[advances in intelligent systems and computing] proceedings of seventh international conference on...

J. C. Bansal et al. (eds.), Proceedings of Seventh International Conference on Bio-InspiredComputing: Theories and Applications (BIC-TA 2012), Advances in Intelligent Systemsand Computing 201, DOI: 10.1007/978-81-322-1038-2_16, � Springer India 2013

A Computational Intelligence based Approach to

Telecom Customer Classification for Value Added

Services

Abhay Bhadani1, Ravi Shankar2, and D. Vijay Rao3

1Bharti School of Telecommunication Technology andManagement, Indian Institute of Technology Delhi,

abhay [email protected],http://web.iitd.ac.in/∼bsz098503/abhay.html

2Department of Management Studies, Indian Institute ofTechnology Delhi, [email protected],

http://web.iitd.ac.in/∼ravi13Institute for Systems Studies and Analyses, Defence Research and

Development Organization (DRDO), Metcalfe House, Delhi,[email protected]

Abstract

Customer classification is an imperative task for any organizationcatering to different market segments. In telecom industry it becomeseven more important to identify which value added services(VAS) wouldbe successful with a given customer segment. VAS provide a flexiblerevenue model that can be customized to different customer segmentsbased on several attributes such as usage and preferences. Selecting andcustomizing VAS provides a wide canvas to the operators for maximiz-ing their returns on the customer portfolio. Computational intelligencetechniques such as Artificial Neural Network (ANN) and Support VectorMachine (SVM) have been successfully used for data mining and machinelearning. These techniques provide a mathematical framework for identi-fying customers profiles and patterns in large datasets that representingthe customers’ data and their preferences. In this paper, we propose amethodology using SVM and ANN techniques to classify telecom cus-tomer data and identify the VAS best suited for the customer segment.We test our results with the SVM yielding high prediction accuracy forthe unknown public test data with Radial Basis Function(RBF) Kernelusing grid search technique.

Keywords: Customer data classification, Support Vector Machine,Artificial Neural Network, Value Added Services

181

1 Introduction

With a fierce competition being faced by the telecom operators, they are leftwith little scope for making profit with simple basic services like Voice or ShortMessage Service(SMS) and people registering for Do Not Disturb kind of ser-vices. VAS provides a flexible revenue model that can be customized for differentcustomer classes based on their usage, preferences and likings. Telecommuni-cation companies generate a tremendous amount of data continously. Theseinclude details related to call data, which describes the calls that traverse thenetworks, network data, describing the state of the hardware and software com-ponents used in the network, and static data like customer data, describingthe telecommunication customers, demographic information, describing the per-sonal details of the customer including name, location, birth date. Computa-tional Intelligence based Machine learning algorithms can classify large datasetsand find interesting patterns. Several techniques have been proposed in litera-ture for the classification of large data sets such as decision trees, ANN, SVM,Linear Discriminant Analysis, Linear Regression, Polynomial Regression, etc.In this paper, we use ANN and SVM techniques for classifying the telecom cus-tomer data. A brief outline of these techniques has been discussed in section 2,3.

SVM works on the principle of constructing a hyperplane1 as the decisionsurface such that the separation margin between positive and negative examplesis maximized, see Figure 1. ANN is a technique which tries to mimic the func-tioning of brain, See Figure 2. It was originally inspired from the functioningof human brains ability to take decisions once it learns which is retained in thememory and unknowingly helps in taking decision. Both these techniques canbe used for classification and prediction to determine business intelligence inlarge sets of telecom customer’s data. Customers use mobile phones for com-munication, entertainment and m-commerce applications. It is thus necessaryfor telecom companies to identify the specific requirement and preferences ofcustomers and thus customize their VAS with a goal of maximizing the utilityto the customers and revenue to the operators.

In this paper, we propose a methodology using SVM and ANN techniquesto classify telecom customer data and identify the VAS Plan best suited for thegiven customer segment. From these classified customer segments we derive theclassification rules where attributes are considered to be fuzzy.

