International Journal of Computer Applications (0975 – 8887)
Volume 27– No.11, August 2011

A Neural Network based Approach for Predicting
Customer Churn in Cellular Network Services
Anuj Sharma

Dr. Prabin Kumar Panigrahi

Information Systems Area
Indian Institute of Management,
Indore, India

Information Systems Area
Indian Institute of Management,
Indore, India

ABSTRACT
M arketing literature states that it is more costly to engage a new
customer than to retain an existing loyal customer. Churn
prediction models are developed by academics and practitioners
to effectively manage and control customer churn in order to
retain existing customers. As churn management is an important
activity for companies to retain loyal customers, the ability to
correctly predict customer churn is necessary. As the cellular
network services market becoming more competitive, customer
churn management has become a crucial task for mobile
communication operators. This paper proposes a neural network
(NN) based approach to predict customer churn in subscription
of cellular wireless services. The results of experiments indicate
that neural network based approach can predict customer churn
with accuracy more than 92%. Further, it was observed that
medium sized NNs perform best for the customer churn
prediction when different neural network’s topologies were
experimented.

General Terms
Prediction, Neural Networks, Churn M anagement.

Keywords
Neural Network, Churn prediction, Wireless Network Services.

1. INTRODUCTION
Enterprises in the competitive market mainly rely on the
incessant profits which come from existing loyal customers.
Therefore, customer relationship management (CRM ) always
concentrates on loyal customers that are the most fertile and
reliable source of data for managerial decision making. This data
reflects customers’ actual individual product or service
consuming behavior. This kind of behavioral data can be used to
evaluate customers’ potential life time value [1], to assess the
risk that they will stop paying their bills or will stop using any
products or services, and to anticipate their future needs [2].
Today, customer relationship management (CRM ) systems are
replacing traditional mass marketing strategies by selective or
personalized marketing practices [3]. These selective marketing
practices involve identifying a sub-set of existing customers that
are likely to stop using products or services of the company
(churn). As existing customer’s churning will likely to result in
the loss of businesses and thus decline in profit, churn prediction
has received increasing consideration in the consumer marketing
and management research literature over the past few years. In
addition, literature suggests that a small change in the retention
rate can result in significant impact on business [4].

In order to effectively control customer churn, it is import ant to
build a more effective and accurate customer churn prediction
model. Statistical and data mining techniques have been utilized
to construct the churn prediction models. The data mining
techniques can be used to discover interesting patterns or
relationships in the data, and predict or classify the behavior by
fitting a model based on available data. In other words, it is an
interdisciplinary area with a general objective of predicting
outcomes and employing sophisticated data processing
algorithms to discover mainly hidden patterns, associations,
anomalies, and/or structure from extensive data stored in data
warehouses or other information repositories [5].
There are several data mining techniques that are proposed to
predict potential customers that are most likely to churn. Among
the popular techniques to predict customer churn are: neural
networks, support vector machines and logistic regression
models [6]. Data mining research literature suggests that for
non-parametric datasets, machine learning techniques, such as
neural networks, often outperform traditional statistical and
structurally restrictive techniques such as linear and quadratic
discriminant analysis approaches [7].
As we consider from business intelligence perspective, churn
management processes under the customer relationship
management (CRM ) framework consists of two major analytical
modeling tasks. First task is predicting those customers who are
about to churn and second task is assessing the most effective
way that an service operator can react that includes providing
special promotion programs to customer or simply do nothing
for customer retention.
This research supports the former task, means it intends to
illustrate how to apply data mining techniques to assist telecom
churn management. This paper suggests artificial neural network
(ANN) technique to find a best model from stored customer data
to predict churn and to prevent the customer’s turnover. Like
this way, the cellular service operators can enhance the
competitive edge.
This paper proposes a neural network (NN) based approach to
predict customer churn in subscription of cellular wireless
services. The rest of the paper is organized as follows. Section 2
reviews the current literature, related to customer churn and
different data mining techniques used to predict churn in
different studies. Section 3 describes the current research
methodology, and Section 4 presents the experimental results on
real time dataset. Finally, conclusion is provided in Section 5.

