Download A Study on the Marketing Segmentation Model Based on the

A Study on the Marketing Segmentation Model Based on the
Competitive Advantages of Enterprises
123
Dai Jun1 NiuFei2
School of information management of Wuhan University ,Hu Bei,Wuhan, P.R.China,430072
Abstract: Market segmentation is an important channel used by enterprises to know the market. The
traditional consumer-oriented strategy of market segmentation fails to meet the demands of market
competition. Segmentation of the market based on competitive advantages makes it possible for
enterprises to combine their respective advantages and consumer demands and thus make better use of
their advantages in the target market. This paper establishes a market segmentation model with the
neural network technique with reference to the traditional theories of market segmentation. The focus is
laid on the selection of segmentation variables, techniques of segmentation and the processing of
segmentation, which provides a new way for small and medium enterprises to analyze the market and
identify their way forward in the fierce competition.
Key words: market segmentation, competitive advantages, neural network
Introduction
Competition is the natural way of survival and development of enterprises in the environment of
the market economy. The key to the survival and development of any enterprise in the fierce competition
lies in its capability to provide products or services superior to those of other enterprises in the market. [1]
Thus the proper positioning of products and services is crucial for acquisition of competitive edge and
victory in the market of any enterprise. In light of modern marketing strategies, enterprises should first
study the demands of consumers from different fields by market segmentation and then determine their
respective target market through market analysis. The traditional market segmentation has two kinds:
consumer oriented and product oriented. The consumer oriented market segmentation follows the
standard of the general characteristics of consumers as a whole while the product oriented approach
divides consumers into categories according to the specific consumption situation of a product or brand,
based on different objectives of marketing, such as product positioning and pricing and advertisement
positioning. [2] Both involve in substance the disintegration of the market from the enterprise and
examination of the features of each in an isolated way and then the establishment the correspondence
between the two. It is evident that the identification of consumer demands with reference to the features
of the enterprise and the competitive edge in particular, together with market segmentation based on the
competitive edge of enterprises will make possible the effective combination of the competitive edge of
enterprises and the consumer markets. This will give play to the competitiveness of enterprises in the
acquired target market and lead to the full occupation of the target market with the competitive products
or services, which will satisfy consumer demands. Realization of this objective lies in the establishment
of a market segmentation model based on the competitive edge of enterprises. This paper will discuss
how to establish a market segmentation model based on an analysis of the traditional strategy of market
segmentation.
1. Characteristics and Defects of Traditional Market Segmentation Strategies
The notion of market segmentation was first put forward by the U.S marketing expert Wendell R.
Smith in the middle 1950s. Market segmentation is the division of a huge potential market into smaller
groups or parts, with similar features in respect of purchase or use of relevant products. [3] The traditional
market segmentation theory adopts the consumer-oriented marketing strategy, which serves to provide
detailed information of the market features, accurate understanding of consumer demands and even the
internal foundations of consumption behavior and consumption psychology to enterprises. It is thus an
important basis for the determination of target markets by enterprises. However, as indicated in the
One of the Key Projects of National Fund for Natural Sciences
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Introduction of this paper, it is necessary for enterprises to know the market as well as themselves
and advantages compared with competitors in particular and to give due consideration to both the
factors, in order to survive and develop in the vehement competition. As the famous master in marketing
Philip Kotler points out that “enterprises have to give priority to consumers as well as competitors and
seek a balance between consumer-orientation and competitor-orientation.” [4] However, as is known to
all, the traditional market segmentation strategy is characterized as follows:
Complete
consumer-orientation. The segmentation variables (inherent nature of consumers or of their behaviors)
as the bases for segmenting the market are independent from the enterprise, which produces the
segmented markets (sub markets) also independent of the enterprise.
Enterprises accept what the
market dictates passively. Although enterprises can choose their target markets freely, they have to
make this choice after the market has been divided. Enterprises have to choose a segmented market (a
sub market) passively. It can be seen that “the balance between consumer-orientation and
competitor-orientation” is lacking in the traditional market segmentation practice. It is thus impossible
to realize the effective matching of the competitive edge of enterprises and the consumer markets. Thus,
the defects of the traditional market segmentation practice are as follows:
It leads to unnecessary competition between enterprises and a waste of resources. In real life
each enterprise has its ideal market, with demands corresponding to the features of the enterprise.
