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50120140501012
50120140501012
50120140501012
50120140501012
50120140501012
50120140501012
50120140501012
50120140501012
50120140501012
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50120140501012

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  • 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & 6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 1, January (2014), pp. 103-111 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME DECISION SUPPORT SYSTEM FOR TELECOM COMPANY Charvi Kunder1, 1, 2, 3 Divya Bhat2, Harshita Kotian3 (Computer Engineering, St. Francis Institute of Technology/ Mumbai University, Mount Poinsur, S.V.P. Road, Borivli (West), Mumbai 400 103, India) ABSTRACT Decision support System (DSS) is a computer-based information system that supports decision-making activities in the sense of assisting human decision makers in the exercise of judgment, but which does not itself make the decision. DSS benefits the management in terms of identification of negative trends, better allocation of business resources and information representation in the form of charts, graphs i.e. in a summarized way. One of its important applications is the price setting process or the calculation of expenditure and modifying plans based on user usage for various services provided to the customers by various firms. Telecom system is such a system where information is very broad & continually updated. The aim of the paper is to represent an intelligent decision support system that will enable the service provider to gain vital insights into the prevailing trends and judge the profits made. The knowledge thus gained can be utilized to identify the plans that need to be modified according to customer preference. The system thus developed can be used in finding out what are the chances that a customer may port out. Also it will be useful in identifying plans that may help to get back the ported out customers by offering optimized plans to them. Keywords: Customer relationship management (CRM), Decision support systems (DSS), Fuzzy Inference System (FIS), optimize, portability, RFM analysis. 1. INTRODUCTION Decision support systems (DSS) are defined as interactive computer-based systems intended to help decision makers utilize data and models in order to identify problems, solve problems and make decisions. They provide support for decision making, they do not replace it. The mission of decision support systems is to improve effectiveness, rather than the efficiency of decisions. Modern organizations use several types of decision support systems to facilitate decision support. In many 103
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME cases, OLAP based tools are used in the business areas, which enable multiple views on data and through that a deductive approach to data analysis. Data mining extends the possibilities for decision support by discovering patterns and relationships hidden in data and therefore enabling the inductive approach of data analysis. Tariff plan designing in a telecom company involves complicated decision-making processes that require different types of information and knowledge. However, typical accounting systems do not readily provide the expected costs for different sales levels. For this, a model is required which separates out the fixed and variable costs and then allows one to predict what the costs would be as the sales or other variables, which impact upon the costs change. It has been estimated that it costs five times as much to attract a new customer as it does to retain an existing one. Creating a loyal customer is not only about maintaining numbers of customer overtime, but it is creating the continuous relationship with customers to encourage purchasing in the future. Instead of attracting new customers, they would like to perform as well as possible more business operations for customers in order to keep existing customers and build up long-term customer relationship. Based on this reason, the paper aims at optimizing the internet tariff plans based on the profits made and customer preference. Also a rule based approach is suggested for estimating the chance of porting out of a customer using a FIS (Fuzzy Inference System). 