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Expert Systems with Applications 38 (2011) 198–205
Contents lists available at ScienceDirect
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
i n f o
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: hanafizadeh@gmail.com (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
P. Hanafizadeh, M. Mirzazadeh / Expert Systems with Applications 38 (2011) 198–205
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’
199
(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
200
P. Hanafizadeh, M. Mirzazadeh / Expert Systems with Applications 38 (2011) 198–205
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).
P. Hanafizadeh, M. Mirzazadeh / Expert Systems with Applications 38 (2011) 198–205
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).
201
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
P. Hanafizadeh, M. Mirzazadeh / Expert Systems with Applications 38 (2011) 198–205
203
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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P. Hanafizadeh, M. Mirzazadeh / Expert Systems with Applications 38 (2011) 198–205
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-
P. Hanafizadeh, M. Mirzazadeh / Expert Systems with Applications 38 (2011) 198–205
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.
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