Construction of Social E-commerce Merchant Segmentation Model Based on Transaction Data

With the rapid development of social e-commerce today, the statistical analysis of consumers on the platform by traditional e-commerce platforms is no longer suitable for the statistical analysis of user behaviour under the current social e-commerce. However, the different income levels of consumers and the different behaviours of using social software have put forward higher requirements for the marketing and promotion methods of social e-commerce. Therefore, it is necessary for social e-commerce to accurately subdivide merchants to identify their value, provide consumers with differentiated services, and implement more effective user strategies. In this paper, the indicators in the traditional RFM model are matched with the characteristics of social e-commerce merchants, the number of friends of social e-commerce merchants is introduced, and a RCFM model suitable for social e-commerce transaction data segmentation is constructed. In this paper, the weight of each index in the RCFM model is calculated by the analytic hierarchy process. Finally, the superiority of the new model in precise segmentation is verified through the weighted optimization and comparison experiment of the RCFM model. This research enriches the related research on social e-commerce business models, provides ideas for social e-commerce merchants' value judgment, and provides a foundation for social e-commerce enterprises to construct social e-commerce merchant portraits and implement targeted services.