2. LITERATURE REVIEW A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSS serve the management, operations and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in advance. DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents and personal knowledge or business models to identify and solve problems and make decisions. Using DSS all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decision. Benefits of DSS are: • • • • • • • • • Improves personal efficiency Speed up the process of decision making Increases organizational control Encourages exploration and discovery on the part of the decision maker Speeds up problem solving in an organization Creates a competitive advantage over competition Reveals new approaches to thinking about the problem space Helps automate managerial processes Create innovative ideas to speed up the performance. The use of data mining to facilitate decision support can lead to the improved performance of decision making and can enable the tackling of new types of problems that have not been addressed before. The paper [1] introduces data mining based decision support system, which was designed for business users to enable them to use data mining models to facilitate decision support with only a basic level of knowledge of data mining. The integration of data mining and decision support can significantly improve current approaches and create new approaches to problem solving, by enabling the fusion of knowledge from experts and knowledge extracted from data. 104
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME Customer churn management, as a part of Customer relationship management (CRM), has become a major concern. CRM is a strategic approach which targets the development of profitable, long-term relationships with key customers and stakeholders. Due to saturated markets and intensive competition, more and more companies have recognized the importance of CRM and have changed their product centric, mass marketing champion strategies toward customer centric, targeted marketing [2]. In mobile telecommunications, the term ‘churn’ refers to the loss of subscribers who switch from one provider to another during a given period. The Walker Information research study evaluated four sectors within telecom: Internet service providers, wireless providers, long distance providers, and local service providers. Nationwide, only 28 percent of customers want to continue their relationship and plan to keep buying service from their current provider, the Walker report states. About half (46 percent) of telecom customers say they are trapped or not pleased with their provider, but will stay until something better comes along. Another 25 percent are considered high risk or ready to begin purchasing from a competitor at any time. Based on a study [3], the estimated average churn rate for mobile telecommunications is about 2.2% per month. This means that one in fifty subscribers of a given company discontinue their services every month. As it is more profitable to retain existing customers than to constantly attract new customers, therefore it is crucial to build an accurate churn prediction model for identifying those customers who are most prone to churn. About 42.03 Lakh subscribers submitted number porting requests in the month of January 2013, according to the Indian Telecom regulator (TRAI).While companies have added new subscribers in the rural areas with a monthly growth of 1.93% and they have reported a net drop of 1.39% in subscriber base in the urban areas in February 2013 Established literature on customer churn uses various data mining technologies, such as Neural Networks, Clustering, Decision Tree, Regression, Support Vector Machine and ensemble of hybrid methods to provide more accurate predictions. Generally, no tool for data mining in CRM is perfect because there are some uncertain drawbacks in it [4]. For example, TABLE 1: Drawbacks of data mining tools Data mining tools Drawbacks ANN No. of hidden layers, training parameters DECISION TREES Too many instances lead to large decision trees GA Brute force computing convergence method, slow Therefore a model for churn prediction based on fuzzy inference system is presented. 2.1 Churn prediction modeling Facing with more complexity and competition in today’s business, firms need to develop innovation activities to capture customer needs and improve customer satisfaction and retention. In this regard, CRM is a broadly recognized strategy to acquire and retain customers. In today’s competitive world, moving toward customer-oriented markets with increased access to customer’s transaction data, identifying loyal customers and estimating their lifetime value makes crucial [3]. Since knowledge of customer value provides targeted data for personalized markets, implementing CRM strategy helps organizations to identify and segment customers and create long-term relationships with them, and as a result, they can maximize customer lifetime value. Data mining 105