Ideally competition between enterprises should be in the overlapping parts of their respective ideal
market. But as the segmented markets (sub markets) are independent of individual enterprises and are
thus not equal to the ideal market of an enterprise. An enterprise has to accept its segmented market and
competition is bound to arise between enterprises which have chosen the same segmented market (sub
market). It is obvious that this segmented market (sub market) is not the overlapping part of their
respective ideal market, which leads to unnecessary competition and thus a waste of resources.
The core competitiveness of enterprises is under restraint in the target market. As indicated
above, the process of segmenting the market is completely consumer centered and the segmented
markets (sub markets) are generally independent of individual enterprise. Failure to take into
consideration the advantages of enterprises is bound to restrain the function of the core competitiveness
in the target market of an enterprise. The direct consequence is that enterprises cannot occupy rapidly
the target market and under the restrain of the existing market player[5].
Market segmentation based on the competitive advantages of enterprises is the identification of
consumer demands and segmentation of the market with reference to the competitive advantages of the
enterprise. This will facilitate division of the market and choice of the target market by enterprises
according to their respective features. Thus the effective matching of the competitive advantages of
enterprises and the consumer market will be realized and a fundamental solution is found for the
problem discussed above.
①
②
①
②
2. The Basis for Building a Market Segmentation Model
Early research on the model of market segmentation is focused on the standards for differentiation
and pursues accurate descriptions of consumer demands and behavior modes through the rational
selection of segmentation variables. These studies have brought the system of segmentation variables to
unprecedented heights with over 100 variables invented by theoreticians. They belong to three
categories roughly: environment segmentation, psychology segmentation and behavior segmentation. As
consumers are of an organic unity, their consumption demands and behavior are the products of various
interacting factors. Thus current models of market segmentation adopt a combination of segmentation
variables such as the person-situation proposed by Peter R. Dickson, which is a combination of
environment segmentation and behavior segmentation.
Later, in consideration of practical application and guided by the instrumentalist approach, research
into market segmentation models shifts to the technique of segmentation and the selection of various
segmentation variables and seeks to tackle the issue of consumer segmentation through development of
statistical instruments. Clustering segmentation, adaptability segmentation and factor segmentation are
all the fruits of these researches. The prevailing model of lifestyle segmentation, though makes no
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contribution to the development of theoretical standards of segmentation, takes into consideration of
various information as to consumers in its statistical instruments: such as demographic geology,
individual characters, social strata, attitude and purchase behavior. Factor and clustering analysis is also
employed to get the segmented clustering based on the lifestyle of the consumer group as a whole or on
the behavior and psychology relating to consumption of a product. [2]
The basic issue (as well as the fundamental issue) of establishing a model of market segmentation
is what factors shall be used as segmentation variables and which segmenting techniques shall be used.
The market segmentation approach based on the competitive advantages of enterprises studies the
market in the view of competition and involves special requirements on the selection of segmentation
variables as well as segmenting techniques. This paper shall analyze the foundation for building the
market segmentation model based on the competitive advantages of enterprises in these two aspects.
2.1 Selection of Segmentation Variables
Market segmentation based on competitive advantages of enterprises is the division of market
according to enterprise features. Thus the choice of variables for segmentation has to take into account
the similarity and difference in consumer demands or behavior as well as whether these variables can
truly reflect the characteristics of an enterprise, especially the distinctions of an enterprise in respect of
competitive edge. The distinction means that the enterprise can provide efficient services to a specific
consumer group. Given the mature system of segmentation variables describing consumer demands and
behaviors, variables reflective of enterprise distinctions of competitiveness may be selected in terms of
environment, psychology and behavior. This is designed fundamentally to analyze the relevance
between enterprise competitiveness and these variables. The core competitiveness of enterprises is used
to describe the competitive advantages of enterprises and the index of a kind of core competitiveness
can be used to determine whether a specific enterprise is competitively advantages in this field. Consider
the customers of enterprises as an event, the distribution of customers on a specific variable as the state
and ranking it based on quantity. Then the Kendall rank correlation analysis is applicable. The Kendall
coefficient can be got according to the coefficient computation table. [10] Considering one variable only,
the lower the Kendall coefficient, the correlation of customer distribution feature of enterprises with
competitive advantages in one aspect is lower than enterprises without competitive advantages in the
same aspect. That is to say the variable can reflect the distinction of enterprises in respect of competitive
advantages.
Kendall Coefficient
τk : τk =
（1）
2M
N ( N − 1)
In the formula above, N is the value of the state index of the event and M is the correlation degree.