2 Background and Literature Review

Decision making becomes a complex activity especially with large datasets be-ing generated continuously as is the case of telecommunication industry (Weiss,2005). Customer classification is a powerful tool being used in decision mak-ing and developing appropriate strategies. Broadly, classification techniques

1A hyperplane is a geometric generalization of the plane into a different number of dimen-sions.

182 A. Bhadani et al.

Figure 1: Support Vector Machine Model

Figure 2: Artificial Neural Network Model

can be treated as parametric and non-parametric functions. There are severaltechniques available in the literature for regression and classification. Linearprobability models, Generalized Linear models, Logistic Regression, Discrim-inant Analysis, Neural Networks, Genetic Algorithms, K-Nearest Neighbors,Decision Trees, Neural Networks, Perceptrons(single layer and multi-layered),Naive Bayes classifiers, Bayesian Networks, Instance based learning, SupportVector Machine (Large margin classifier) are some fo the popular techniques ofcomputational intelligence used for making decisons with large input datasets.

Modeling complex, N-dimensional problems precisely is a challenging task.Highly predictive models continue to play an increasingly important role in21st century marketing applications, particularly in areas such as automatedmodeling, mass-produced models, intelligent software agents, and data mining(Cui & Curry, 2005). Predictive accuracy is considered to be the standard wayfor measuring the model quality (Politz & Deming, 1953). The SVM avoidsover reliance on particular structural assumptions and implicitly automates themodel identification process. By doing so it enters the parameter estimationphase with a family of structural possibilities. Using kernel transformationscleverly, the SVM solves a nonlinear problem with a linear model (Cui & Curry,

A Computational Intelligence 183

2005).ANN and SVM have been applied in several areas and a wide variety of

applications have been developed. Several authors (Yeh, Chi, & Hsu, 2010; Lee,2007; Lovell & Walder, 2006; Wu, Huang, & Meng, 2008; J.-J. Huang, Tzeng,& Ong, 2007; Guo-en & Wei-dong, 2008) have tried to use SVM for buildingmodels for regression as well as classification purpose to name a few. Cross sell-ing from mobile phones has been studied to some extent by (Ahn et al., 2010;Ahn, Ahn, Oh, & Kim, 2011), where they use decision tree, ANN and LogisticRegression based models for telecom customer classification. Consumer be-haviour based on consumer/user lifestyles, use-motivations and product/serviceattributes using cluster and factor analysis for developing their strategic planshave also been studied recently (Mazzoni, Castaldi, & Addeo, 2007). Reuverand Haaker propose a viable business model based on context aware mobileservices from practitioners and experts in this field (Reuver & Haaker, 2009)and Constantiou proposes a theoretical framework for adoption of Mobile TV(Constantiou, 2009). A service oriented approach towards intelligence in tele-com industry based on questionnaires has been recently proposed in (Ishaya &Folarin, 2012). A score based model was proposed by (Ahn, Ahn, Byun, & Oh,2011) to study the likings and adoption of VAS by telecom user. Several authors(Sharma & Panigrahi, 2011; B. Huang, Kechadi, & Buckley, 2012; Pendharkar,2009; Guo-en & Wei-dong, 2008) have tried to model telecom customer churnbased on ANN and SVM techniques. However, all these studies lacked mathe-matical approach to predict the likings and success factors to generate businessintelligence to understand telecom customers and their preferences.

3 Computational Intelligence Techniques for clas-sification

Several techniques such as Bayesian network, decision trees and time seriesmodeling have been proposed in literature classification of large data sets. Theuncertainty associated with the data, the immense size of the data to dealwith and the diversity of the data type and the associated rules and scalesare important factors to rely on unconventional mathematical tools such asComputational intelligence based techniques in the domain of soft computingsuch as Artificial Neural Network (ANN) and Support Vector Machine (SVM),Logistic Regression, Fuzzy Reasoning, evolutionary computing for data analysisand interpretation have been successfully used for data mining and machinelearning. These techniques provide a mathematical framework for identifyingcustomers profiles and patterns in large datasets representing the customers’data and their preferences.