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International Journal of Computer Applications (0975 – 8887)
Volume 27– No.11, August 2011

2. LITERATURE REVIEW
2.1 Custome r Churn Management
As the market becoming more competitive, many companies
have started realization of the importance of customer-oriented
business strategy for sustaining their competitive edge and
maintaining a stable profit level at top line and bottom line. That
is, companies mainly rely on the stable income which comes
from loyal customers. However, creating new customers and
retaining loyal customers is difficult and costly. As new
customer account is setup different expenses like credit
searching, advertising and promotional expenses are occurred.
These expenses are several times greater than cost of efforts that
might enable the firms to retain a customer [8]. So, it has
developed into an industry-wide belief that the best core
marketing strategy for the future is to retain existing customers
and avoid customer churn [9].
M arketing research literature has noted that ‘customer churn’ is
a term used in the cellular mobile telecom service industry to
indicate the customer movement from one provider to another,
and ‘churn management’ is a term that describes an operator’s
process to retain profitable customers [10]. Churn is also called
attrition and often used to indicate a customer leaving the
service of one company in favor of another company. Similarly,
the term churn management in the cellular network services
industry is used to describe the practices of securing the most
important customers for a company [11]. In essence, effective
customer management presumes an ability to forecast the
customer decision to shift from one service provider to another.
Customer management also presumes a good measurement of
customer profitability and different strategic and tactic retention
measures to overcome the customer’s movement.
In practice, a cellular operator can segment its customers by
their profitability and focus retention management only on those
profitable customer segments. The other way is, to score the
entire customer data base with respect to propensity to churn and
prioritize the retention effort based on profitability (life time
value of customer) and churn propensity [6]. However, the
telecom services industry in developing countries is yet to
standardize a set of customer profitability measurements (e.g.
current versus life-time value, business unit level versus
corporate level value, number of accounts versus number of
customers, loyalty versus profitability, etc.). This research
assumes non-significant correlation between profitability and
propensity to churn in order to simplify the modeling framework
and to focuses on churn prediction based on available data
mining techniques.
Burez and Van den Poel (2006) indicate that there are two types
of targeted approaches to managing customer churn: reactive
and proactive. When a company (cellular service provider)
adopts a reactive approach, it waits until customers ask the
company to cancel their service relationship. In this situation,
the company may have to offer the customer an incentive to
stay. On the other hand, when a company proceeds with a
proactive approach, it tries to identify customers who are likely
to churn to other service providers before they actually do so.
The company then provides special promotion programs or
incentives for these customers to avoid the customers from
churning. Targeted proactive promotion programs have potential
advantages of having lower incentive costs. However, these

strategies may be very wasteful if churn predictions are
inaccurate, because companies will be wasting money to
incentivize customers who will not churn. Therefore, an accurate
customer-churn prediction model is also critical for success of
customer incentive programs [3].

2.2 Related Work
Building an effective customer churn prediction model using
various techniques has become a decisive topic for business and
academics in recent years. In order to understand how different
studies have constructed their churn prediction models, this
paper reviews some of current studies as shown in Table 1.
Table 1. Related Literature about Customer Churn
Author
Coussement
and Poel,
2008 [12]

Data set
Subscriber database

Burez and
Poel, 2006
[3]
Hung et al.,
2006 [6]

Pay-TV company

Buckinx and
Poel, 2005)
[13]
Poel and
Larivie‘re,
2004 [4]
Kim and
Yoon, 2004
[9]
Chiang et
al., 2003
[14]
Wei and
Chiu, 2002
[15]

Retailing dataset

Wireless telecom.
company

Prediction method
Support vector
machines random
forests logistic
regression
Logistic regression
and M arkov chains
random forests
Classification
(decision tree,
neural network)
clustering (Kmeans)
Neural networks,
logistic regression

European financial
services company

Hazard model
survival analysis

Five mobile carriers
in Korea

Logistic regression

Network banking

Association rules

Taiwan wireless
telecommunications
company

Classification
(decision tree)

