fig. 4.2 presents Fanava ADSL market. The U-matrix shows segments of the market and each variable map illustrates the distribution of values of the corresponding variable. Variable maps reveal knowledge underlying in customers data, some rules can be inferred from the maps are:

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Fig. 4.2. Fanava ADSL market map.

 Income and education are positively correlated in the earlier mentioned market. In other words, wherever in the map there is high education (dark red area of education map) the income level is high as well (dark blue area of income map), and vice versa.  Most of our customers are highly educated (red and yellow area) generally with comparatively high annual income (dark blue and blue area). The customers set do not contain too old or too young people – normally they are in a specific range of age.  Highly educated customers (red area of education map) generally use high-speed internet for research purposes (red area of usage purpose) and educated ones (orange and light blue area) for business and entertainment. Low-educated customers’ purpose is mainly entertainment.  Those who have moderate income and are educated or low educated is use the Internet with the purpose of doing business.

Fig. 4.4. Labeled U-matrix according to loyalty rate of segments.

U-matrix represents segments of the market. Every cluster of the map corresponds to a segment of the market. Nine different segments can be identified roughly from the Fanava U-matrix. Fig. 4.3 shows identified clusters. Segments have been separated by black line. 4.4. Segments analysis

Fig. 4.3. Identified segments of market on SOM map.

Customers of each segment have particular characteristics. For example, the segment located on the top right corner of the U-matrix represents those customers who live in densely populated cities, are comparatively educated. In addition, their age average is not so high and they high annual income. This information can pro-

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vide us with a clear view of the market segments and their specifications for market managers who are generally interested in recognizing their target segment or segments. Target segment contains the most loyal and profitable customers. If a company selects the target segment correctly, it will earn the most profit from its target segment. To identify target segments of Fanava, behavioral aspect of loyalty was investigated, then according to loyalty rate of each segment customers, it was labeled in the map. Samples (15–20) were randomly selected from each cluster and their purchasing histories were retrieved from Fanava sales system based on their identification code. According to frequency of renewing purchase convention by customer, the rate of loyalty of each segment was estimated. Fig. 4.4 is labeled U-matrix according to loyalty rate of market segments. 5. Conclusion Market segmentation fulfills a crucial role in modern marketing paradigms, but not much has be done to make an effective instrument for visualizing market segments in a way which not only is easily understood by market managers but also show all the knowledge underlying the dataset. In this research, a visualization approach was proposed based on neural networks and SOM to market segmentation which has advantages over other clustering methods such as easiness to understand, ability to reveal mutual relationships among different customer’s characteristics variables, and its independence from predefining market segments number. In this paper, ADSL market of a reputable Iranian telecommunication company was studied. Critical variables of their market context was identified using a survey and the dataset for training the network was obtained from the survey questionnaire and customers’ data saved in the sales system database. After constructing and training the SOM network, U-matrix and variable maps were produced that revealed not only the market segments but also mutual relationships between all market variables. References Anable, J. (2005). ‘Complacent car addicts’ or ‘aspiring environmentalists’? Identifying travel behaviour segments using attitude theory. Transport Policy, 12, 65. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press. Bloom, J. Z. (2004). Tourist market segmentation with linear and nonlinear techniques. Tourism Management, 25, 723–733. Cadden, D. T. (1991). Neural networks and the mathematics of chaos – an investigation of these methodologies as accurate predictors of corporate bankruptcy. In First international conference on artificial intelligence applications on wall street (pp. 582–589). EEE Computer Society Press. Chaturvedi, A., Carroll, J. D., Green, P. E., & Rotondo, J. A. (1997). A feature-based approach to market segmentation via overlapping k-centroids clustering. Journal of Marketing Research, 34, 370–377. Cheung, Y. (2003). k*-means: A new generalized k-means clustering algorithm. Pattern Recognition Letters, 24, 2883–2893. Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467. Dillon, W. R., Kumar, A., & Borrero, M. S. (1993). Capturing individual differences in paired comparisons: An extended BTL model incorporating descriptor variables. Journal of Marketing Research, 30, 42–51. Gehrt, K. C., & Shim, S. (1998). A shopping orientation segmentation of French consumers: Implications for catalog marketing. Journal of Interactive Marketing, 12(4), 34–46. Hruschka, H., & Natter, M. (1999). Comparing performance of feed forward neural nets and k-means of cluster-based market segmentation. European Journal of Operational Research, 114, 346–353. Hsu, T. H., & Yang, T. H. (2000). Application of fuzzy analytic hierarchy process in the selection of advertising media. Journal of Management and Systems, 7, 19–39. Hung, C., & Tsai, C. (2008). Market segmentation based on hierarchical selforganizing map for markets of multimedia on demand. Expert Systems with Applications, 34, 780–787. Hwang, S., & Thill, J. C. (2007). Using fuzzy clustering methods for delineating urban housing submarkets. In Proceedings of the 15th international symposium on advances in geographic information systems.

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