3.1 Artificial Neural Network

Neural Networks concept in computer science discipline was inspired by thearchitecture as well as functioning of the human brain. It exhibits certain fea-


tures such as the ability to learn complex patterns of the information and thengeneralize the knowledge gained (Rosenblatt, 1962; Venugopal & Baets, 1994).Artificial Neural Networks (ANNs) attempt to model the architecture of bio-logical neural networks. Biological neural networks are made of simple, tightlyinterconnected processing elements called neurons. The interconnections aremade by the outgoing branches, the “axons”, which again form several connec-tions (“Synapse”) with the other neurons. When a neuron receives a number ofstimuli, and when the sum of the received stimuli exceeds a certain thresholdvalue, it will fire and transmit the stimulus to adjacent neurons. The biggestadvantage of neural network methods is that they can handle problems withlarge number of parameters, and still are able to classify objects well even whenthe distribution of objects in the N-dimensional parameter space. ANN has theability to give multiple outputs from the same source of information. It classifiesthe output in multiple classes as desired by the analyst. The neural network de-sign process primarily involves the following sequence of steps: Collecting data,Creating the network, Configuring the network, Initializing the weights and bi-ases, Training the network, Validating the network, Finally, Using the networkfor prediction purpose.

Artificial Neural Network are have been designed in three stages/layers :Input Layer(I), Hidden Layers(Hi) and Output Layer (O) . This notation hasbeen used in Table 3. Input layers can have n-inputs which is primarily decidedbased on the number of attributes to be used. Depending on the applicationit can have multiple outputs used for Multi-Class classification or single-class(also called as Regression) in the output layer. Hidden layers can be single ormultiple depending on the need for designing the network. Usually one hiddenlayer is sufficient for most of the applications depending on the problem andnetwork design. However, more than one hidden layers can be used to enhancethe accuracy and robustness of the model. In this work, we have used one, twohidden layers for our experiments.

3.2 Support Vector Machine

SVM is a relatively new supervised machine learning techniques with a strongmathematical foundation being used for solving a variety of problems and re-sulting in high performance due to it’s semi-parametric approach. SupportVector Regression(SVR) and SVM are based on statistical learning theory, Vap-nik Chervonenkis(VC) theory, developed over the last several decades (Vapnik,1995, 1998). The SVM, developed by Vapnik and others in 1995, is used formany machine learning tasks such as pattern recognition, object classification,and in the case of time series prediction, regression analysis. The SVM hasbeen successful as a high performance classifier in several domains includingpattern recognition, data mining and bioinformatics (Lovell & Walder, 2006;Hsu, Chang, & Lin, 2010). SVR, is the methodology by which a function isestimated using observed data which in turn “trains” the SVM.

To carry out the non-linear regression using SVR, it is necessary to map theinput space x(i) into a (possibly) higher dimension feature space ϕ(x(i)).


k(x, y) = 〈φ(x), φ(y)〉 (1)

k(x, y) is some kernel function,x denotes the feature space taken as input parameters,y denotes the target or output vector.

The kernel functions used in our work are Polynomial, RBF (also sometimestermed as Gaussian) and Sigmoid (Hyperbolic Tangent) kernels which have beendescribed below:

Polynomial Kernel

k(x, y) =(αxT y + c)d

)(2)

RBF (Gaussian) Kernel

k(x, y) = exp

(−||x− y||2

2σ2

)(3)

Sigmoid Kernel

k(x, y) = tanh(αxT y + c) (4)

Kernel methods are a class of algorithms for pattern recognition and identi-fying patterns, whose best known element is the SVM. The use of kernels is thekey in SVM/SVR applications. The kernel trick is a mathematical tool whichcan be applied to any algorithm which solely depends on the dot product of twovectors. Wherever a dot product is used, it can be replaced by a kernel function.When properly applied, linear algorithms are transformed into a non-linear al-gorithms (sometimes with little effort or reformulation). The use of a positivedefinite kernel ensures that the optimization problem will be convex and solu-tion will be unique. SVMs have also been proven to outperform other non-lineartechniques including ANN, Logistic Regression (Sapankevych & Sankar, 2009).These learning algorithms have also been applied to general regression analysis:the estimation of a function by fitting a curve to a set of data points. Moredetails about kernel functions can be found here (Souza, 2010).

