COMPARATIVE STUDY OF SUPERVISED LEARNING IN CUSTOMER RELATIONSHIP MANAGEMENT

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1 International Journal of Computer Engineering & Technology (IJCET) Volume 8, Issue 6, Nov-Dec 2017, pp , Article ID: IJCET_08_06_009 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication COMPARATIVE STUDY OF SUPERVISED LEARNING IN CUSTOMER RELATIONSHIP MANAGEMENT Tamanna Kachwala Ph. D. Scholar, Rai University, Saroda, Ahmedabad , Gujarat, India L. K. Sharma ICMR-National Institute of Occupational Health Ahmedabad , Gujarat, India ABSTRACT Customers are valuable to an organization. The competitive market environment makes customer relationship management very noteworthy for the business prospects. Therefore, research on customer relationship management is attracting data mining researchers. Data mining can support customer expansion by matching products with customers and better pursuing of product promotion campaigns. In this study, classification algorithms, namely J48, SGD, Bayes Net and Naïve Bayes Updatable were experimented on customer data. The comparison of these classification algorithms based on different performance metrics is presented. It will help to select a best suitable algorithm. The performance of the classification models is measured using 10-fold cross validation. The WEKA environment was utilized for the experiments and assessments of these methods. The 80% data were correctly classified by all these methods. It reveals that data mining, classification methods can be adopted for the customer relationship management study. Key word: Customer Relationship Management, Data Mining, Classification, C4.5, SGD, Naïve Bayes Updatable, Bayes Net, WEKA. Cite this Article: Tamanna Kachwala and L. K. Sharma, Comparative Study of Supervised Learning In Customer Relationship Management. International Journal of Computer Engineering & Technology, 8(6), 2017, pp INTRODUCTION Customer satisfaction gives assurance of business growth for a longer period. It is challenging task in business to increase constancy among existing customers and appeal to new customers to adopt the products or services offered by an organization. Customer Relationship Management (CRM) is the strategy for building, managing, and strengthening loyal and long editor@iaeme.com

2 Tamanna Kachwala and L. K. Sharma lasting customer relationships. CRM strategy helps an organization to improve the business processes and technology solutions around selling, marketing, and servicing functions across all customer touch-points [1] [2]. Therefore, in present scenario, CRM became growing and importance study area. Customers are the most vital part of a business. Any business scenario is not possible without satisfied customers who remain reliable and develop their relationship with the organization. That is why an organization should plan and use a clear strategy for treating customers. Data mining can help in customer retention as it enables the timely identification of valuable customers with increased likelihood to leave, allowing time for targeted retention campaigns. It can support customer development by matching products with customers and better targeting of product promotion campaigns. It can also help to identify distinct customer groups. Data mining aims to extract knowledge and insight through the analysis of large amounts of data using sophisticated modelling techniques [3]. It converts data into knowledge and actionable information. The data to be analysed may reside in well-ordered data marts or may be extracted from different unstructured data sources. A data mining procedure has many stages. It normally involves extensive data management before the application of a statistical or machine learning algorithm and the development of an appropriate model. Specialized software packages have been developed (data mining tools), which can support the whole data mining procedure. Data mining models consist of a set of rules, equations, or complex transfer functions that can be used to identify useful data patterns, understand, and predict behaviours. Predicative modelling techniques such as classification is an important data mining task [4]. It is also called supervised learning. Supervised learning can be described as it is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictive features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown [5]. 2. RELATED STUDY Digitization has influenced various fields of human s work, including customer relationship management. Data mining can play a very important role towards the CRM, therefore; data mining researchers interacted on CRM research and applying data mining methods to significant management of customer relationship data for the customer oriented strategy [6]. Natchaiar and Baulkani [7] proposed the feature selection method for the CMR data set. It improves the data quality and enhance the performance of classification. Vafeiadis et al. [8] performed a comparison of supervised learning method for the customer churn predication for the telecommunication industry and it found that SVM perform well for this data set. Xiao et al. [9] presented customer churn predication method. The classification method is used for this task. Fang et al. [10] applied decision tree and support vector machine classifier for the insurance customer profitability predication. Fathin et al. [11] used self-organizing map (SOM) clustering technique along with classifier techniques including decision tree, artificial neural networks, support vector machine, and K- nearest neighbours for the customer churn predication. Ansari and Riasi [12] utilized neural network for the evaluation of affecting factors of customer loyalty of insurance companies. The above studies show that the supervised learning method can be an important tool for the customer relationship management, such as customer identification, attraction, retention and development. It also shows that the performance of supervised learning method varies as 78 editor@iaeme.com

