مدل امتیازدهی رفتار برای برنامه های وفاداری ائتلاف با استفاده از متغیرهای خلاصه از داده معامله
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|9510||2013||7 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||9 روز بعد از پرداخت||487,980 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||5 روز بعد از پرداخت||975,960 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 5, April 2013, Pages 1564–1570
OKCashbag (OCB), the largest coalition loyalty program in Korea, offers a number of benefits such as sharing customer data with participating firms and cross-selling. There is great value in utilizing information pertaining to coalition loyal patrons. However, the size of transaction data is huge. We propose how to create necessary summary information by reducing the dimension of coalition transaction data. This information is then utilized to develop a behavior-scoring model. We expect that our study results can contribute to big data analysis for coalition loyalty program.
OKCashbag (OCB) is the largest coalition loyalty program in Korea. OCB covers assorted business areas such as fuel retailing, telecommunications, brokering, banking, comprehensive retailing, restaurants, online retailing and entertainment including movie, to name a few. From a company perspective, this type of program offers a number of benefits over single-loyalty programs. First of all, marketing, operational and infrastructure costs can be shared (Capizzi and Ferguson, 2005 and Nath, 2005). Second, it creates greater potential for cross-selling (Ferguson and Hlavinka, 2006). Third, coalition loyalty programs can complement the strengths and weaknesses of participating companies (Berman, 2006). Thanks to such benefits, coalition loyalty programs have become very important in the 21st century (Capizzi & Ferguson, 2005). To maximize the benefits of such programs, it is important to utilize information pertaining to loyal patrons who have histories of frequent purchases in various participating firms. Insights derived from demographic information and the purchasing behavior can help us gain a better understanding of whether a coalition loyalty program is doing what it is supposed to do. Loyal patrons are generally defined by the recency of their last purchase, the frequency of purchases within a given time period and the monetary values of all purchases (RFM) (Chiang, 2011 and Hosseini et al., 2010). They tend to increase their spending over time and spread positive word-of-mouth (Migueis, Van den Poel, Camanho, & Cunha, 2012). Unlike general loyal patrons, OCB loyal patrons are defined in terms of the number of participating firms covered by individual customer’s purchase history, in addition to the period since date of joining OCB, the frequency of saving points and the amount of points saved. Especially, OCB considers various purchase activities to be the most valuable information when identifying coalition loyal patrons. However, there have been few studies related to loyal patrons in coalition loyalty programs examining purchase activities in various firms over period. In coalition loyalty program, the amount of transaction data over various firms is large and the main issue is how to reduce the dimension of transaction data that describe various types of purchases. Purchase history is typically recorded as a multivariate time series. The main purpose of this study is to develop behavior-scoring models that can identify loyalty patrons in coalition program over period by proposing summary variables that can reduce the dimension of coalition transaction data. First, we propose an application-scoring model, by utilizing customers’ demographic information and their lifestyle categorized by OCB. Next, we propose a behavior-scoring model using summary variables of additional characteristics containing multivariate time series of purchase histories of customers in the OCB program. In order to identify customers’ behavior trends, we create several summary variables from customers’ transaction data. Our results are expected to contribute to not only OCB business but also similar coalition programs in utilizing customer data for their marketing strategies. This paper is organized as follows: In Section 2, we review literature related to transaction data processing and loyalty programs. In Section 3, behavior-scoring models are developed using empirical data. In the last section of the paper, we discuss our study results as well as areas for further research.
نتیجه گیری انگلیسی
OCB, the largest coalition loyalty program in Korea, offers a number of benefits over single loyalty programs from a firm’s perspective. In addition, holding and gaining loyal patrons strengthens the benefits firms receive from coalition loyalty programs. Thus, insights into demographic information and the purchasing behaviors of loyal patrons are crucial for understanding whether a coalition loyalty program is doing what it is supposed to do. Coalition loyalty programs, which create a network of participating firms and users, can collect customers’ data from their purchasing activities in relation to participating firms. The various purchase activities are regarded as valuable information when it comes to defining loyal patrons. However, the size of transaction data becomes huge and summary statistics are necessary to represent purchase activities during a certain period rather than for each transaction activity. We create summary variables that can be applied to reduced period data from actual transaction. Details are described in Table 4. These variables are then applied to the behavior scoring model for OCB loyal patrons. From the behavior-scoring model, it was observed that OCB loyal patrons who tend to extensively save points in various areas apparently accumulated more points in online shopping, department stores or theaters. Based on our findings, efficient ways of doing business in OCB are recommended as follows. First, to effectively attract customers who do not show tendencies to become OCB loyal patrons, OCB and participating firms need to focus on credit card users and convenience store/supermarket shoppers. Credit card users who frequently save OCB points are less interested in saving points than are users of telecommunication services who are regarded as normal customers among OCB service users. OCB needs to attract credit card users by offering services that remind them of OCB rewards and benefits offered by methods other than credit card use. Coalition firms also need to be concerned about customers who save points mainly at convenience stores or supermarkets. Offering additional point awards that can be used in buying daily necessities leads customers to use OCB services because those users usually purchase daily necessities at convenience stores or supermarkets. Second, to effectively retain customers who display strong tendencies to become OCB loyal patrons—customers who mainly use online shopping, or patronize department stores and theaters—should receive particular attention from OCB. Customers who make purchases at online shopping malls are sensitive to product prices. Therefore, companies can utilize up-selling techniques, encouraging customers to purchase other products at lower prices based on the past purchase information of customers who use online shopping malls. In addition, customers who repeatedly buy a product at department stores tend to be loyal to specific brand names. Therefore, OCB-participating firms need to establish strategic alliances with brands with potential because those alliances appeal to customers who are brand-sensitive. And finally, customers who frequently enjoy movies can be attracted by offers of premiere movie tickets or various discount services in OCB. From our study of loyal patrons in coalition loyalty programs, we identify their various needs by their purchasing activities. Therefore, we recommend that coalition loyalty programs provide customer segmentation that offers different services, rewards and incentives depending on customer behavior. We expect that this kind of customer service policy can address customer needs and reinforce relations with customers. A limitation of our study is that we surveyed only one year of transaction data in 2010 while our demographic data were accumulated from 2000 to 2010. In future research, by collecting transaction data over a longer term, one can seek to verify the causal relationship between demographic information for OCB loyal patrons and purchase histories using a structural equation model. This information would directly benefit cross-selling or up-selling strategies by determining accurate customer purchase patterns along with customer characteristics. Moreover, the proposed research framework can be extended to other coalition loyalty programs. Our empirical study results indicate that the classification accuracy of the behavior-scoring model that takes into account additional variables reflecting transaction information obtained during a recent six-month period is better than that of a model that does not include such variables. These kinds of derived variables can be used for behavioral scoring for other business entities such as credit card companies. These areas are left for further research.