4 Classification of Telecom customer data

Telecom companies generate huge data based on customer profiles. Some of thedata are static (such as, demographic data, socio-economic factors, gender andpreferences) that are collected when the mobile numbers are issued; whereasothers (such as call usage profile, packs activated, services used) are dynami-cally recorded in a spatio-temporal database. This data would prove to be anasset to the telecom company provided it can dig out reasonable amount of in-formation about the customer inorder to identify and deliver appropriate VASand customize them for the desired customer segment.


Based on the sample data from telecom companies, we have identified twenty-one attributes from the literature (Weiss, 2005; Ahn, Ahn, Oh, & Kim, 2011;Cui & Curry, 2005; J.-J. Huang et al., 2007; Mazzoni et al., 2007; Constan-tiou, 2009; Wang & Wang, 2009). The attributes used for the problem formu-lation are: Age(age), Gender(gen), Religion(rel), Qualification(qual), Occupa-tion(occ), State(st), Circle(ci), Plan Opted(pl-opt), Opting Any VAS(ot-vas),Roaming Duration(roam), Family Income(inc), Operating Any Banks Trans-actions(bank), Number of Family Members(f-mem), Marital Status(mar), TotalDay Minutes(d-min), Number of Total Day Calls(d-call), Total Night Minutes(n-min), Number of Total Night Calls(n-call), Total Minutes per Day(m-day), Av-erage Duration per Call(dur), Avg. Monthly Minutes consumed(avg-min).

4.1 Rules

Out of 21 attributes, ten important attributes have been considered for the clas-sification of the customers inorder to identify their needs and calling patterns.This is presented in the Table 2.

Consultation with subject matter experts from various telecom companieshelped us in identifying the needs of the customers and accordingly differentschemes or products are designed as part of the targeted marketing after sege-menting the customers.

Several cost models have been explored and a linear cost model has beenapplied with the various plans associated with different customer segments. Weare refining the cost model and the attributes and the plans from the company sothat this forms a Decision Support System for formulating the plans to optimizethe portfolio and maximize the company’s revenue. In our cost model, we haveconsidered the distribution of customers and considering the cost associatedwith the calls made to a local network, roaming network, Same Operator’sLocal or STD number, Other Operator’s Local or STD number, Night Calls,SMS charges Local or STD, Data Usage(Internet), and ISD Calls to obtain theexpected revenue from each plan.

5 Results and Analysis

The ANN and SVM have been trained to give outputs belonging to 13 classeslabeled A to M. A test data set was generated using statistical distributionssuch as, uniform, normal and Bernoulli distributions based on the attributehaving close resemblance to real data set. However, targets were decided basedon different criteria like age, monthly income, mobile usage time and duration,gender, education and call usage.

The data was normalized between -1 to 1 before training and testing so thatno attribute dominates in making decision. The ANN and SVM both weretrained with 3000 data sets with 5 fold cross-validation checks. Both modelswere tested on another 3000 samples. We conducted extensive experimentsusing MATLAB for ANNs and LIBSVM (Chang & Lin, 2001) for SVM with


Rule based ClassificationS.No. Rule Applied Label1 age(LOWER-YOUNG-AGE) and

occ(Student, Unemployed) andmar(Unmarried) and avg-min(≥ 2000)

A - Single Young Un-employed Normal Phoneusers

2 age(LOWER-MIDDLE-AGE) andocc(Student, Unemployed) andmar(Unmarried) and avg-min(1500-2000)

