SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 3(2); September (2022), pp. 85–91
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Customer Churn Prediction Model for Telecommunication
Industry
Teoh Jay Shen, Abdul Samad Bin Shibghatullah
Institute of Computer Science & Digital Innovation, UCSI University, Cheras, Kuala Lumpur, Malaysia

ARTICLE INFO
Article History
Received 23 November 2021
Accepted 28 October 2022
Keywords
Machine learning
Deep learning
Churn prediction
Telco industry

ABSTRACT
Customer churn is a constant issue that poses a serious threat and is one of the top issues fortelecom businesses. The businesses are working to develop and build a strategy to anticipate
customer attrition. This is why it is important to identify the sources of client churn. Churnprediction is the process of identifying which consumers are most likely to stop using a service
or to cancel their subscription. Because getting new customers frequently costs more than keeping
existing ones, it is an important prediction for many firms. The suggested models built in thiswork use both deep learning and machine learning algorithms. These models were developed andtested using the Python environment and a publicly available dataset from www.kaggle.com. Thisdataset, which was used in the construction of the models' training and testing phases, includes
7043 rows of customer data with 21 features. Four different machine learning and deep learningalgorithms were utilized by these models, including the Artificial Neural Network, SelfOrganizing Map, Decision Tree and a hybrid model with the combination of the Self-Organizing
Map and Artificial Neural network algorithms.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

Based on a research conducted by Abbas Keramati and
Seyed M.S. Ardabili [3], it stated that the annual churnrate within telecommunication can be range from 20% to
40%, whereas the cost needed to obtain a single newcustomer is 5 to 10 times higher than maintaining an
existing customer [3]. Therefore, to develop a churnprediction model with high accuracy is very important fora Telco service provider to be successful or bottom-line
survival of a Telco service provider in this intensely
saturated marketplace. On those grounds, the Telco
market is ever changing and getting more and morecompetitive [4]. In fact, customer churn is not only the
essential concern on the marketing side, but also one of
the fundamental dimensions of CRM which is also
known as customer relationship management [5]. Churnprediction model is the model that utilized data mining
techniques to transform an enormous amount of data into

1. Introduction
In Telco industries or other business-related field,
customer churn is defined as the action of existingcustomers terminating their subscription of service withthe company due to several reasons such as
dissatisfaction of the service provided/better priceoffered by competitors for the same services [1].
Customers churn prediction and management is
specifically intense in the mobile telecommunicationindustry as the market is increasingly saturated by thecustomers who change their subscription from one
network provider to another in a very frequent manner.
Furthermore, one of the main reason that customers
churn are recognized as the biggest problem in any
industry is due to the cost of obtaining new customers iswhole lot more than keeping the existing customers [2].

Corresponding authors’ E-mail: 1001539989@ucsiuniversity.edu.my, abdulsamad@ucsiuniversity.edu.my85

a meaningful insight and present it in a way that normal
people could also understand.

algorithms to build a churn model. The aims andpurposes of the research of such studies differ. Some of
the aims are to simply evaluate if machine learning is anapplicable approach for customer churn. In others, theaims may be to evaluate and select the best algorithms forprediction.

Machine Learning carries a huge potential in the field of
data mining and data analytics [6]. Machine Learning canbe defined as the ability of a computer to study anddiscover a set of rules from the given input data or overallbuild a model that can be used to find correlations
between the variables or a structured data set to makepredictions [7]. Numerous of machine learning predictivemodelling algorithms have been proposed and applied inbuilding churn prediction models, these machine learningalgorithms can be ranged from simple linear regression
to more complex hybrid methodologies. Thesealgorithms are able to work efficiently in predicting thecustomers who are most likely to churn. At the past few
years, besides machine learning, deep learning has also
become one of the most popular trends thanks to its
potential in processing big data [8]. Deep Learning, also
known as deep structured learning is basically a divisionof Machine learning that relying on algorithms that trying
to model high degree abstractions on data [9]. Deep
Learning Algorithms (DLAs) establish a multi layered
hierarchical structure of learning and indicating data.
DLAs are useful in dealing with a massive amount of
unsupervised data as it study and discover thecorrelations of data in a greedy layer structured method[9]. Thus, this project is going to develop two deeplearning churn prediction models, one hybrid churnprediction model (SOM+ANN) and one machinelearning churn prediction model using artificial neuralnetwork (ANN), self-organizing map (SOM), anddecision tree (DT) algorithms. Lastly, CRSIP-DM
methodology was applied for the development of thisresearch.

