The CRM Strategy of a Korean Duty-Free Shop Based on the RFM Model

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1 The CRM Strategy of a Korean Duty-Free Shop Based on the RFM Model Catholic University of Pusan, syyu@cup.ac.kr Abstract In this study, we present an RFM model to establish a customer segmentation strategy at a Korean duty-free shop. In this shop, customers visiting time and purchase amount are restricted by the government, so that a different purchase pattern from general retail distribution business is exhibited. At the shop, recency demonstrated normal distribution, but visiting frequency and purchase amounts showed a division between a small number of top-class customers and a multitude of bottom-class customers. Here, classes were identified through a normalization process involving the characteristics of visiting frequency and purchase amount among duty-free shop customers. Based on the result, an RFM model was developed, and RFM scores were allocated to customers who had bought goods at the duty-free shop in past five years. Through this process, a customer relationship management strategy was developed. Keywords: RFM Model, Customer Segmentation, Customer Relationship Management 1. Introduction Since an International Monetary Fund (IMF) loan was received in the late 1990s, the duty-free shop industry in Korea has become an increasingly competitive environment. Thus, mass marketing using advertisements and promotion channels such as television, newspapers, magazines, and posters no longer helps to reinforce competitiveness in the duty-free shop industry. Furthermore, since traditional mass marketing is an approach to guide customers to an event or commodity, it cannot lead to customer satisfaction in the true sense. The demand for the duty-free shop industry in Korea has not increased, while the number of dutyfree shop is increasing. As a result, competition among the shops is deepening; at the same time, the desires of customers are more diversified. Thus, the relationship between suppliers and consumers in the distribution market is changed from a supplier-oriented market to a consumer-oriented market. In a consumer-oriented market, traditional one-off and indiscriminate mass marketing strategies cannot satisfy individual consumers desires. Thus, new marketing strategies are needed where businesses can communicate with target customer groups and give individual target customers specialized services resulting from strategic customer segmentation and based on customer data([1-3]). In this study, we analyze the purchasing patterns of customers in a Korean duty-free shop. The customers are categorized into three classes according to their level of contribution. To classify customers, we use the RFM model, which uses the measures of recency, frequency, and monetary amount. Furthermore, we propose a proper CRM (customer relationship management) strategy for the top class of customers([4,5]). The three measures in the RFM model give information on what customers do: Recency means how long it has been since each customer made his or her last purchase; frequency denotes how many times each customer has made a purchase; and monetary amount represents how much each customer has spent in total. These may be the most important characteristics in determining the likely profitability of relationship management to an individual customer, and therefore are used to segment the list of customers. The rest of the paper is organized as follows. In section 2, we give an overview of the CRM and RFM models. In section 3, we present the research methodology for classifying customers based on the RFM model. Section 4 presents CRM strategies for each class of customer. Finally, section 5 concludes the paper. Advances in information Sciences and Service Sciences(AISS) Volume4, Number14, Aug 2012 doi: /AISS.vol4.issue

2 2. Literature review In this section, we review related studies of the customer relationship management, customer value analysis and RFM model. CRM is a philosophy of business operation for acquiring and retaining customers, increasing customer value, loyalty and retention, and implementing customer-centric strategies([6]). CRM, devoted to improve relationships with customer, focuses on a comprehensive picture on how to integrate customer value, requirements, expectations and behaviors via analyzing data from transaction of customer([7]). Enterprises can shorten sales cycle and increase customer loyalty to build better close relationships with customers and further add revenues by good CRM. Thus, an excellent CRM can help enterprises keeping existing customers and attracting new ones([8-10]). Customer value analysis is a kind of analytic method for discovering customers characteristics and makes a further analysis of specific customers to abstract useful knowledge from large data[11]. Thus, it is clear that enterprises apply value analysis method to customers for knowing about who are the target customers which contribution is outstanding. Kaymak[12] further pointed out that the RFM model is one of the well-known customer value analysis methods. Its advantage is to extract characteristics of customers by using fewer criterions (a three-dimension) as cluster attributes so that reduce the complexity of model of customer value analysis. In this study, different groups of customers are segmented using their consuming behavior via RFM attributes. By this way, we ensure that the standards which cluster customer value are not established subjectively, so that the clustering standards are established objectively based on RFM attributes. That is, customer segmentation is established only by three attributes (recency frequency monetary of purchase). Therefore, this study uses RFM model (fewer attributes) of customer value analysis as cluster attributes, and then improve the close relationships of enterprises with customers. The RFM model is a model that differentiates important customers from large data by three variables; interval of customer consumption, frequency and money amount. The detail definitions of RFM model are described as follows [13]: Recency of the last purchase (R) represents recency, which refers to the interval between the time that the latest consuming behavior happens and present. The shorter the interval is, the bigger R is. Frequency of the purchases (F) represents frequency, which refers to the number of transactions in a particular period, for example, two times of one year, two times of one quarter or two times of one month. The many the frequency is, the bigger F is. Monetary value of the purchases (M) represents monetary, which refers to consumption money amount in a particular period. The much the monetary is, the bigger M is. According to the literature, researches showed that the bigger the value of R and F is, the more likely the corresponding customers are to produce a new trade with enterprises[14]. Moreover, the bigger M is, the more likely the corresponding customers are to buy products or services with enterprises again. RFM method is very effective attributes for customer segmentation[15]. Although RFM model is a good method that differentiates important customers from large data by three variables, there are two studies, having some different opinions with respect to the three variables of RFM model[16,17]. Hughes[16] considered that the three variables are equal in the importance; therefore, the weights of the three variables are identical. On the other hand, Stone[17] indicated that the three variables are different in the importance due to the characteristic of industry. Thus, the weights of the three variables are not equal. 3. Customer segmentation using the RFM model 3.1. The construction of customer data In this study, purchase data from about 230,000 customers who visited the shop at least once during the past five years were gathered. Among these patrons, 91,165 customers were Korean, and 146,587 customers were foreign. The total sales volume was about $241 million over the five years; the sales 417