2.2 Selection of Segmenting Techniques
Techniques of market segmentation are ex ante (standards determined before the research begins)
or ex post (standards for segmentation and the number of clustering after segmentation are unknown
before the analysis). Corresponding to these two approaches are the classifying of statistics and
clustering of data mining. [6] The common assumptions of market segmentation in choosing these
techniques are[2]: 1. segmentation variables can directly express the distribution of consumer demands
and consumer behaviors, and the classes of variables correspond to the classes of consumer demands
and behaviors. 2. Consumers are the simple combination of factors at various levels (such as the
function need and price acceptance of a product, perception of advertisement and media habits) and
segmentation variables of different kinds are independent of each other. The foundations for market
segmentation based on the competitive advantages of enterprises cover not only characteristics of
consumer needs and behaviors but also features of enterprises. The chosen variables can reflect the
distinctions of enterprises in respect of competitive advantages. However, due to the indirect correlation
between the two, the classes after segmentation based on the variables do not correspond to the target
classes as between the variables and target classes there is a complex non-linear correlation. Use of the
artificial neural network (ANN) can produce knowledge necessary for segmenting the market based on
the competitive advantages of enterprises out of the given training samples and thus realize division of
the whole market. In addition, use of the ANN does not need the assumption that variables of different
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kinds are independent, which is more in conformity with reality.
ANN is a mature technique of data mining in wide use. It is a wide parallel network composed of
adaptive simple units (neurons) [7] . Each neuron processes the information acquired at the connection
weight to which it is connected. It then synthesizes (sums) the information, after processing by a
function, then transfers the results to other neurons through the connection weight. Learning and
relevance mapping of the network is realized according to the learning rules. There are diverse neural
network models. Here we focus on the BP neural network model adopted in this paper.The BP neural
network is composed of neurons connected together. A single neuron is shown in Figure 1[8]:
x1
w1 j
x 2 w2 j
yj
∑f
θj
xn wnj
Figure 1 Composition of Neurons
、
； y is the output； w is the value of the
corresponding connection weight；θ is the threshold； f is the initiating function (activation function)
Where: x1 x 2 …… x n is the input of neurons
j
ij
j
of the neuron.
y i = f ( ∑ x iy i − θ i )
The BP neural network is divided into the input layer, output layer and the middle layer (hidden
layer) [9]. Theoretically, there is no limit on the quantity of hidden layers. But usually there are one or
two hidden layers, structured in the way as shown in Figure 2:
Figure 2 Structure of Neural Network
where:
xi
is the input of the BP network;
yi
is the output of the BP network;
di
is the
expected input of the BP network; wijk is the value of connection weight of from the jth neuron in the
ith layer to the kth neuron in the (i+1)th layer oij is the output of the jth neuron in the ith layer of the
θ
；
neural network; ij is the threshold of the jth neuron in the ith layer of the BP network; netij is the
total output of the jth neuron in the ith layer of the BP network; Ni is the number of neuron nodes in
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the ith layer of the BP network;
η
is the learning rate.
Forward propagation of the BP network:
net ij
= f ( net ij )
： f ( net ) = 1 + exp[ −(1net
ij
Definition of errors:
ej = d j − yj
Objective function:
E
=
1
2
（ 2）
∑ o ( i −1) k ⋅w ( i −1) kj
k =1
o ij
Initiation Function
ni −1
=
∑ ( d j − yj )
ij
（3）
− θij )]
2
j
w ijk (t + 1) = w ijk (t ) + ∆w ijk
∆ w ijk = η ( d k − yk ) yk (1 − yk ) o ij
Weight Value Adjustment Formula:
（4）
（5）
The (i+1)th layer is output layer:
The (i+1)th layer is the hidden layer:
Ni + 2
∆w ijk = η o ( i + 1) k (1 − o ( i + 1) k ) ∑ (δ ( i + 1) hw ( i + 1) kh )oij
h =1
The (i+1)th layer is output layer:
δik = ( dk − yk ) yk (1 − yk )
Ni + 2
The (i+1)th layer is the hidden layer:
δ ik = o ( i + 1) k (1 − o ( i + 1) k ) ∑ (δ ( i + 1) hw ( i + 1) kh )
h =1
θik (t + 1) = θ ik (t ) + ∆θik
∆θik = η ( dk − yk ) yk (1 − yk )
Adjusting formula of the threshold weight:
The (i+1)th layer is output layer:
（ 6）
Ni+2
The (i+1)th layer is output layer:
∆θik = η o ( i + 1) k (1 − o ( i + 1) k ) ∑ (δ ( i + 1) hw ( i + 1) kh )
h =1
Terminating conditions in common use: the weight value approaches stability, namely the
adjustment of weight after input of each mode in the current learning is less than the standard value.
