CHAPTER 4 A FRAMEWORK FOR CUSTOMER LIFETIME VALUE USING DATA MINING TECHNIQUES

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1 49 CHAPTER 4 A FRAMEWORK FOR CUSTOMER LIFETIME VALUE USING DATA MINING TECHNIQUES 4.1 INTRODUCTION Different groups of customers prefer some special products. Customers type recognition is one of the main aims of each business and firms know customer preferences, Liu and Liou (2011). Firms should try to find customer groups according to customer behavior. Maintaining positive relationships with customers, increasing customer loyalty, and expanding customer lifetime value are the basis of Customer Relationship Management (CRM) approach, Ahn et al. (2003). Customer loyalty is a perfect feature for segmenting customers. Customers past purchasing behavior shows their loyalty. The customer is loyal, if he/she purchases more at his/her lifetime, buys products recently and spending more money during the lifetime. But if a customer doesn t purchase recently, total number of his/her purchasing is low and spent money is low, he/she is disloyal customer. The value of a customer changes during their established relationship with an organization. Many organizations do not know which of their customers they should focus on. It is necessary to know how much it costs to serve existing customers and how much profit they bring to the organization. CLV is the key interest to enable organizations to proceed with the development of strategies. RFM model is one of CLV models that have been used for customer loyalty in a lot of studies is useful model. The parameters need for constructing model is available. RFM is a behavioral model and consider all past customer purchases to prospect the future customer behaviors. With using CLV models for customer loyalty and data mining techniques for customer segmentation according CLV, we can recommend right products to right customers and provide individual marketing decision for each customer. The customers, who receive products according to their requirements, will be satisfied and purchase more at time and finally they will be loyal customers. On the other hand, firms spend low cost for customer retention and reach profitability. Customers of the company should be loyal. They have to buy the products or services of the company repeatedly. The core parts of CRM activities are to understand customers profitability and retain profitable customers. To cultivate the full profit potentials of

2 50 customers, many companies already try to measure and use customer value in their management activities. The most critical factors that determine a company s success or failure are evaluating customer s CLV and retaining the most valuable customers. The definition of CLV is the net present value (NPV) of the future profit that can be created at a particular duration, Zikmund et al. (2003). CLV is used to identify profitable customers and to develop good strategies to target customers. Figure 4.1 shows the conceptual framework for customer lifetime value. Transaction Data Recency Frequency Monetary Term Customer Loyalty Customer Segmentation Fig.4.1: Conceptual Framework for Customer Lifetime Value 4.2 CONCEPTS OF CUSTOMER LIFETIME VALUE Customers are more valuable than others. Long-lifetime customers are more profitable to an organization. But, it is very difficult to distinguish the more profitable than less profitable. CLV is the net present value of the profit an organization expects to realize from a customer for the duration of their relationship. Customer lifetime value focuses on customers as assets rather than sources of revenue; the volume of purchases made, customer retention rates, and profit margins are factors taken into account in calculations. Strategies for increasing customer lifetime value aim to improve customer retention and lengthen the life of the relationship with the customer. It is a key factor in the customer equity of an organization.

3 51 Since not all customers are financially attractive to the organization, it is essential that their profitability be determined and the resources be allocated according to the customer s lifetime value. By knowing the current value of the customer, the customers are segmented into different categories. Segmenting is used to concentrate more on the profitable customers. Once customers are segmented based on profitability, company can tailor offerings to various segments. Today, companies around the world are increasingly segmenting their customers in order to increase profitability. Segmenting helps companies to tailor their offerings to each of the segment. One of the common ways of segmentation is based on loyalty and profitability. Figure 4.2 shows about customer segmentation. Fig.4.2: Customer Segmentation After segmenting customers, companies tailor their offerings and strategies to convert existing customers to become more loyal and more profitable. Segmenting helps companies to allocate their marketing resources based on the customer value. Customer lifetime value gives a formalized depiction of a long-term view of the customers and gives a better picture of what the company is going after. By definition, customer lifetime value is the present value of the future profits. To increase customer lifetime value, one has to increase the profits generated from that customer. It is noted that highly satisfied customers often recommend it to their friends,