3 Comparative Study of Supervised Learning In Customer Relationship Management per features selection method, the size of data and the nature of the data set. Therefore, a systematic comparative analysis is required before adopting the supervised learning method for any domain specific tasks. 3. METHODOLOGY 3.1. Data set The two data were obtained from the UCI machine-learning repository related to customers. The first data is associated with direct marketing campaigns of a Portuguese banking institution [13]. The phone calls were used for the marketing campaigns. This data set includes 20 various social, economic attributes of customers. The second data set contains information on customers of an insurance company. The data consist of 86 variables and includes product usage data and socio-demographic data. The data were supplied by the Dutch data mining company Sentient Machine Research and is based on a real world business problem. The training set contains over 5000 descriptions of customers, including the information about whether or not they have a caravan insurance policy. A test set contains 4000 customers of whom only the organisers know if they have a caravan insurance policy [14] Supervised Learning Method In this study, C4.5 decision tree, Stochastic Gradient Descent, Bayes Net, Naïve Bayes Updatable, Back Propagation Network, Radial Basis Function supervised learning methods were considered for the comparative assessments. C4.5 is one of the most popular supervised learning methods in the machine learning. It produces the decision tree. A decision tree is equivalent because of the tree structure having different nodes, like root node, intermediate nodes and leaf node. Decision tree split the input gap of a data set into mutually exclusive areas, where each area having a label, a value or an action to express its data points. C4.5 produces small and accurate decision tree for the many domains and it generate fast result and reliable classifiers [15]. Stochastic Gradient Descent (SGD) is an easy and efficient approach for discriminative learning. It implements stochastic gradient descent for understanding a variety of linear models. Worldwide replaces every missing values and transforms nominal attributes to binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. In support of numeric class attributes, the squared, Huber or epsilon-insensitive loss function must be used [16]. In Bayes Net classifier conditional chance on each node is calculated first and then a Bayesian Network gets produced. Bayesian Network is like to directed acyclic graph. The best guess made in Bayes Net is, that every one of attributes are nominal and there are no missing values and such value replaced worldwide. The several search methods and quality measures are used by Bayes network learning. It has two stage process; first learn a network structure and then learn the probability tables [17]. Naïve Bayes Updatable is the improved edition of NaiveBayes. A default accuracy utilized by this classifier when build Classifier is known with 0 training instances is of 0.1 for numeric attributes and therefore it also identified as incremental update. It utilizes the nonparametric density estimation statistical methods [18]. Back Propagation Network (BPN) is a Multi-Layer Perceptron Learning (MLP) method capable to classify the non-linear input data. It uses extended gradient-descent based delta learning rule known as back propagation. During classification, the signal at the input units 79 editor@iaeme.com

4 Tamanna Kachwala and L. K. Sharma propagates all the way through the net to determine the activation values at all the output units. Each input unit has an activation value that represents some feature external to the net [5]. Radial Basis Function (RBF) network is a three-layer feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden unit outputs. Further, RBF Network (RBFN) was also implemented wherein a normalized Gaussian radial basis function network is the basis of process [5]. 4. EXPERIMENT AND RESULTS The supervised learning methods were applied on above both dataset using Weka 3.8 [19]. The test data set and 10-fold cross validation were to validate the model and measure the accuracy of the classifier model [20][21][22]. The detailed results of 10-fold cross validation test for the Bank dataset are shown in Table 1 and Table 2 shows results of 10-fold cross validation test for the Insurance company data set. The 10-fold cross validation estimator has a lower variance than a single holdout set estimator (test data set validation), which is important if the amount of data available is limited. In case of a single hold-out set, 70% of data are used for training and 30% used for testing, the test set is considered as small, and there ought to be variation in the performance estimate for different samples of data, or for different partitions of the data to form training and test sets. However, with 10-fold validation, the variance is reduced by averaging over 10 different partitions to form 10 sub-sets; making the performance estimate less sensitive to the partitioning of data [20][21][22]. The results shows that the BPN and C4.5 classifiers are leading in term of correctly classified instance 90.50% and 90.31% respectively in case of banking dataset. The RBF and C4.5 classify correctly 94.02% in both methods in case of insurance company dataset. It reveal that if number of data set is large than BPN is better than RBF and C4.5 methods perform better in the both the cases. The accuracy of different methods on these two dataset is depicted on Figure 1. It can be observed that the performance of classification methods BN, NBU and BPN is better when the number of instances is large and the performance of classification methods C4.5, SGD and RBF is better the when the number of instances is less Overall, the selected classification methods classify more than 80% correctly for the both the dataset. Table 1 Comparison for various classifiers using Banking Dataset Method/Parameters C4.5 SGD BN NBU BPN RBF Correctly classified Instances (%) Incorrectly classified Instances (%) Kappa statistic Mean absolute error Root mean squared error Relative absolute error (%) Root relative squared error (%) Total Number of Instances editor@iaeme.com