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME techniques are known as a powerful tool for this purpose. The purpose of the paper [3] is customer segmentation using RFM technique and clustering algorithms based on customer’s value, to specify loyal and profitable customers. The paper uses a combination of behavioral and demographical characteristics of individuals to estimate loyalty. Bhatnaga and Ghose (2004) [3] provide a new transaction model based on service and customer satisfaction and showed that price is not the only measure that affects customer purchasing decisions, but also it is important that customer and company agree on product value and good customer services . Therefore, organizations should not seek to develop a product to satisfy their customers, but they should track customer purchase behavior and present distinct products for each segment. In other words, customer segmentation based on buying behavior is essential for developing successful marketing strategies, which in turn cause creating and maintaining competitive advantage. Current methods of customer value analysis based on past customer behavior patterns or demographic variables, are limited for predicting future customer behavior. So today, analysts are trying to use better patterns for analyzing customer value. Nowadays, RFM method is one of the most common methods for segmenting and identifying customer value in the organization. [3]. 2.2 RFM model In most of the researches, two methods are common for identifying loyal customers, one of them is in terms of demographic variables (such as age, gender, etc.) and the other is in terms of interactive behaviors of customers that are expressed with the so-called R-F-M. RFM model is proposed by Hughes in 1994, and has been used in direct marketing for several decades. Customers who have the most RFM score are profitable. Implementing the RFM analysis is a very simple process, calculations are simple and the marketing personnel can easily analyze the subscriber’s behavior without the need for professional IT programs [5]. Targeting subscribers is improved by the RFM analysis because it examines when, how often and in what amounts the purchases were made. In successive years (Kotler, 1994; Peppers & Rogers 1995, Schijns and Schroder 1996) also studies proved efficiency the RFM Moreover, certain authors have proposed modified variants of RFM: Yeh, Yang and Ting (2009) [1] proposed the RFMTC model (Recency, Frequency, Monetary value, Time elapsed from the last acquisition and Churn probability). 3. ESTIMATING THE CHANCES OF A CUSTOMER PORTING OUT In order to find what are the chances that a customer may port out from the services of a service provider a FIS (Fuzzy Inference system) is created with two variables, loyalty of customers and customer satisfaction. Here the customer satisfaction is obtained by using a rule based approach with two variables, number of complaints and count of plan change made by the customer. Satisfaction Rule base: a. Number of complaints (less, average, more) b. Number of plan change (less, average, more) c. Satisfaction (least, medium, high) Example: a. If number of complaints is ‘more’ and plan change count is ‘less’ then satisfaction is ‘medium’. b. If number of complaints is ‘more’ and plan change count is ‘more’ then satisfaction is ‘least’. 106
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME Portability Rule Base a. Loyalty (Very high, high, medium, low, very low) b. Satisfaction (least, medium, high) c. Portability (Very high, high, medium, low, very low) Example: a) If loyalty of customer is ‘high’ and satisfaction is ‘medium’ then chances of porting out is ‘medium’. b) If loyalty of customer is ‘low’ and satisfaction is ‘least’ then chances of porting out is ‘very high’. Using rule based approach the estimation is made for current customer about the chances of porting out. 3.1 Customer clustering using RFM and demographic variables Fig.1 A Two-Phase Clustering Process A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. The clustering algorithms used include K-Means and Two-step-K-means, which at first was known as Forgy method, is one of the well-known algorithms for clustering with top to down approach that desired data objects divide into k groups in terms of its special features and characters [6]. Groups are classified based on minimum sum of squares distance between object from center. This method is dependent on the definition of initial centers, so this algorithm must be run with different centers, and a case is acceptable which has the lowest error rate in terms of Euclidean distance. On the other hand, this algorithm is very sensitive to noise in data. In contrast, Two-step algorithm doesn’t need the exact number of clusters which was defined by user and it determines the optimum number of clusters in a range which is defined by user. 107