4 52 relatives and others. This recommendation results in new customers and referral sales. The cost of acquiring new customers by referrals is substantially lower than traditional methods. In the long run, high customer satisfaction results in lower customer acquisition costs and higher margins, thus increasing customer lifetime value. 4.3 RECENCY, FREQUENCY AND MONETARY (RFM) MODEL RFM measurement is an important method for assessing customer lifetime value. Recently, a Weighted Rececncy, Frequency and Monetary (WRFM) based method has been proposed to provide recommendations based on customer lifetime value, including recency, frequency and monetary. To identify customer behavior, the well-known method called recency, frequency and monetary. RFM is a compound metric designed to create a single weighted score that reflects the engagement level and value of customer purchase behavior. It considers time since last purchase; how often purchases have occurred in the last X periods and the value of purchases in combination to achieve a metric reflective of a customer's purchasing intensity. RFM model is used to represent customer behavior characteristics. (1) R (Recency): the period since the last purchase; a lower value corresponds to a higher probability of the customer s making a repeat purchase; (2) F (Frequency): number of purchases made within a certain period; higher frequency indicates greater loyalty; (3) M (Monetary): the money spent during a certain period; a higher value indicates that the company should focus more on that customer. RFM method avoids focusing on less profitable customers, allowing resources to be diverted to more profitable customers. Different weights should be assigned to RFM variables depending on the characteristics of the industry. 4.4 RECENCY, FREQUENCY, MONETARY AND TERM (RFMT) MODEL RFMT measurement is the proposed method for assessing customer lifetime value. (1) R (Recency): The period since the last purchase; a lower value corresponds to a higher probability of the customers making a repeat purchase. (2) F (Frequency): Number of purchases made within a certain period; higher frequency indicates greater loyalty. (3) M (Monetary): Money spent during a certain period; a higher value indicates that the company should focus more on that customer.

5 53 (4) T (Term): Number of days from the first purchase date to the last visits date. Different weights should be assigned to RFMT variables. T is used to identify the long-term customers. 4.5 ANALYTICAL HIERARCHY PROCESS (AHP) Analytical Hierarchy Process was a multi-goal decision making method, by professor of Pittsburgh University (Thomas L.Satty) in Utilizing institutional framework to establish and influence mutual hierarchical structures of the relation, enables us to make valid decisions on complicated problems, make valid decisions under the uncertain risks, or seek consistency in the diverse judgment. The AHP has been used to determine the relative importance (weights) of the RFMT variables W R, W F, W M and W T. The three main steps of the AHP are: (1) Perform pair wise comparisons. (2) Assess the consistency of pair wise judgments. (3) Computing the relative weights PERFORM PAIR WISE COMPARISONS This asks evaluators (decision makers) to make pair wise comparisons of the relative importance of RFM variables using the scale as shown in Table 4.1, Liu and Shih (2004) ASSESS THE CONSISTENCY OF PAIR WISE JUDGMENTS Evaluators will make inconsistent judgments when making pair wise comparisons. Before the weights are computed, the degree of inconsistency is measured by an inconsistency index value. Perfect consistency implies a zero inconsistency index value. However, perfect consistency is not often achieved, since people are often biased and inconsistent, when making subjective judgments. Therefore, an inconsistency index value of less than 0.1 is acceptable. If the inconsistency index value exceeds this, then the pair wise judgments may be revised before the weights of RFM are computed.

6 54 Table 4.1: Relative Degree of Importance for Pair wise Comparisons Comparative importance Description Explanation 1 Equally importance Two activities contribute equally to the objective. 2 Intermediate between Experience and judgment slightly favor one equal and weak activity over another. 3 Weak importance of one over another Experience and judgment slightly favor one activity over another. 4 Intermediate between weak and strong Experience and judgment strongly favor one activity over another. 5 Essential or strong importance Experience and judgment strongly favor one activity over another. 6 Intermediate between strong and demonstrated An activity is strongly favored and its dominance is demonstrated in practice. 7 Demonstrated importance An activity is strongly favored and its dominance is demonstrated in practice. 8 Intermediate between demonstrated and absolute The evidence favoring one activity over another is of the highest possible order of affirmation. 9 Absolute or extreme importance The evidence favoring one activity over another is of the highest possible order of affirmation.