A comparative study for different type of machinelearning algorithms for predicting churn customer for
prepaid services was done by Brandusoiu and Todereanin the year 2016 [10]. The machine learning algorithms
used in this study includes the Neural Network, Support
Vector Machine and Bayes Network. The dataset used inthis study consist of 3333 rows of customer call details
with 21 independent variables and 1 dependent variablewhich is the churn status with the values of only YES or
NO. Some features also captured the data of incoming
and outgoing calls and messages for each customer. In
order to reduce the data dimension, the principal
component analysis algorithms were used by the author.
AUC which is known as the area under the curve is thetechnique used by the author to evaluate the performanceof the ML algorithms. The AUC values were reported
99.10%, 99.70% and 99.55% respectively for Bayes
Network, support vector machine and neural network[10]. A similar study was made by Yue He, Zhenglin He
and Dan Zhang in 2009 [11], where they designed a newmodel for churn prediction on the basis of the Neural
Network algorithm. This model was proposed with theintention to solve the customer churn issues in a big
Chinese Telco company which consists 5 million ofcustomers. The results of the model accuracy rate
reached 91.1%, which is very a stunning result [11].
In the year 2015, 9 researchers from China conducted
research of the customer churn problem in the big data
platform [12].The goal of this study was to demonstratethat big data can significantly improve the performanceof predicting customer churn based on the 3Vs (variety,volume, velocity) of the data. Interpreting the data at
China’s largest Telco Company, a big data platform isneeded to engineer the process and solve the problems.
AUC (area under the curve) was once again used tomeasure the model performance and Random Forest
Algorithm was applied to build the model in this study[12]. Other than that, at the same year 2015, a group of

2. Previous Work
Customer churn is one of the significant matters in the
Telco industry nowadays. Numerous methods were
implemented to predict customer churn in Telcocompanies. Majority of these methods employedmachine learning, deep learning and data mining
techniques. Most of the previous work done byresearchers focused on implementing only one techniqueof data mining to retrieve knowledge, where others
focused on conducting studies between several ML/DL
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researchers from the United States with Maryam M
Najafabadi [13] as the leader demonstrated how to use
deep learning to interpret big data, extracting anddiscovering complex correlation hidden under a large
dataset. Moreover, they classified and list out certain
types of problems that can be solve by applying deeplearning. These problems are problems related to
prediction and classification, sematic indexing,
extracting information from raw dataset and data tagging.
They discussed several components of deep learning toaddress certain issues in big data analytics [13].

Modeling, Evaluation and Deployment. In general, thesephases are implemented subsequently. However in thismain stream, various iterative processes can be seen due
to the fact that every outcome of each phase impacts thenext methodology phase. In other words, after the data
understanding phase is completed, data analyst usuallyneeds to go back to the business understanding phase torefine or reconsider the aims and objectives for the
project. In the same manner, after the modelling part,mostly there is always a need for the data analyst to
conduct a new data pre-processing process in order toenhance the model’s performance. Moreover, the resultsof the evaluation phase could also potentially leads to a
new start of the CRISP-DM process in the case that themodels do not satisfy the aims and objectives definedpreviously in the research.

Furthermore, Niall McLaughlin and his team used theconcept of a CNN, which is also known as convolutional
neural network to develop an android malicious software
detection system in the year 2017. It was a creative andmodern application of deep learning in the domain of
malware analysis. This application was proficient inlearning to perform malware detection and feature
extraction at the same time. Lastly the team came to aconclusion that showed the proposed model is more
efficient than an n-gram based malicious software
detection system. The last observation of study related todeep learning application in predicting churn was done
by Federico Castanedo and his team in 2016. They
introduced deep neural network as a perfect tool forcustomer churn prediction as it constructs numerous
hidden layer and it disseminates the weights of eachneurons from one layer to the next layer. Deep Learning
supports automated process of extracting features withthe maximum information (impact), therefore there wasa remarkable melioration in the performance of the modelin accuracy wise. Billions of call details from a Telco
business enterprise were used as the data input to train
the predictive model for customer churn prediction.
77.9% of AUC (are under the curve) values wereachieved on the validation dataset. Moreover, theyextended the idea into the field of fraud detection invarious industries such as the banking industry, insurance
industry and Telco industry.

Fig.1: Phases and tasks of the CRISP-DM process
Fig. 1 shows the complete cycle of the CRISP-DM
methodology and the phases within the cycle. The details
are explained in the following section:
1. Business Understanding: This is the initial phase of
CRISP-DM methodology. Three statements areproduced in this phase which the statements include thestatement of business objective, statement of data mining
objective and statement of success criteria. This phaseemphasizes on understanding the objectives andrequirements of the project from a business point of view,
then transforming this information into a problem
statement of the project and the draft of strategy toaccomplish the objectives.
2. Data Understanding: The data understanding phasebegin with the initial data collection and then follows by