3 ratio of local residents was 43% and that of foreigners was 57%. Sales per customer in terms of local residents and foreigners were $1,144 and $938, respectively. Furthermore, about 50% of local customers and 8% of foreign ones were registered as a member of the shop. Figure 1 shows the distribution of customer data RFM scores The duty-free shop stores different types of customer data such as time, age, region, and sex; such data help to elucidate overall sales trends. However, these simple statistical data are limited when it comes to utilizing them as marketing data according to level of contribution of each customer. Thus, we segmented the customers on the basis of their contribution to the shop using RFM analysis. In the RFM model, first, in case of the R-score, we gave a high score to customers who visited recently and a low score to those who visited a long time ago. In a Korean duty-free shop, local residents may buy products at the duty-free shop if they are leaving the country. Thus, we gave 5 points to customers who visited within the past year, 3 points to those visiting in the past 2 or 3 years, and 1 point to the others. The F-score - it represents customers visiting frequency - was given on the basis of the number of visits. If the frequency was greater than five times, we gave 5 points; for three or four times, we gave customers 3 points, and otherwise we gave them 1 point. Because the Korean government only allows customers leaving the country to visit duty-free shops, it is suitable to give the highest score to customers who visit more than once per year. In the case of local residents, the purchase amount per visit is restricted to $3,000. Thus, the M- score, which represents monetary amount, was 5 points the highest possible score for customers who purchased more than $3,000 worth of products over the past five years. For a purchase amount of more than $2,000 and less than $3,000, we gave 4 points, and if the purchase amount was more than $1,000 and less than $2,000, we gave 3 points. Otherwise, we gave the customer 1 point. We computed the RFM score using the weighted average of recency (R-score), frequency (F-score), and monetary amount (M-score). Considering the nature of Korean duty-free shops, which restricts the visiting frequency and purchasing amount of local residents, it is valid to assign higher weights to the F-score and M-score. After some trial and error, we assigned the weights of the R-score, F-score, and M-score as 0.1, 0.4, and 0.5 respectively. Thus, the RFM score is calculated as follows: RFM score = 0.1 x R-score x F-score x M-score After computing the RFM score for all 230,000 customers, we classified the customers according their score. As a result, we defined the top 6 % of customers (RFM score over 4 points) as class A, the middle 16.5 % of customers (RFM score of 2 to 4 points) as class B, and the rest the bottom 77.5 % of customers (RFM score under 2 points) as class C. Figure 2 shows the results of customer classification on the basis of RFM score. 418

4 Figure 1. Construction of customers Figure 2. Sales contribution of each class Customer Segmentation Figure 2 shows the sales contribution for each customer class. From this figure, it is clear thatt the sales portion of customers included in class A (6 %) is 48.6 % of all sales amounts. Thus, we should target the marketing strategy to customers in class A, corresponding to the top 6 % of customers. Furthermore, due to the legal restrictions on purchase amount per visit and visiting time in Korea, we need to analyze customers according to their nationality, specifically whether they are local residents or foreigners. We divided class A customers into active and inactive customers. Figure 3 shows the results when class A customers are classified according to nationality and level of activity. Figure 3. Classification of customers in class A 419

