Study On The CRM Model For Chain Retailers

With the development of information technology and the economic globalization, the giant retailers, like Wal-Mart, Carrefour and Metro, have entered and shared the retail market of China. For coping with the challenge of international retailers which hold powerful capital and advanced management ideas, Chinese chain retailers must change old management ideas to actualize the 4C-centered (Customer, Cost, Convenient, Communication) mode of management and transfer "commodity business" to "information business" by collecting and picking up useful data from distributive environment to support decision making. By this way, the abilities of competition and self-grow can be improved for Chinese chain retailers. In the process of retailing management, CRM system is the key to implement the advanced management ideas. The investigation shows that Chinese retailers rarely can implement CRM plan deeply. So, the study on the CRM model for Chain retailers, called R-CRM (Retail Customer Relationship Management) in this paper, is necessary and significant for research and application. Based on the existing research, this paper builds the CRM model for chain retailers in which the linkage between the supplier and the customer is the main idea and the operations in the model are driven by commodity's. The detail contents in this paper describes as follows:1. The subject of CRM for chain retailers is analyzed and the solutions to customer classification, suppliers evaluation and decision-making in the process ofbusiness management are described.2. The framework of R-CRM is proposed firstly. The business workflows, three-dimensional structure, the index system of analysis and the function modules are analyzed deeply for building the three-dimensional model of R-CRM, in which the linkage between the supplier and the customer is the main idea and the operations in the model are driven by commoditys. By this way, the customer, the business parterner and interior clients can be management all-round and efficiently.3. Aiming at the characteristics of retail customers buying and the variety of retail commoditys, the customer classification model based on SVM is proposed, called R-DCSS. In the model, the cloud-form mapping mechanism, the nondeterministic inference, the probability distribution function and the algorithm of SMO are used to classify the buying behavior of customers. The experimentation shows that the classification precision of the model is excellent.4. Based on BP neural network, the supplier evaluation model is proposed considering the demands of commodity-driven and the linkage management between suppliers and customers. Using the principal component method to choice main factors of evaluating suppliers, the number of dimensions can be cut down on the preconditions of saving complete evaluation imformation. Then the quantitative evaluation to suppliers can be figured out by self-learning in BP neural network. The experiment shows that the model can work with good adaptability, effectiveness and accuracy.5. Considering the distribution and heterogeneity of retailing data, the distributive decision-making model based on Byasian network: R-BNs is proposed. In the model, the correlation learning theory is applied and the Bee-gent system is used to build the framework of the model. Using the data in the distributive database, the iterative byasian network can be obtained by training to support the business decision-making. The experiment shows that the algorithm holds the characteristics of precision, efficiency and space-saving.6. For application purposes, a CRM propotype system for Hangzhou Department Store is designed and applied considering the management demands of the retailer, called HZ-RCRM. Benefiting from the algorithm and model in R-CRM, the Chinese retail enterprise can choose the optimum supplier driven by commodities, classify customers considering their buying behavior, and achieve distributive decision-making support. Furthermore, the efficiency of management and the accuracy of decision support can be improved.