전통시장의 고객관계관리 전략(CRM)에 대한 효율성 분석

Analysis of the Efficiency of the Traditional Market’s CRM Activities

Purpose - The purpose of this study is to analyze the effectiveness of customer relationship management (CRM) support policies for facilitating traditional markets, especially with respect to customer acquisition and maintenance, and to investigate the factors affecting CRM. Research design, data, and methodology - We analyzed the CRM efficiency of traditional markets in 16 cities and provinces in Korea on the basis of DEA analysis and Malmquist productivity analysis. The DEA model calculates a ratio of the weighted mean of various inputs to the weighted mean of various outputs and measures the efficiency of a specific decision making unit (DMU), which is compared to the reference group that has a similar input-output structure. The input variables are coupon, event, parcel service, premiums, while is the number of customers per day. Further, through regression analysis, we analyzed CRM-related factors affecting traditional markets’ customer appeal and revenue growth. Results - We obtained the results of the efficiency of traditional markets in 16 provinces. The traditional markets in Seoul, Busan, and Jeju were found to be efficient in a model CCR that used the number of customers per day as an output variable, while Chungbuk, Jeonbuk Province, and According to the results of the DEA analysis and Malmquist productivity analysis, large cities such as Seoul, Busan, and Jeju showed efficiency in CRM-related investment businesses in traditional markets for attracting customers. The Malmquist analysis results confirmed that the productivity of traditional markets increased from 2008 to 2010. The results of the regression analysis revealed that the “customer acquisition/maintenance factor” and the “offering of customer convenience facility factor” were significant to the daily average number of customers, which is a dependent variable. The results of the test with the mediating variable, “number of customers,” and the final dependent variable, “sales revenu