5 Comparative Study of Supervised Learning In Customer Relationship Management Table 2 Comparison for various classifiers using Insurance company dataset Method/Parameters C4.5 SGD BN NBU BPN RBF Correctly classified Instances (%) Incorrectly classified Instances (%) Kappa statistic Mean absolute error Root mean squared error Relative absolute error (%) Root relative squared error (%) Total Number of Instances Figure1 Shows the graphical view of accuracy for various classifiers on different datasets. 5. CONCLUSION Data mining methods can be a better tool for the benefit to both customer and firm. In this study, the data mining method namely, supervised learning methods were experimented for the customer relationship management. The banking and insurance sector data was utilized and the most adopted supervised learning methods in other applications were selected for the experiment and performance comparisons. The result reveals that the all selected classification methods produces more than 80% accuracy and highest achieved accuracy is 90.31% and 94.02% for the banking and insurance data respectively. It can be concluded that data mining methods such as supervised learning will be useful for the customer identification, attraction, development and retention in CRM. REFERENCES [1] Chen I. J. and Popovich K., Understanding customer relationship management (CRM): People, process and technology, Business Process Management Journal, 9 (5), 2003, pp [2] Kaur J., Customer Relationship Management: A Study of CRM Policies of Different Companies, Global Journal of Finance and Management, 8(2), 2016, pp [3] Han J., Kamber M and Pei J., Data Mining Concepts and Techniques, 3 rd Edition, Morgan Kaufmann Publication, [4] Maimon O. and Rokach L., Introduction to knowledge discovery and data mining. In Data Mining and Knowledge Discovery Handbook, Springer US, 2010, pp editor@iaeme.com

6 Tamanna Kachwala and L. K. Sharma [5] Kotwiantis S. B., Supervised machine learning: A review of classification, Informatica 31, 2007, pp [6] Wang L., the New Trend and Application of Customer Relationship Management under Big Data Background, Modern Economy, 7, 2016, pp [7] Natchair S. U. and Baulkani S., Customer relationship management classification using data mining techniques, IEEE International Conference on Science Engineering and Management Research, 2014, DOI: /ICSEMR [8] Vafeiadis T., Diamantaras K.I., Sarigiannidis G., Chatzisavvas K.Ch., A comparison of machine learning techniques for customer churn prediction, In Simulation Modelling Practice and Theory, 55, 2015, pp [9] Xiao J. et al., Churn Prediction in Customer Relationship Management via GMDH-Based Multiple Classifiers Ensemble, IEEE Intelligent Systems 31, 2016, pp [10] Fanga K. et al., Customer profitability forecasting using Big Data analytics: A case study of the insurance industry, Computers & Industrial Engineering, 101, November 2016, pp [11] Fathian M. et al., Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods, Kybernetes, 45(5), 2016, pp [12] Ansari A. and Riasi A., Modelling and evaluating customer loyalty using neural networks: Evidence from start-up insurance companies, In Future Business Journal, 2(1), 2016, pp [13] Moro S., Cortez P. and Rita P., A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62, June 2014, pp [14] P. van der Putten and M. van Someren (eds). CoIL Challenge 2000: The Insurance Company Case. Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report June 22, [15] Quinlan, J.R., C4.5 Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, [16] Bottou L., Large-Scale Machine Learning with Stochastic Gradient Descent. In: Lechevallier Y., Saporta G. (eds) Proceedings of COMPSTAT'2010. Physica-Verlag HD, [17] Bouckaert R. R., Bayesian Network Classifiers in Weka for version 3.5.7, The university of Waikato, New Zeeland, URL: [Last access on 12/12/2017] [18] George H. John and Pat L., Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 1995, pp [19] Frank E., Hall M. A., and Witten I. H. The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Fourth Edition, [20] Elkan C., Evaluating Classifiers. Department of Computer Science and Engineering, University of California, San Diego; [21] Song Q, Wang G and Wang C., Automatic recommendation of classification algorithms based on data set characteristics. Pattern Recogn. 45(7), 2012, [22] Majumder J. and Sharma L. K., Application of Data Mining Techniques to Audiometric Data among Professionals in India, Journal of Scientific Research Reports, 3(23), 2014, pp editor@iaeme.com