Coussement and Van den Poel (2008) applied support vector
machines (SVM ) in a newspaper subscription churn context in
order to construct a churn model. The customer churn prediction
performance of the support vector machine model is
benchmarked to logistic regression and random forecasts [12].
Burez and Van den Poel (2006) built a prediction model for
European pay-TV company by using M arkov chains and a
random forest model benchmarked to a basic logistic model [3].
Other studies (e.g. Kim & Yoon, 2004) only used a binomial
logistic regression to construct the prediction model [9].
According to research literature about customer churn, most of
the related work focuses on using only one data mining method
such as classification or clustering to mine the customer
retention data. Few studies (Hung et al., 2006) applied more
than one technology which was based on cluster analysis and
classification [6]. From the review of the literature we can
conclude that neural network can predict customer churn in
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International Journal of Computer Applications (0975 – 8887)
Volume 27– No.11, August 2011
different domains like Pay -TV [3], retail [13], banking [4] and
finance [14].
This paper proposes a neural network (NN) based approach to
predict customer churn in subscription of cellular wireless
services.

3. THE PROPOSED NEURAL NETWORK
(NN) BASED APPROACH
3.1 Artificial Neural Network
An ANN is a complex network that comprises a large set of
simple nodes known as neural cells. ANN was proposed based
on advanced biology research concerning human brain tissue
and neural system, and can be used to simulate neural activities
of information processing in the human brain [16]. ANN has the
topological structures of information processing nodes that
distribute information in parallel fashion. The mappings of
inputs and estimated output responses are obtained via
combinations of nonlinear transfer functions. We can make use
of self-adaptive information pattern recognition methodology to
analyze the training algorithms of the artificial neural networks
using past experience, neural cells, memory and association, to
process fuzzy, nonlinear, and noise-containing data without
developing any mathematical models. The various algorithms of
the neural networks training are Hebb, Delta, Kohonen, and BP
computation. The mostly used BP computation algorithm is the
error back propagation algorithm proposed by the PDP group of
Rumelhart in 1985 [17].
Y1

Output
Layer

Hidden
Layer

Transfer
function

PE

Weighted
Sum

PE

PE

X1

X4
X2

X3

Input
Layer

PE

PE

PE

PE

PE=Processing Element
Fig 1: Neural Network with One Hi dden Layer
Neural networks can be differentiated into single-layer
perception and multilayer perception (M LP) network. The
multilayer perception consists of multiple layers of simple, two
state, sigmoid transfer function having processing element or
neurons that interact by using weighted connections. In addition,
the neural network contains one or more several intermediary
hidden layers of neurons between the input and output layers.
Such intermediary layers are called hidden layers and nodes
embedded in these layers are called hidden nodes since these are
not taking inputs directly from outside. A typical feed-forward
multi- layer perceptron neural network consists of the input

layer, output layer, and hidden layer (topological structure is as
shown in Fig. 1). The n eural networks adopting the error back
propagation training algorithm are called BP networks, whose
learning process comprises both backward and forward
propagation. In forward processing, the sample signals will
gradually progress through each layer with the sigmoid function
The neural network cell i.e. neuron of each layer (input or
hidden) only affects the status of the next neural cell. If the
expected output signals cannot be obtained in the output layer,
the weight values of each layer of the neural cells must be
modified to keep the error minimum. Erroneous output signals
will be backward from the source. Finally, the signal error will
arrive in certain areas with repeated propagation thus the
weights of the different layer neurons are modified.
The network is set at n layers and yjn, such that yjn indicates the
output with n layers and j nodes. If yj0 is equal to xj, and j
indicates the inputs. Let Wijn be the connection weight between
yin-1 and yjn, then we get the threshold of θjn of n layers and j
nodes. The neural network learning algorithm includes the
following steps:
1. Initialize node connection weights with random values and set
other parameters.
2. Read in the input signal vector and the desired output. The
signals progress at the networks with the following formula:
(1)
It starts to calculate the j nodes of each layer with the output yjn
from the first layer through to the completion of calculation
processing. F(s) represents one of the sigmoid transfer functions.
3. Compute the actual output via the calculations, working
forward through the layers.
4. Compute the error. The error value of each node for the
output layer is obtained from the different values between the
real output and the required output (Djk ) and is given by:
(2)
The error value of each node for the previous (hidden) layers
depends on the backward error propagation of each layer (n = n,
n-1... 1) and is given by:
(3)
5. Change the node connection weights by working backward
from the output layer through the hidden layers which is done by
the following formula:
(4)
(5)
Where p indicates the iterative times (epoch) of the layers. The
constant η indicates the learning rate and α indicates the
momentum constant and their values can be from 0 to 1.