7 COMPUTING THE RELATIVE WEIGHTS This determines the weight of each decision elements. This work employs eigenvalue computations to derive the weights of the RFM. The AHP questionnaire for RFM is shown in table 4.2. The RFM pair wise comparison matrix is shown in table 4.3. According to the assessments, the relative weights of the RFM variables were computed. Thus the weights of the three variables are not equal, Ching-Hsue Cheng and You-Shyang Chen (2009). According to assessments, the relative weights of the RFM variables are , and The implication of the RFM weightings is that recent is the most important variable; so evaluators must mainly concentrate on regularity of customer purchasing. If some perform no transactions for a long period, they may have been lost or transferred to new vendors. Criteria Table 4.2: AHP Questionnaire for RFM Comparative Importance Criteria 9:1 7:1 5:1 3:1 1:1 3:1 5:1 7:1 9:1 Recency Frequency Recency Monetary Frequency Monetary Table 4.3: RFM Pair wise Comparison Matrix Recency Frequency Monetary Recency Frequency 1/3 1 5 Monetary 1/7 1/5 1 The AHP questionnaire for RFMT is shown in table 4.4. The RFMT pair wise comparison matrix is shown in table 4.5. According to the assessments, the relative weights of the RFMT variables are computed. Thus the weights of the four variables are not equal. According to assessments, the relative weights of the RFMT variables are 0.626, 0.236, and The implication of the RFMT weightings is that recent is the most important variable; so evaluators must mainly concentrate on regularity of customer purchasing. If

8 56 some perform no transactions for a long period, they may have been lost or transferred to new vendors. T is considered as long-term or short-term customers. The regular customers can be easily identified. The new customers can also be predicted. Criteria Table 4.4: AHP Questionnaire for RFMT Comparative Importance 9:1 7:1 5:1 3:1 1:1 3:1 5:1 7:1 9:1 Criteria Recency Frequency Recency Monetary Recency Term Frequency Monetary Frequency Term Monetary Term Table 4.5: RFMT Pair wise Comparison Matrix Recency Frequency Monetary Term Recency Frequency 1/ Monetary 1/7 1/5 1 3 Term 1/9 1/7 1/ FRAMEWORK USING DATA MINING TECHNIQUES Data Mining is a knowledge discovery process of extracting previously unknown, actionable information from databases. It is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is the search from relationships and global patterns that exist in databases, but are hidden among the vast amounts of data. These relationships represent valuable knowledge about the database and objects in the world. It is a multi-disciplinary research and application area that aims to discover novel and useful knowledge from vast databases, using methods ranging from artificial intelligence, statistics, and databases. The focus of these techniques is the discovery of unknown but useful knowledge that is hidden within such vast data.

9 57 Data mining explores information or knowledge from the patterns. Modern enterprises often collect a large number of patterns, including important information such as the market, customer, supplier, rival, and trends for the future. With data mining, one can successfully navigate through the complex and comprehensive data to find useful details to make decisions, and enhance enterprise competition advantageously K-MEDOIDS ALGORITHM Clustering is the process of grouping a set of physical or abstract objects into groups of similar objects. 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 k-medoids algorithm is a clustering algorithm related to the k-means algorithm and the medoid shift algorithm. Both the k-means and k-medoids algorithms are partitional (breaking the dataset up into groups) and both attempt to minimize squared error, the distance between points labeled to be in a cluster and a point designated as the center of that cluster. In contrast to the k-means algorithm, k-medoids chooses data points as centers (medoids or exemplars). The k-medoid is a classical partitioning technique of clustering that clusters the data set of n objects into k clusters known a priori. It is more robust to noise and outliers as compared to k-means because it minimizes a sum of dissimilarities instead of a sum of squared Euclidean distances. A medoid can be defined as the object of a cluster, whose average dissimilarity to all the objects in the cluster is minimal i.e. it is a most centrally located point in the given data set. The most common realization of k-medoid clustering is the Partitioning Around Medoids (PAM) algorithm and is as follows: 1. Initialize: randomly select k of the n data points as the medoids. 2. Associate each data point to the closest medoid. ("closest" here is defined using any valid distance metric, most commonly Euclidean distance, Manhattan distance or Minkowski distance). 3. For each medoid m 1. For each non-medoid data point o 2. Swap m and o and compute the total cost of the configuration 4. Select the configuration with the lowest cost.