3. Methodology and Algorithm
For this research, CRISP-DM Methodology is use tomodel the customer churn prediction in Telco industry.
CRISP-DM contains of the following phases: Business
Understanding, Data Understanding, Data Preparation,87the data reading process with the purpose of getting
familiarize with the data, identify the data quality, todetermine the initial insights to the data and to discover
relevance subset to create the hypothesis for hidden
knowledge.
3. Data Preparation: This phase includes all the actions todevelop the final dataset on the basis of the raw data. The
tasks in data preparation phase are commonly been
executed several times and does not follow any
prescribed order. The tasks include row and feature
selection, data filtering, data cleaning and transforming
the dataset into the appropriate format for the modelling
tools.
4. Modeling: The application of the algorithms and codedeveloping parts are constructed under this phase. Model
optimizing process is conducted in order to get the best
performance form the model. Usually for one data
mining problem, there are multiple algorithms andtechniques available to be apply and solve the problems.
However, some algorithms require specific requirementsfrom the data. Thus, moving back to the data preparation
part is often required.
5. Evaluation: In this phase, a thorough evaluation on themodel and the steps of execution to construct the model
is conducted in order to make sure that the businessobjectives are achieved by the model. The main activityof this phase is to assess and identify whether is there any
critical business problems that has not been recognizedadequately. In the end of this phase, a conclusion on theusage of the model results should be made.
6. Deployment. The deployment phase remains as the lastphase in the CRISP-DM process. Where in this phase thedata mining results are utilized as the business rule andthe findings and knowledge obtained from the modelneeds to be visualized and presented in a way thatbusiness people can understand it.

fashion. Multiple nonlinear transfer functions are
combined to maps the inputs and measured output
responses [15].
Self-Organizing Map (SOM) is classified as amethodology under unsupervised learning, it distribute agroup of patterns into clusters or segments. Cluster
analysis can be defined as the process of arranging agroup of data object into different cluster. Above all, nopredefined clusters are allocated. In the year 1987, Teuvo
Kalevi Kohonen introduced and illustrated a brand new
structure of a neural network architecture named the SelfOrganizing Map (SOM). SOM turned out to become
incredibly useful in the case that the input dataset arecomplicated and high dimensions. SOM is applied to
study the correlations in a dataset and distribute the data
into different cluster based on the similarity of patternsof the data where the modellers are not able to forecast
the class of the classification[16]. SOM is classified as amethodology under unsupervised learning [17], it
distribute Decision trees algorithm possess the basis of agraph structure, every decision could potentiallygenerates a new node and eventually developed into atree-like graph [18].
As a rule, a hybrid model is a combination of two or moremachine learning or deep learning algorithms. As an
example, the clustering (SOM) and classification (ANN)techniques can be combined in sequence. In other words,
clustering techniques are able to be applied as a preprocessing phase to determine different pattern ofclusters for later supervised learning [19] [20]. Therefore,
the result of the clustering process can either be used todetermining the main clusters of a set of data given or beused as a pre-classification of unnamed features [21] [22].
Thus, the result of the clustering process can be included
as a feature into the training set to train a prediction
model. After the completion of the hybrid model, it has
the capability to group or forecast new instances. In otherterms, the first stage of the hybrid model is to detect theoutlier of the dataset and the second stage of the hybrid
model is to make prediction. Since the customer churnprediction is a supervised classification process, thus the
hybrid models that are chosen in this paper are the cluster(SOM) to classifier (ANN) model [23] [24].

Artificial Neural Network (ANN) is part of the model ofthis research. ANN was invented on the basis of a
sophisticated biology research regarding neural systemand human brain tissue. ANN is applied to stimulate theneural operations of knowledge processing in the humanbrain [14]. In ANN, the neurons which can also be called
as the information processing nodes are formed in a
topological structures. Therefore, the neurons
disseminate their data and information in a parallel883.1. Data

4.

Only one dataset was used for this research. In order toobtain a good dataset, an online research are done by the
researcher in order to find a free open source dataset that
fulfilled the requirements of this research, finally adataset that fulfils the requirements was found on
www.kaggle.com. A total of 7043 rows of customer
details with 21 features were provided in the dataset. The
variables of the dataset used were stimulated from a
Telco company from USA. Variables of the dataset are
categorized into two types. Nominal types define a set ofvalues which represents a certain meaning, whilequantitative types refer to values that can be calculated
and ordered, such as integer and float values. Variable“Churn” is the dependant variable, meaning it is thevariable that the model is trying to predict (see Table 1,
row 21).
Table 1: Description of the Data

The method of evaluation that will be used to evaluate
the algorithm’s performance in this research is thecalculation of the F-measure value. The accuracy,
precision, recall and the F-measures value for all of thefour proposed models are calculated accurately andpopulated in Table 2 with its corresponding algorithm.

