Research on the influence of nonlinear regression model of user data on sales strategy adjustment in cross-border e-commerce Wish platform

Under the accelerated process of economic globalization and the booming development of Internet technology, cross-border e-commerce, as a new mode of international trade, is becoming a new driving force for the transformation and upgrading of foreign trade with its high efficiency and convenience, low cost and high benefit. This study uses data cleaning and missing value filling methods to preprocess user behavior data and merchandise sales marketing data in cross-border e-commerce Wish platform, and discretizes user behavior data using rough set method. Then, we select the merchandise sales and user behavior as the dependent and independent variables to construct a multiple nonlinear regression model in order to analyze the influence of user data on sales in cross-border e-commerce Wish platform. The results of the multivariate nonlinear regression model show that user behavior in cross-border e-commerce Wish platform has a significant effect on merchandise sales (P=0.005243). It is also found that the sales strategy adjusted according to the regression results can improve the sales and promotion effect of enterprises in cross-border e-commerce platform. The research results of this paper enrich the theoretical and practical research on the optimization and adjustment of cross-border e-commerce enterprises’ sales strategies, provide theoretical basis and decision-making reference for the subsequent adjustment of cross-border e-commerce enterprises’ sales strategies, and help cross-border e-commerce enterprises to go global.