Customer‐oriented sales modeling strategy in a big data environment. (16th May 2021) - Chen, Weijun; Ogiela, Urszula; Zhang, Jindan
Abstract: In order to provide personally oriented solutions in sales strategies for different retailers in the fierce market competition, data mining is being increasingly used in retail industry. We use customer‐oriented data mining technology to extract valuable information from a great amount of historical commercial data, so that we can provide different sales promotions for merchants to extend the business. "Small profits and quick returns" is a popular sales strategy to increase sales volume by many shopping malls. By using statistical modeling, we establish a mathematical model to analysis the relationships among the discount rate, the turnover and the profit margin according to the historical sales data of a shopping mall in recent two years. Base on this, the paper conducts the empirical research on small profits and quick returns and make sales recommendations on promotions. Our analysis indicates that the effect of discount rate on turnover is greater than the effect of profit rate on turnover in the retail industry. It is also necessary to consider the different nature of goods. The final computing results are also influenced by different types of goods. Furthermore, we consider the classification of goods into three types: the sell‐well goods, the common goods, and the unsalable goods and analyze them in the similar framework. The paper shows that there are different characteristics for the different types.