3. Model of Market Segmentation Based on the Competitive Advantages of
Enterprises
3.1 Establishment of a BP Neural Network
(1) Parameter Design of the BP Network
Selection of the input: the variables for segmentation will be used for input; number of nodes in the
input layer; the number of nodes in the input layer is the number of segmentation variables; and design
of output (output represents the functional objectives generally). Here we discuss the issue of
classification and the design of the output layer is actually the classification. Five classifications may be
determined according to the degree of segmentation required by the enterprise: complete support (1),
relatively good (0.75), good (0.5), relatively poor (0.25), and not in conformity (0). Output can be
designed also according to the conformity degree of each item of identified core competitiveness with
consumer needs.
Design of the Training Sample: the Sample has to be representative and consider the equilibrium
between classifications. The number of samples shall be 5 to 10 times that of the number of network
connection weights.
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Design of initial weight value: to make the net input near the zero value, the initial weight value shall be
as small as possible.
(2) Design of Structural Parameters of the BP Network
Design of the Hidden Layer: As the feed forward network of the in the odd hidden layer can map
all continuous functions, only one hidden layer is enough.
Design of Node Number of the Hidden Layer: The node number of the hidden layer is determined
（ ）：
on the empirical formula: m
m = nk n is the node number of input, k is the node number of
output.
(3) Improvement of the BP Network
As the standard BP algorithm has the defects of more iterating, slow rate of convergence, this paper
introduces the adaptive learning rate to improve it.
a. If the object function (E) of the network increases after the first batch of weight adjustment, this
adjustment is void, the object function is
7
η = b ⋅η 0 < b < 1
b. If the value of the object function (E) decreases, the adjustment is valid,
8
η =a⋅η 1 < a
（）
（）
3.2 Model of Market Segmentation
The market segmentation model includes five functional modules shown in Figure 3.
Segmentation
of market
Evaluation of the Analysis of Market
练样本
Segmentation
and
Training Sample
Data Mining by the BP
Network
Expert
Review
Investigation
and Analysis
of Consumer
Demands
Analysis
Identification
of
competitive
Advantages
and Selection
of Variables
for
Segmentation
Figure 3 The market segmentation model Based on Enterprise Competitive Advantages
The market segmentation model includes five functional modules :
(1) Identification of Competitive Advantages and Selection of Variables for Segmentation
The competitive advantages of enterprises can be described in terms of the core competitiveness of
enterprises, which is a group of the most central and crucial factors. Competitiveness is composed of
resources and capacities and identification of competitiveness should start with the resources and
capacities of enterprises to find the competitiveness of enterprises. Then efforts shall be made to
determine which competitiveness could constitute the core competitiveness. Specifically speaking, there
are the analyses of the current resources, geological advantages or disadvantages, integration operation
capacities, public relations capacities, services to customers and competitors of enterprises. Individual
examination of the composition of competitiveness after analysis shall be made to determine whether
one item is qualified as core competitiveness. In this way, the core competitiveness of enterprises is
identified. On this basis, the conclusion can be reached where the enterprise has competitive advantages.
Then the Kendall rank correlation analysis shall be applied to each segmentation variable. The Kendall
coefficient is got through formula 1 and those variables with low Kendall coefficient are selected.
(2) Investigation and Analysis of Consumer Demands
First, tentative investigations will be conducted with questionnaires designed based on the selected
segmentation variables. Analyze the results of questionnaire to review the rationality of the
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segmentation variables and questionnaires. Improve the questionnaires in response to the deficiencies
exposed in the first investigation and adjust the segmentation variables if necessary. Once it is decided
that the segmentation variables and questionnaires are reasonable, detailed plans for investigation have
to be compiled, which will be followed for real investigation to obtain market information. Admittedly
there are many ways to obtain market information and it is necessary to adopt various channels to get a
complete understanding of the market.
(3) Expert Analysis and Review
The performance of the network is closely related to the training sample. Thus the selection and
organization of the training sample are up to experts in relevant fields. First experts shall analyze and
classify consumer needs with regard to enterprise core competitiveness and in light of their knowledge
and experiences, then select the consumer demands distributed fairly between different categories and
representative as the training sample. Then experts will analyze the input data of the sample and give the
corresponding output value. The essence of this process is the selection of a small part which is of
utmost importance out of all market data and let experts divide the market in light of the enterprise
characteristics. This realizes the transformation from input data to output data, including the knowledge
necessary for segmenting the market based on enterprise competitive advantages, which can be of
service to the BP network learning.