B - Single Young Un-employed Cautious Phoneusers

3 age(MIDDLE-AGE) and qual(Graduate) andocc(Unemployed) and avg-min(≤ 3500)

C - Young unemployedgraduate with high usage

4 roam(More than two weeks) andocc(Business) and avg-min(≥ 2000) andbank(Yes) and inc(Less than Rs. 10K)

D - Low income business-men with high romaingactivity

5 roam(More than a week) and occ(Business)and avg-min(≥ 2000) and inc(50K-100K)

E - High income business-men heavy mobile users

6 age(MIDDLE-AGE) and roam(Less than aweek) and occ(Professional) and avg-min(≥1000) and ci(A)

F - Young professional lessphone usage residing ingood economic zone

7 avg-min(≤ 1500) and cir(C) G - Cautious user in loweconomic zone

8 avg-min(≥ 1500) and cir(C) H - Heavy mobile user inlow economic zone

9 avg-min(≤ 1500) and cir(A,B) I - Cautious user in mod-erate economic zone

10 occ(Agriculture) and avg-min(≤ 2000) andci(A,B,C)

J - Cautious Farmers

11 age(OLD-AGED) K - Old Age people12 age(UPPER-MIDDLE-AGE) L - Middle Aged people13 Others M - All Others

Table 1: Associating rule applied for labeling the customer preferences

different kernels to make sure the robustness of the model developed by boththe approaches.

We used Levenberg-Marquardt (LM) Technique to train Feed Forward NeuralNetwork whereas three different kernels (Polynomial, RBF and Sigmoid) wereused for SVM. Grid search technique was used to find the range which helped usto identify the proper values of the kernel parameters to achieve better accuracyor classification.

The classification accuracy achieved by both the techniques are presented inTable 3, 4, 5, 6:

We found SVM-RBF Kernel a very powerful kernel technique for the classi-fication of telecom customers when compared with different kernel methods aswell as ANN experimented over number of iterations. To find the best value forthe given problem we used grid search technique to find different values for γ,


Customers’ attributes based classification1. (age = LOWER YOUNG ∧ occ = STUDENT ∨ UNEMPLOYED

∧ mar = UNMARRIED ∧ incoming = HIGH ∧ outgoing = HIGH∧ SMS = HIGH) 7→ Plan 1

(STUDENT)

2. (age = LOWER YOUNG ∧ occ = STUDENT ∨ UNEMPLOYED∧ avg-min = AVERAGE ∧ incoming = LOW ∧ outgoing = HIGH∧ SMS = LOW) 7→ Plan 2

(STUDENT orJOB SEEKER)

3. (age = YOUNG ∧ qual = GRADUATE ∧ occ = UNEMPLOYED∧ avg-min = VERY HIGH) 7→ Plan 3

(JOB SEEKER)

4. (age = YOUNG ∧ occ = SELF ∧ incoming = HIGH ∧ avg-min= HIGH ∧ SMS = LOW ∧ inc = HIGH) 7→ Plan 4

(AGGRESSIVETALKER)

5. (age = YOUNG ∧ occ = SELF ∧ incoming = HIGH ∧ avg-min= AVERAGE ∧ inc = BELOW AVERAGE ) 7→ Plan 5

(PURPOSEFULTALKER)

6. (age = YOUNG ∧ roam = HIGH ∧ incoming = AVERAGE ∧occ = PROFESSIONAL ∧ avg-min = HIGH ∧ cir = A ∨ B ) 7→Plan 6

(FREQUENTTRAVELLER)

7. (age = YOUNG ∧ roam = LESS ∧ incoming = LOW ∧ occ =PROFESSIONAL ∧ avg-min = LOW ∧ cir = B ∨ C ∧ ot-vas =YES) 7→ Plan 7

(RESERVEDUSER)