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME The paper [6] proposes a new segmentation method based on data mining and most commonly used CRM model: RFM and demographic variables. A two-phase clustering is utilized to segment customers. One of the key purposes of marketing is to identify the target customer portfolios and analyze it by segmentation and then set marketing strategies to each segment in order to reduce the risk of significant customer’s defection. 3.1.1 Customer clustering using RFM The computing process is introduced step by step as follows: Step 1: Data preprocessing At first, select the dataset for empirical case study. To preprocess the dataset to make knowledge discovery easier is needed. Thus, we firstly delete the records which include missing values or inaccurate values, eliminate the redundant attributes and transform the datum into a format that will be more easily and effectively processed for clustering customer value. Step 2: Cluster customer value by K-means algorithm. The following step is to define the scaling of R–F–M attributes based on Hughes (1994) and yield quantitative value of RFM attributes as input attributes, then cluster customer value by using Kmeans algorithm. The detail process of this step is expressed into two sub-steps. Step 2-1: Define the scaling of R–F–M attributes: This sub-step process is divided into five parts introduced in the following: (1) The R–F–M attributes are equal weight (i.e. 1:1:1). (2) Define the scaling of three R–F–M attributes, which are 5, 4, 3, 2 and 1 that refer to the customer contributions to revenue for enterprises. The ‘5’ refers to the most customer contribution to revenue and ‘1’ refers to the least contribution to revenue. (3) Sort the data of three R–F–M attributes by descendant order. (4) Partition the three R–F–M attributes respectively into 5 equal parts and each part is equal to 20% of all. The five parts are assigned 5, 4, 3, 2 and 1 score by descendant order (TABLE 2) (5) Yield quantitative value of R–F–M attributes according to previous process (4) as input attributes for each customer. There are total 125 (5 _ 5 _ 5) combinations since each attribute in R–F–M attributes has 5 scaling (5, 4, 3, 2 and 1). RFM Score TABLE 2: Scaling of R-F-M attributes R- Recency (%) F– Frequency (%) M-Monetary (%) 5 Score 0 - 20 0 - 20 0 - 20 4 Score 20 - 40 20 - 40 20 - 40 3 Score 40 - 60 40 - 60 40 - 60 2 Score 60 - 80 60 - 80 60 - 80 1 Score 80 - 100 80 - 100 80 - 100 Step 2-2: Cluster customer value by K-means algorithm. According to quantitative value of R–F–M attributes for each customer, partition data (m objects) into K clusters using the K-means algorithm for clustering customer value. 108
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME Firstly, let k = 5 clusters (i.e. C1, C2, . . ., and C5) by clustering methods. Furthermore, we obtain that the center of the cluster C1, C2, C3, C4 and C5 is c1, c2, c3, c4 and c5, respectively as follows: C1={S11, S12,…, S1P1}, C2={S21, S22,…, S2P2},…, C5={S51, S52,…, S5Pk}. where 1 ≤ k ≤ m, Sij denotes the jth element of Ci, Pi denotes the number of elements in Ci and 1 ≤ i ≤ k. Due to the three attributes (R–F–M) (we set R, F, M = 1, 2, 3, respectively), we obtain c1, c2,. . ., and c5 as: c1 = (v11, v12, v13), c2 = (v21, v22, v23), . . . C5 = (v51, v52, v53). Secondly, compute the distance Di between ci and zero point: D1 = √ (v11 – 0)2 + (v12 - 0)2 + (v13 - 0)2 , D2 = √ (v21 – 0)2 + (v22 - 0)2 + (v23 - 0)2 , . . . D5 = √ (v51 – 0)2 + (v52 - 0)2 + (v53 - 0)2 . Thirdly, sort the distance D1 to D5 by descendant order and then based on this order, give the cluster C1 to C5 a class of customer loyalty. We name the 5 classes as Very High, High, Medium, Low and Very Low. ‘Very High’ refers to most customer loyalty and ‘Very Low’ refers to least customer loyalty. Finally, compute the data of customers to decide to which class is belonging (TABLE 3). TABLE 3: The cluster results by K-means with 5 classes on output (Values obtained by considering a certain dataset as example) Cluster Centre C1 C2 C3 C4 C5 c1_R 4.52 3.68 1.58 3.58 1.67 c2_F 4.54 1.96 1.96 2.89 1.13 c3_M 3.62 4.22 4.30 1.64 1.66 The distance to zero point 7.63 5.93 4.98 4.89 2.61 Very high High Medium Low Very Low Loyalty 3.1.2 Customer clustering using demographic factors Each cluster is internally clustered according to different demographic variables such as age, gender, location, etc. 109
