Sales Forecasting, Market Analysis, and Performance Assessment for US Retail Firms: A Business Analytics Perspective

Abstract

Retail firms are the best representatives of a developed country’s economic condition because they sell many of the necessary goods used for daily consumption, including food, clothes, shoes, electric appliances, and office supplies. This study presents a novel framework to help retail practitioners achieve the following goals: (1) predict sales revenues by identifying significant economic indicators, (2) estimate stable equilibriums by capturing interactive dynamics between competing firms, and (3) derive operational efficiencies and indicate required improvements by conducting performance assessments. To verify the validity of the research, data pertaining to Walmart, Costco, and Kroger are collected. Specifically, the least absolute shrinkage and selection operator (Lasso) is adopted in order to identify significant economic indicators. Consumer price index and regular wage are two common indicators that affect the the three firms’ sales numbers. In sales forecasting, support vector regression (SVR) and multivariate adaptive regression splines (MARS), respectively, perform the best in the training set and the testing set. Finally, the Lotka–Volterra model (LVM) and data envelopment analysis (DEA) are used for competitive analysis and performance assessment. A relationship of economic mutualism has been identified between the three firms. Furthermore, research findings show that Kroger performs inefficiently, though it can expect to increase sales more than the others in stable equilibriums.