10 58 5. Repeat steps 2 to 5 until there is no change in the medoid. Customers with similar customer lifetime values in terms of RFMT were clustered using k-medoids method. The normalized RFMT values of each customer were then multiplied by the relative importance of RFMT variable, W R, W F, W M and W T, which were determined by the AHP. The K-medoids method has been applied to cluster the customers into eight groups, according to the weighted RFMT values. 4.7 ANALYSIS RFMT analytic approach is used to evaluate customer's loyalty and the contribution in the field of marketing management, and is used to assess customer's lifetime value. It is a method to identify high-response customers in marketing promotions, and to improve overall response rates, which is well known and is widely applied today ASSESS CUSTOMER LIFETIME VALUE The customer exhibiting high RFM and RFMT score should normally conduct more transactions and result in higher profit. The operational definition of RFMT is shown in Table 4.6. After getting the relative weight of RFM and the RFM of an individual customer, a consumers lifetime value can be calculated by the following steps: Step1: Standardize each customer's RFM value Because each of the units of RFM is different, standardization is necessary. The standardization formulas are shown in Table 4.7. Step2: Calculate each customer's CLV Multiply each customer s RFM value times its weight to get an integrative CLV score, and show as formula. C x = W R X R + W F X F + W M X M

11 59 Table 4.6: RFMT definition Construct Means Defines Units R (Recency) The latest data one purchased The total days between the day of the latest purchase and analysis days F The number of Consuming frequency times (Frequency) purchases made within a certain period M The money spent Amount of money of dollars (Monetary) during a certain period total consuming T The term from first The number of days days (Term) visit to last visit between first purchase date and recent visit Table 4.7: Standardized Formulas for RFM Construct Formula R x = (x L x)/( x L x S ) F x = (x x S )/( x L x S ) M x = (x x S )/( x L x S ) where x and x represent standardized and primitive RFM value respectively. x L and x S represent the largest and smallest of the R, F or M value of customers group respectively. R value formula for shoulder to relation, x value little x that change value loud, in the same pace with F, M value. C x = customer CLV X R = customer's standardized R value X F = customer's standardized F value

12 60 X M = customer's standardized M value Step3: Calculate the CLV of each cluster The k-medoid method was then applied to cluster the customers into many groups, according to the weighted RFM values. The customers standardized values of each cluster were added together, and then divided by the number of customers of each cluster to get the average values. Then, a CLV value can be derived after multiplying the weights as shown in the formula below. C j I = W R C j R + W F C j F + W M C j M where C j I = CLV of cluster j C j R=Average of standardized R of cluster j C j F =Average of standardized F of cluster j C j M =Average of standardized M of cluster j j = 1 to n cluster. The trained data were stored in transaction database. The K-medoids method was applied to cluster the customers into eight groups, according to the weighted RFM values. Each cluster represents market segmentation. Table 4.8 lists the clusters generated by k- medoids according to RFM pattern. Table 4.8: Clusters generated by k-medoids According to RFM Pattern Cluster Customers Recency Frequency Monetary RFM Pattern Loyalty R F M R F M R F M R F M R F M R F M R F M R F M 8 Total

13 61 Customers in clusters with the pattern R have been considered to be loyal, purchased recently, purchase frequently, and spend regularly. They are golden customers. Clusters with the pattern R may include new customers who have only recently visited the company. These customers will try to develop closer relationships with the company. These customers may become golden customers. Finally, clusters with the pattern R include those who very rarely visited the site and made very few transactions. They are valueless customers, and may only make purchases during sales. Cluster 2 displays the pattern R. Consequently, these customers can be considered to be most loyal customers who frequently visit and make large purchase. They are golden customers. Cluster 3 displays the pattern of R. These customers can be considered to be loyal customers. Cluster 1 displays the pattern of R and may represent new customers who have recently visited the company to make purchase. These customers will try to develop closer relationships with the company. Cluster 4, 5, 7 and 8 show the pattern of R, and are likely to be the clusters with least loyalty. Cluster 6 displays the pattern of R and they are likely to move from the firm. Table 4.9 lists the CLV ranking by weighted sum of normalized RFM values. Table 4.9: CLV Ranking by Weighted Sum of Normalized RFM Values Cluster Recency Frequency Monetary Integrated Rating (CLV) CLV Rank Average C j I = W R C j R + W F C j F + W M C j M (where W R = , W F = and W M = )