5 4. CRM strategy In this section, we propose CRM strategies for Korean and foreign customers, respectively. 4.1 Strategies for Korean customers The number of local resident customers in class A is about 10,000, it corresponding to 11 % of all local customers. Among these, 18 % are customers without membership. This means that even non- customers are 36 % and 64 %, respectively, and the same proportions are seen among local customers. In the case of sales, the proportion of purchases by local customers in class A is 46 %. Furthermore, over the last five years, customers in class A represent about $5,000 each in sales; this is about five member customers may display a high degree of loyalty. The proportions of male and female times the amount in sales per customer for all local customers. It is particularly worthy of note that sales to men in their 30s and 40s are increasing. The proportion of inactive customers, those who have not visited the shop within the last two years, is 17 %. However, the purchasing amount per visit of active customerss is $509, while the purchasing amount per visit of inactive customers is $659. This means that customers a low frequency of contribution remain customers even if they do not shop there frequently. Furthermore, 25 % of inactive customers visited the shop more than 10 times. This means that visiting frequency and purchasing amount per visit are not different between active and inactive customers. However, the proportion of inactive customers without membership is 41 %; on the other hand, the proportion of active customers without membership is 12 %. Thus, we can see that there is a large difference between the two. From this result, we conclude that it is necessary to register customers as members. Next, we willl look at the attributes of local active customers in class A. Figure 4 shows the classification of local active customers in class A into four groups according to their purchasing amount. In the case of group 1, sales per customer are about $19,000, while sales per customer in group 4 are $1,500. Visiting frequency is 27 and 6 visits for group 1 and group 4, respectively; there are large differences among the groups. Thus, it is necessary to augment the contributions of customers in class A by increasing their visiting frequency. Figure 4. Classification of local active customers in class A 4.2 Strategies for foreign customers The total number of foreign customers in class A is about 4,000, it corresponding to 3 % of all foreign customers. Unlike the case of local residents, the proportion of foreign customers without membership in class A is 42 %. This value is a very large portion compared with local customers, and shows that membership management for foreign customers is insufficient. The proportions of foreign male and female customers in class A are 60 % and 40 %, respectively. On the other hand, in the case of all foreign customers, the proportions are 45 % and 55 %, respectively 420

6 The proportion of sales to foreign customers in classs A is 50 %. Furthermore, sales per foreign customer in class A represent about $17,000,, about 3.4 times the sales per local customer in class A. The portion of the foreign sleeping customers in class is 25 %. The visiting frequency of these sleeping customers is 17, but, as a result they leaved the shop. The reason for leaving is that the portion of sleeping customers without membership is 55 %. It shows that even though the customer has high contribution, if the shop cannot maintain the relationship to the customer, the customer breaks away from the company. The proportion of the inactive foreign customers in class A is 25 %, and the visiting frequency of these customers is 17. The reason for leaving is that the proportion of inactive customers without membership is 555 %. This shows that even if a customer makes a high contribution, if the shop cannot maintain a relationship with the customer, the customer will not be loyall to the company. Figure 5 shows the result of classifying the active foreign customers in class A. In the case of group 1, sales per customer represent about $50,,000; on the other hand, sales per customer in group 4 represent $1,500. Additionally, visiting frequency and sales per visit in group 1 are also very large compared to lower groups. Figure 5. Classification of foreign active customerss in class A 5. Conclusions In this study, we proposed an RFM model to establish a customer segmentation strategy at a Korean duty-free shop. In this shop, customers visiting time and purchase amount are restricted by the government, so that a different purchase pattern from general retail distribution business is exhibited. At the shop, recency demonstrated normal distribution, but visiting frequency and purchase amounts showed a division between a small number of top-class customers and a multitude of bottom-class customers. Here, classes were identified through a normalization process involving the characteristics of visiting frequency and purchase amount among duty-free shop customers. Based on the result, an RFM model was developed, and RFM scoress were allocated to customers who had bought goods at the duty-free shop in past five years. Through this process, a customer segmentation strategy was developed. 6. References [1] [2] [3] Sunil Gupta, Donald Lehman, Customers as Assets, Journal of Interactive Marketing, vol.17, no.1, pp.9-24, Roland Rust, Katherine Lemon, Valarie A. Zeithaml, Return on Marketing: Using Customer Equity to Focus Marketing Strategy, Journal of Marketing. vol.68, no.1, pp , Beomsoo Shim, Keunho Choi, Yongmoo Suh, CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns, Expert systems with applications, vol.39, no.9, pp ,

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