8. (age = OLD ∧ avg-min = LOW ∧ cir = B ∨ C ) 7→ Plan 8 (OLD PEOPLE)9. (age = YOUNG ∧ n-min = HIGH ∧ avg-min = VERY HIGH ∧

cir = B ∨ C ) 7→ Plan 9(NIGHT PLANS)

10. (age = YOUNG ∧ occ = AGRICULTURE ∧ n-min = LOW ∧avg-min = HIGH) 7→ Plan 10

(AGRI PROMOTEPLAN)

11. NO RULE 7→ Plan 11 (STANDARDRATES)

Table 2: Plan based classification as per customer preferences

Artificial Neural Network TechniqueS.No. Architecture (I:Hi:O) Mean Square Error Accuracy1 21:10:10:13 0.0219 77.7%2 21:15:15:13 0.026 76.3%3 21:10:13 0.0273 77.6%4 21:15:13 0.0265 74.8%

Table 3: Neural Network Performance

C and d. We found that ANN struggled to cross 77.7% accuracy whereas SVMwith sigmoid kernel staggered at around 74% and polynomial kernel resultedin giving accuracy of 78.16%. RBF kernel performed better than all the dis-cussed technique with a performance yielding around 80.13% with C = 1024,γ = 0.000976563 or log(γ) = −10.0. With the grid search approach we canconclude that the performance of SVM may increase if C lies between 1024 and2048 whereas γ may be set accurately ranging somewhere around log10.


SVM with Polynomial KernelS.No Degree(d) γ log(γ) Accuracy1 2 0.0625 -4.0 78.0667%2 3 0.03125 -5.0 78.1667%3 4 0.03125 -5.0 77.9667%

Table 4: SVM Performance using Polynomial Kernel

SVM with Sigmoid/tanh KernelS.No γ log(γ) Accuracy1 0.0625 -4.0 59.1667%2 0.03125 -5.0 74.5667%3 0.015625 -6.0 62.4333%

Table 5: SVM Performance using Sigmoid/Tanh Kernel

SVM with RBF KernelS.No C log(C) γ log(γ) Accuracy1 0.03125 -5 0.0625 -4 44.13 %2 0.0625 -4 0.125 -3 51.40 %3 0.125 -3 0.0625 -4 68.43 %4 0.25 -2 0.0625 -4 75.27 %5 0.5 -1 0.0625 -4 77.20 %6 1 0 0.03125 -5 77.67 %7 2 1 0.03125 -5 78.17 %8 4 2 0.03125 -5 78.27 %9 8 3 0.015625 -6 78.40 %10 16 4 0.015625 -6 78.60 %11 32 5 0.0078125 -7 78.80 %12 64 6 0.0078125 -7 79.23 %13 128 7 0.00390625 -8 79.57 %14 256 8 0.00390625 -8 79.80 %15 512 9 0.001953125 -9 79.97 %16 1024 10 0.000976563 -10.00 80.13%17 2048 11 0.000976563 -10.00 80.03 %18 4096 12 0.000976563 -10.00 79.87 %19 8192 13 0.000976563 -10.00 79.70 %20 16384 14 0.000976563 -10.00 79.13 %21 32768 15 0.000976563 -10.00 78.93 %22 65536 16 0.000976563 -10.00 78.77 %

Table 6: SVM Performance using RBF Kernel

6 Conclusions and Future work

In this work we found SVM as a better alternative to ANN for classificationof customers in the telecom domain which can be used effectively for target


marketing and promoting services in the form of VAS to the increase the utilityfor the customers and a better revenue earning for the telecom operators. Byusing this approach, the marketing and the telecom companies would be in abetter position to yield good returns. In future work, sensitivity analysis ofdifferent features is to be explored to see the effect on classification accuracy.Different bio-inspired optimization techniques also needs to be explored to findthe global optimum value for the kernel parameters and designing of suitableplans for the right segment. Feature selection algorithms might also play crucialrole in determining the accuracy of the model. Evaluation of different kernelswhich might be best suitable would be an interesting area to be explored. Costmodels related to telecom services are also being explored.

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