3.2 Dataset
This paper has used the churn data set from the UCI Repository
of M achine Learning Databases at the University of California,
Irvine [18]. The churn dataset deals with cellular service
provider’s customers and the data pertinent to the voice calls
they make. Customers have a choice of service providers, or
companies providing them with cellular network services. When
these customers change cellular service provider they are said to
churn which results in a loss of revenue for the previous cellular
service provider. The telecommunications company concerned
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International Journal of Computer Applications (0975 – 8887)
Volume 27– No.11, August 2011
here has used all its databases like billing database, customer
service database etc. and generated a list of pertinent records.
The neural network is implemented on Clementine data mining
software package from SPSS, Inc [19]. The data set contains 20
variables worth of information about 2,427 customers, along
with an indication of whether or not that customer churned (left
the company). The variables are as followsState: categorical variable, for the 50 states and the
district of Columbia
Account length: integer-valued variable for how long
account has been active
Area code: categorical variable
Phone number: essentially a surrogate key for
customer identification
International Plan: dichotomous categorical having
yes or no value
Voice M ail Plan: dichotomous categorical variable
having yes or no value
Number of voice mail messages: integer-valued
variable
Total day minutes: continuous variable for number of
minutes customer has used the service during the day
Total day calls: integer-valued variable
Total day charge: continuous variable based on
foregoing two variables
Total evening minutes: continuous variable for
minutes customer has used the service during the
evening
Total evening calls: integer-valued variable
Total evening charge: continuous variable based on
previous two variables
Total night minutes: continuous variable for storing
minutes the customer has used the service during the
night
Total night calls: integer-valued variable
Total night charge: continuous variable based on
foregoing two variables
Total international minutes: continuous variable for
minutes customer has used service to make
international calls
Total international calls: integer-valued variable

Total international charge: continuous variable based
on foregoing two variables
Number of calls to customer service: integer-valued
variable

3.3 Training and Testing of Neural Network
When using neural networks to perform predictive modeling, the
input layer contains all of the input fields or variables used to
predict the outcome variable. The output layer contains an
output field which is the target of the prediction. The input and
output fields can be numeric or symbolic. In Clementine,
symbolic fields are transformed into a numeric form (dummy or
binary set encoding) before processing by the network. The
hidden layer contains a number of neurons at which outputs
from the previous layer combine. A network can have any
number of hidden layers, although these are usually kept to a
minimum to simplify the predictive model. All neurons in one
layer of the network are connected to all neurons within the next
layer while the neural network is learning the relationships
between the data and results, it is said to be training. The Figure
2 provides details of implementation of neural network using
Clementine 12.0.
Clementine provides two different classes of supervised neural
networks, the M ulti-Layer Perceptron (M LP) and the Radial
Basis Function Network (RBFN). There are five different
algorithms available within the Neural Net node of Clementine
but this paper has used the widely applied Quick method. The
Quick method use a feed-forward back-propagation network
whose topology (number and configuration of nodes in the
hidden layer) is based on the number and types of the input and
output fields.
To prevent over-training problems that can occur within neural
networks, randomly selected proportion of the training data is
used to train the network. As the data pass repeatedly through
the network, it is possible for the network to learn patterns that
exist in the sample only and thus over-train. So the network may
become too specific to the training sample data and loose its
ability to generalize. The randomly selected proportion of the
training data is used to train the network and once this
proportion of data has made a complete pass through the
network, the rest is reserved as a test set to evaluate the
performance of the current neural network architecture.