(4) Data Mining by the BP Network
Preparation data is a precondition to data mining. Preparation of data is the definition, processing
and expression of data and making the data suited to a specific data mining method. It includes: data
cleaning and selection, data processing and transformation and management of data collection. Data
cleaning and selection is the assessment of each data, winkling out dirt data, filling the lost data and
correcting the wrong data. Data processing is the enhancement and processing of clean data thus
selected. Data transformation is the establishment of a mapping table for each text data to transform
them into digital data identifiable by the network. The initiation function of neurons determines that the
BP neural network accepts data value below (0.1) only and the data has to be reduced in proportion to
this zone. Data distributed roughly evenly in a range such as age and income as the segmentation
variables can be mapped directly to this zone. Data distributed unevenly such as the price sensitive
degree and education as the segmentation variable can be transformed by the piecewise linear function
or logarithmic equation and reduced in proportion to the zone. Discrete data such as the gender and
region as the segmentation variable can be expressed by being coded by 0 and 1 or by being evaluated in
the continuous zone. Management of data collection is the production of training samples and test
collection. As the output data of the sample has to be given by experts and organization of the training
sample has to be completed in the previous chain. A small part selected out of the training sample can be
used as the test collection.
Then make the BP network learn. This process includes forward propagation and back propagation.
The training sample gives an input module, the neurons process the input by formula 3 and propagate
forward by formula 2 until the output mode is produced. This is a process renewed at each layer and
called forward propagation. The value of object function is got by formula 4. Improve the learning rate
in light of the change of the value of the object function and transmit the erroneous signal from the
output layer to the input layer through the original channel while at the same modifying the connection
weight of each layer and adjusting the threshold by formula 6. Then repeat learning until the value of the
object function reaches stable and requirements. This process is called back propagation. By the training
mode group provided by the training sample, train the network repeatedly and repeat forward
propagation and back propagation until each training mode satisfies the requirements. After the BP
network get stable test the result of learning by the test collection.
The BP network after successful learning has mastered the essential knowledge for segmenting the
market based on enterprise competitive advantages and can be used for analyzing all the market
information. Make these data as the output mode and start forward propagation, the corresponding
output mode can be got, namely the conformity degree of enterprise competitive advantages with the
consumer demands. Thus the market is segmented.
(5) Evaluation of the Analysis of Market Segmentation
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Analysis of the segmented markets (sub markets) after data mining by the BP network is necessary.
One of its two purposes is to see whether the division of market in this way is reasonable and whether it
is possible to realize the expected results. If not, analysis is in need to find the reasons (mainly the
selection of segmentation variables and sample design) and modify the market segmentation model. The
other purpose is to understand the overall features of the segmented markets (sub markets) such as the
market volume and consumer distribution. Organization and selection of the training sample is the key
to the study of the market by this model as whether this model includes enough knowledge determines
the effect of market segmentation. Transformation of data is another issue which deserves special
attention. The structure of data shall not be change after transformation or the segmented markets (sub
markets) thus got will be too concentrated or discrete.
4 Summary and Expectations
This paper analyzes the characteristics and deficiencies of the traditional strategies of market
segmentation and put forward the view of segmenting the market based on the competitive advantages
of enterprises and discusses the model to realize this view. Segmenting the market based on the
competitive advantages of enterprises can help enterprises identify the market where they can give play
to their proper advantages, especially small and medium enterprises. As generally small and medium
sized enterprises are in a disadvantaged position in market competition, which does not imply that they
have no competitiveness. Rather, what they need is to find the markets where they can exert their
competitive advantages. Segmenting the market based on the competitive advantages of enterprises is
applicable also to analysis of competitors. Inverse analysis can get an understanding of the features and
advantages of competitor enterprises by their occupied markets. In addition, it can help enterprises to
fathom their direction of development when there is a change in market demands
The market segmentation model based on enterprise competitive advantages is grounded on the
data mining (by BP neural network) of ANN. Thus the effect of market segmentation is highly
dependent on the training sample, the selection of which is completed by experts. If automatic
processing of this process can be achieved while guaranteeing the quality, the adaptability of the whole
model will be enhanced and the time of operation will be reduced.
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：
，
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