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME 4. GENERATION OF OPTIMAL INTERNET PLANS The paper [4] assumes that the market is segmented into two customer classes. Class 1 customers also called as ‘Time sensitive customers’ are willing to pay a price premium for a shorter service time, while Class 2 customers also called as ‘Price sensitive customers’ are willing to accept a longer service time in return for a lower price. The Service Provider offers products and services that differ only in their guaranteed service times and prices. The behaviour of the market depends on the specific combination of market parameter values [8]. The market which is more sensitive to price, and the substitution effect in demand is stronger for the guaranteed service time difference such a market is referred to as a PTD market. The market which is more sensitive to service time and the substitution effect in demand is stronger for the price difference such a market is referred to as a TPD market. In a TPD market, the time sensitivity of demand becomes stronger and the price sensitivity of demand in a PTD market becomes stronger. Numerical result shows that the optimal profit in a TPD market is always larger than the optimal profit in a PDT market, meaning that in a time sensitive market, a service provider will create more profit using market segmentation [8]. Firstly develop a model to determine the optimal product price and optimal capacity necessary for maximizing total profit using an Associative mining algorithm. The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree). This method outperforms other popular methods for mining frequent patterns, e.g. Apriori Algorithm. FP-Growth allows frequent item-set discovery without candidate itemset generation. It is a two step approach: Step 1: Build a compact data structure which is built using two passes over the dataset. Step 2: Extracts frequent itemsets directly from the FP-Tree by traversing through FP-Tree [9]. After applying FP-Tree algorithm suggestions would be generated to optimize these plans. These suggestions could be in the form of marginally increasing or decreasing the capacity, price or duration in order to maximize the profit and attract more customers. 5. CONCLUSION Thus the field of computerized decision support is expanding to use new technologies and to create new applications. The Paper is of immense use for inferring qualitative data from quantitative raw data already accumulated by a telecom company. The knowledge thus gained can be utilized to identify the need for support mechanisms such as making of optimal plans and estimation of porting out of customers in order to retain them so as to boost the performance of the company and increase the success percentage. Thus the aim of the system developed is to enable administration of the company, gain vital insights into the prevailing trends and judge the progress of the company and its competency. 6. ACKNOWLEDGEMENT We would like to express our profound gratitude to our guide Ms. Snehal Kulkarni and our Professor Ms. G. Anuradha for their exemplary guidance, constant monitoring, supervision and cooperation as well as for providing necessary information regarding the domain of the topic throughout the course of the research. 110
  • 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME REFERENCES Journal Papers [1] RokRupnik, MatjažKukar, Marko Bajec and MarjanKrisper, DMDSS: Data Mining Based Decision Support System, 28th International Conference on Information Technology Interfaces (ITI), 2006. [2] Ning Lu, Hua Lin, Jie Lu and Guangquan Zhang, A Customer Churn Prediction Model in Telecom Industry Using Boosting, IEEE, 2011. [3] R. qiasi, M. baqeri-Dehnavi, B. Minaei-Bidgoli and G. Amooee, Developing a model for measuring customer’s loyalty and value with RFM technique and clustering algorithms, The Journal of Mathematics and Computer Science, Vol. 4 No. 2 , 172-181, 2012. [4] Ching-Hsue Cheng and You-Shyang Chen, Classifying the segmentation of customer value via RFM model and RS theory, Elsevier, 2009. [5] Adrian Radulescu; Mihai Florin Băcilă; IoanLiviuMărar, RFM based Segmentation: An Analysis of a Telecom Company’s Customers, International Conference “Marketing – from information to decision”, 2012. [6] Morteza Namvar, Mohammad R. Gholamian and SahandKhakAbi, A Two Phase Clustering Method for Intelligent Customer Segmentation, International Conference on Intelligent Systems, Modelling and Simulation, 2010. [7] Ki-sung Hong & Chulung Lee, Integrated pricing and capacity decision for a telecommunication service provider, Springer, 14 March 2012. [8] Boyaci T, Ray S (2003) Product differentiation and capacity selection cost interaction in time and price sensitive markets. Manufacturing and service operations management 5(1):18–36 [9] Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, 8, 53–87, April 21, 2004. [10] Rinal H. Doshi, Dr. Harshad B. Bhadka and Richa Mehta, “Development of Pattern Knowledge Discovery Framework using Clustering Data Mining Algorithm”, International Journal of Computer Engineering & Technology (IJCET), Vol. 4, Iss. 3, 101 - 112, 2013. [11] A.Thirunavukarasu and Dr.S.Uma Maheswra, “Fuzzy Metagraph Based Knowledge Representation of Decision Support System”, International Journal of Computer Engineering & Technology (IJCET), Vol. 3, Iss. 2, 157 - 166, 2012. 111

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