Fig 2: The Implementation of Neural Network using Clementine 12.0
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International Journal of Computer Applications (0975 – 8887)
Volume 27– No.11, August 2011

4. RESULT AND DISCUSSION
The analysis section of the generated model displays
information about the neural network. Figure 3 depicts the final
model summary. The predicted accuracy for this neural network
is 92.35%, indicating the proportion of the test set correctly
predicted. The input layer is made up of one neuron per numeric
or flag type field. The input variable named state and phone
number were removed from the model since these variables
were used for identification only. This study has experimented
with multiple hidden layers in the neural network, containing
three to seven neurons but the best results were obtained having
one hidden layer with three neurons. The output layer contains
two neurons corresponding to the two values of the output field
(churn being true or false).

and where the differences are. Figure 5 represents matrix of
actual (rows) and predicted (columns) churn of the proposed
approach.
The model is predicting over 97% of the false churner means
loyal customers, correctly but only 66% of true churners. If we
wanted to correctly predict the loyal customers who will not
churn at the expense of the other types this would appear to be a
reasonable model. On the other hand, if we wanted to predict
those customers that were going to cause the company a loss by
churning, this model would only predict correctly in about two
third of the instances.
CustServ Calls
Intl Plan
Day Mins
Day Charge
Intl Charge
Intl Mins
Eve Charge
Eve Mins
VMail Message
Day Calls
VMail Plan
Night Charge
Night Mins
Night Calls
Intl Calls
Eve Calls
Account Length
Area Code

Importance 0

0.05

0.1

0.15

0.2

0.25

Fig 4: The Relative Importance of the Input Variables

Fig 3: The Generated Model
Figure 4 represents relative importance of the input variables for
doing sensitivity analysis of the generated model. The input
fields are listed in descending order of relative importance.
Importance values can range from 0.0 and 1.0, where 0.0
indicates unimportant and 1.0 indicates extremely important. In
practice this figure rarely goes above 0.35. Here we see that
Customer Service Calls is the most important field within this
current network, and then followed by International Plan, Day
M inutes and Day Charge. So we can conclude those customers
who are frequently calling to customer service numbers, having
international calling plan and spending much time in calling
others during day time, may become potential churners.
The generated Neural Net calculates two new fields, $N-Churn
and $NC- Churn, for every record in the input data base. The
first represents the predicted Churn (true or false) and the second
a confidence value for the prediction. The latter is only
appropriate for symbolic outputs and will be in the range of 0.0
to 1.0, with the more confident predictions having values closer
to 1.0. When predicting a symbolic field, it is valuable to
produce a data matrix of the predicted values ($N-Churn) and
the actual values (Churn) in order to study how they compare

Churn?

False

$N-Churn?
True

Count
Row %

0
0.000

1
100.000

False

Count
Row %

2038
97.605

50
2.395

True

Count
Row %

114
33.728

224
66.272

Fig 5: Matrix of Actual (Rows) and Predicted (Columns)
Churn

5. CONCLUSION AND FUTURE
RESEARCH
Churn prediction and management is crucial in liberalized
cellular mobile telecom markets in developing countries. In
order to be competitive in this market, cellular service providers
have to be able to predict possible churners and take proactive
actions to retain valuable loyal customers. Therefore, to build an
effective and accurate customer churn prediction model, has
become an important research problem for both academics and
practitioners in recent years. This paper suggests that data

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International Journal of Computer Applications (0975 – 8887)
Volume 27– No.11, August 2011
mining techniques can be a promising solution for the customer
churn management and we can establish an early -warning model
for this non-steady-state customer system. The final model
summary in this paper concludes that the model gives more than
92% overall accuracy for the prediction of the customer churn.
For future work, several issues can be considered. First, as the
data pre-processing stage in data mining is a very important step
for the final prediction model performance, the dimensionality
reduction or feature selection step can be involved in addition to
data reduction. Second, along with neural networks, other
popular prediction techniques can be applied in combination,
such as support vector machines, genetic algorithms, etc to
develop hybrid models. Finally, the current methodology of
churn prediction can be tested for other sectors like banking,
insurance or air line and comparisons can be done for prediction
accuracy.

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