Results and Evaluation

Table 2 Models performance comparison table.
Algorithm
Artificial Neural
Network (ANN)
Self-Organizing
Map (SOM)
Decision Trees(DT)
Hybrid Model(SOM + ANN)

Accuracy
78.9
0
51.8
9
73.4
1
79.5
3

Precision
84.03

Recall
88.24

F-measure
86.08

69.35

70.67

70.00

85.59

80.73

83.08

83.90

89.40

86.56

In Table 2 the performance of each model can beobserved clearly. Based on the F-measure score, thehybrid model possesses an 86.56% of F-measure score
which is also the highest among the proposed models,
and then followed by the Artificial Neural Networkmodel which holds almost the same F-measure score(86.08%) with the hybrid model. In addition, theDecision Tree model holds a 83.08% of F-measure score
and the Self-Organizing Map model possesses the lowest
F-measure score (70.00%) among the proposed models.
As the Self-Organizing Map model applied theunsupervised learning method, thus it is reasonable for
the SOM model to have a comparatively weakerperformance compared other models that applied
supervised learning method in this case. Lastly, by
evaluating the results of the models we noticed that theoverall performance of the model increased after
implementing the Hybrid model approach.
5. Conclusion and Future WorkCRISP-DM methodology was implemented for
conducting a prediction of churn customer in the Telco
industry. The aim of this research is to compare thealgorithms and to determine which is the most suitable
and accurate to predict the results of the given problem.
A uniquely modified dataset was analyzed with different
machine learning and deep learning models and its result
89were compared with an evaluation method. The
algorithms used were the Artificial Neural Network(ANN), Self-Organizing Map, Decision Tree (DT) and a
hybrid algorithm which is the combination of SOM andANN algorithm. After studying and comparing theresults of each algorithms, the hybrid model with two
deep learning algorithms combined together areconcluded as the model that achieved the best outcomes
for this research.

There are some constraints in this research that needs tobe recognized and improvement needs to be done against
them. Firstly, because of the privacy issues of the Telcocompanies, real time dataset are not allowed to be usedin this research. This research focused on study andcompares the performance of different models, thus the
future work will focus on the feature selection and theresearch of the customer needs by applying different data
mining techniques. Moreover, future research can be
improved by implement others machine learning anddeep learning methods such as: K-Nearest Neighbours,
Logistic regression, Learning Vector Quantization,
Support Vector Machines and Random Forest. They canbe used to have a better understanding of each machinelearning algorithm results provides a statistical results toaids the algorithm selection process.

Moreover, the likelihood of successfully predicting thecustomer churn with Machine Learning and Deep
Learning depends on the correct choice of data andalgorithm. In order to achieve the best outcomes,
choosing a suitable machine learning or deep learningalgorithm for the problem or topic is crucial. However,by having the algorithm alone is unable to provide thebest prediction results. Therefore, having the rightvariables and types of dataset is also an important factor
in getting the best prediction results. Also, by looking at
the scores of the feature importance is able to determine
how the changes of each independent variable impact onanother dependent variable. In the optimization processof the dataset, only highly statistically significancevariables/predictors will be selected and grouped in an
optimal dataset for independent variable. In fact, theoptimal dataset does help the models to come up with
better predictions. In addition, based on our literature
review, implementing machine learning and deeplearning model to predict customer churn is new andprevailing in the Global Telco industry. Featureengineering and data gathering process for predicting
churn in the Telco industry in one of the popular andimportant elements of the current research Thus, it isexpected that with an in-depth understanding ofcharacteristic and behaviour of the churn customers,
Telco companies can develop new strategies to address
churn. These strategies need to be applied on the targetedcustomers who shared the similar behaviours with thechurn customers group. Offering a new plan, provides
better quality of service, determine the needs fromdifferent segments of customer, and provides tailoredoffers for different customer groups can be all includedin the strategies.

Acknowledgements
The authors wish to thank ICSDI, and CERVIE UCSI
University for supporting this project.
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Authors Introduction
Mr Teoh Jay ShenHe received his Diploma ofInformation Technology from UCSI
University in 2018 and continue his
Bachelor of Science in Computing at
the same university and completed in2020. His research interest are inmachine learning and artificial
intelligent.
Dr. Abdul Samad Bin Shibghatullah
He received the bachelor accounting
degree from Universiti Kebangsaan
Malaysia, Bangi, Malaysia in 1999,
the M.Sc. degree in computer sciencefrom the Universiti Teknologi
Malaysia, Skudai, Malaysia in 2002,
and the Ph.D. degree in computerscience from the Brunel University of Uxbridge, United
Kingdom. He is currently Associate Professor at Instituteof Computer Science & Digital Innovation, UCSI
University, Kuala Lumpur, Malaysia. His current researchinterests include optimization, modelling and scheduling.

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