14 CUSTOMER LIFETIME VALUE USING RFMT MODEL The AHP can be used to determine the relative importance (weights) of the RFMT variables, W R, W F, W M and W T. After getting the relative weight of RFMT and the RFMT of an individual customer, a consumer s lifetime value can be calculated by the following steps: Step1: Standardize each customer's RFMT value Because each of the units of RFMT is different, standardization is necessary. The standardization formulas are shown in Table Step2: Calculate each customer's CLV Multiply each customer s RFMT value times its weight to get an integrative CLV score, and show as formula C x = W R X R + W F X F + W M X M + W T X T Table 4.10: Standardized formulas for RFMT Construct Formula R x = (x L x)/( x L x S ) F x = (x x S )/( x L x S ) M x = (x x S )/( x L x S ) T x = (x x S )/( x L x S ) where x and x represent standardized and primitive RFM value respectively. x L and x S represent the largest and smallest of the R, F, M and T value of customers group respectively. R value formula for shoulder to relation, x value little x that change value loud, in the same pace with F, M and T value. C x = customer CLV X R = customer's standardized R value X F = customer's standardized F value X M = customer's standardized M value X T = customer's standardized T value

15 63 Step3 Calculate the CLV of each cluster The K-medoid method has been applied to cluster the customers into many groups, according to the weighted RFMT values. The customers standardized values of each cluster were added together, and then divided by the number of customers of each cluster to get the average values. CLV for all clusters have been derived after multiplying the weights as shown in the formula given below. C j I = W R C j R + W F C j F + W M C j M + W T C j T where C j I = CLV of cluster j C j R=Average of standardized R of cluster j C j F =Average of standardized F of cluster j C j M =Average of standardized M of cluster j C j T =Average of standardized T of cluster j j = 1 to n cluster. The trained data were stored in transaction database. The K-medoids method was then applied to cluster the customers into eight groups, according to the weighted RFMT values. Each cluster represents market segmentation. Table 4.11 lists the clusters generated by k- medoids. Customers in clusters with the pattern R are considered to be loyal, purchased recently, purchase frequently, spend regularly and also long-term customers of the company. They are golden customers. Clusters with the pattern R M may include new customers who have only recently visited the company. These customers will try to develop closer relationships with the company. These customers may become golden customers. Finally, clusters with the pattern R include those who very rarely visited and made very few transactions. They are valueless customers, and may only make purchases during sales. Enterprises reduce prices to attract such customers. R include those are longterm customers but they made very few transactions. Company has to retain those customers. Cluster 2 has the pattern R. Consequently, these customers can be considered to be most loyal customers who frequently visit and make large purchase. Cluster 3 has the pattern R. They are loyal customers. Cluster 1 has the pattern of and may represent new customers who have recently visited the company to make purchase. They may become golden customers. The company needs to attract those new customers. Cluster 6 has the pattern of R and represented as long-term customers but with least profit. They must be attracted by the company. Cluster 4, 5, and 7

16 64 and 8 have the pattern of R, and are likely to be the clusters with least loyalty. Such customers rarely visit and make transactions. Table 4.11 Clusters generated by k-medoids According to RFMT Pattern Cluster Customers Recency Frequency Monetary Term RFMT Pattern R F M T R F M T R F M T R F M T R F M T R F M T R F M T R F M T Total Table 4.12: CLV Ranking by Weighted Sum of Normalized RFMT Values Cluster Recency Frequency Monetary Term Integrated Rating (CLV) CLV Rank Average

17 65 C j I = W R C j R + W F C j F + W M C j M + W T C j T (where W R = 0.626, W F = 0.236, W M = and W T= 0.056) CLV has been changed according to term value. Long-term customers are valuable customers and they have to be attracted more. Customers of Clusters 2, 3, and 6 are long-term customers. Term is a good indicator in customer lifetime value. Long-term and short-term customers have been identified efficiently. The accuracy of CLV calculated by RFMT model is better than RFM model UTILIZE TARGET MARKETING TO IMPROVE CLV After checking the measurement method of CLV for accuracy, customers based on CLV can be divided by RFMT value to identify the customers with higher CLV value, and calculate the limited marketing resources to optimize the application. RFMT analysis is used to evaluate customer s CLV and loyalty, and therefore identify the target customers with high CLV by clustering analysis. 4.8 SUMMARY Based on the result of clustering analysis, the numbers of target customers with high loyalty, high interest, and high amount of purchase have been identified. Clustering customers into different groups not only improves the quality of recommendation but also helps decision-makers identify market segments more clearly and thus develop more effective strategies.