# A Hybrid Model of Data Mining and MCDM Methods for Estimating Customer Lifetime Value. Malaysia

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2 (MCDM) methods has been used by various researchers [9, 10]. Fuzzy AHP (FAHP) has been applied in this research in order to solve the problem of vagueness and ambiguity of decision makers udgments. In this paper, K-means clustering analysis was applied with the integration of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and FAHP methods to customer segmentation and ranking. The proficiency of this hybrid methodology was then illustrated by conducting a case study in cosmetics industry. 2. Preliminaries 2.1 K-means Clustering Algorithm K-means algorithm was developed by MacQueen [11] which aims to find the cluster centers, (c 1,, c K ), in order to minimize the sum of the squared distances (Distortion, D) of each data point (x i ) to its nearest cluster centre (c k ), as shown in Equation below where d is some distance function. Typically, d is chosen as the Euclidean distance. The steps of K-means algorithm are shown as follows: (1) Initialize K centre locations (c 1,, c K ). (2) Assign each x i to its nearest cluster centre c k. (3) Update each cluster centre ck as the mean of all x i that have been assigned as closest to it. 2 (4) Calculate min., (5) If the value of D has converged, then return (c 1,, c K ); else go to Step TOPSIS In this work, TOPSIS as a MCDM technique has been utilized to rank the clusters. TOPSIS was proposed by Hwang and Yoon [12]. The steps of TOPSIS are presented as follows: 1) Determining of criteria (RFM), using FAHP. 2) Establishing the data matrix which shows the final center of each cluster. 3) Normalization, 4) Establishing V matrix which is weighted matrix:, 1,...,m; 1;...; n 5) positive ideal point v and negative ideal point v for = 1,2,..., n 6) Calculating the distance from point v i to positive ideal point v and negative ideal point v for = 1,2,..., n as follows: n n + 2 1/2 2 1/2 i = [ ( i ) ] di = [ ( vi v ) ] = 1 = 1 d v v 7) calculate the relative closeness to the ideal solution. 3. Methodology 81

3 The methodology used in this study combines two approaches to estimate, and evaluate CLV. These approaches are Clustering method and MCDM method. The concept of this methodology is that customers with similar values in RFM variables can be segmented based on their CLV. The methodology is illustrated in Figure 1. The proposed methodology in this study includes three phases as stated below: Phase1- Data understanding and collection Phase2- Data preprocessing Phase3 - Modeling and data analyzing and strategies development These phases are explained in detail as follows: - Phase 1- Data were collected from the company data base. RFM scores of customers were considered as basic data of this research. - Phase 2- Data preparation and data normalization were included in data preprocessing phase. In data preparation step, some noisy and uncompleted data have been omitted. Next, a normalization process is required to put the fields into comparable scales. This process is due to different scales of RFM inputs. In this paper, min-max approach was used which recalled all record values in the range between 0 to1. - Phase 3- Weights of RFM variables were calculated by FAHP and the normalized data of RFM have been weighted by these weights. Then, based on weighted RFM values customers were clustered by K- means clustering. Finally, the clusters of customers were ranked using TOPSIS method. Then the strategies for resource allocation have been developed based on the clusters ranks. Phase1- Data understanding Data collection (RFM values and collection of customers) Phase2- Data preprocessing Data preparation Data normalization Phase3- Modeling and data analyzing and strategies development Weighted RFM (FAHP method) Customer clustering based on Weighted RFM Ranking customers clusters and strategies development Figure 1. Proposed method 4. Case Study 82

4 In order to demonstrate the proposed method, a case study has been conducted in a cosmetics commercial company which is located in Iran. Board of directors of the case company had a plan to segment customers based on their CLV. Then, they wanted to recognize the high value segment in order to allocate their marketing and advertising resources to their customers and cluster their customers. According to the methodology, this study has been conducted as follows and the results of the study have been tabulated. Phase1 - Data understanding and collection Based on RFM scores of customers, 1132 customer records have been collected from the historical records of the company. RFM scores of customers are shown in Table 1. Table 1. RFM scores of customers Customer No. Recency Frequency Monetary Phase2 - Data preprocessing In this phase, a normalization process has been applied in order to convert data in similar scale ranging from zero to one (0 to1). The normalized data are shown in Table 2. Table 2. RFM normalized data Customer No. Recency Frequency Monetary Phase3 - Modeling and data analyzing In this phase, firstly, RFM variables were weighted by FAHP. Chang s FAHP [13] has been used in order to weight the variables. Three experts were asked to make pairwise comparison. Using pairwise comparison, FAHP has been calculated the weights of RFM variables. Table 3 shows the results of FAHP. Table 3. RFM variables weights Recency Frequency Monetary Weight

5 Then, as shown in Table 4, normalized RFM scores of customers were multiplied in their corresponded weights. Next, weighted data were clustered by K-means clustering algorithm. SPSS 11.5 software has been utilized to perform K-means clustering. Customers were divided into five from the best to worst clusters and the final centers of clusters were calculated. The clustering results are shown in Table 5. Finally, TOPSIS method has been used to rank clusters based on their RFM final center which is shown in Table 6. Table 4. RFM weighted normalized data Customer no Recency Frequency Monetary Table 5. Clustering results Final Cluster Centers Cluster no. Recency Monetary Frequency Number of customers Table 6. Results of TOPSIS Cluster CLi Rank Strategies development The company has three communication channels which are categorized based on their implementation costs from high to low that are stated as follows, respectively: 1. Verbal communications (Face-to-face, telephone) 2. Written communications (catalogues, proposals, s, letters, and training manuals) 3. External communications (Media advertisement and web pages). According to ranking of the clusters shown in Table 6, board of directors of the company can decide better about allocating each of this communication channels to the ranked clusters. Consequently, some strategic decisions can be made regarding to attain new customers, maintain current customers, and increase the satisfaction of high value customers. 84

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