Qualitative and Quantitative Analysis of the Puerile Strategy on Big Data

Abstract

With the advent of the era of big data, the economic market has been in a dynamic change. In order to adapt to this change, many companies have implemented a widely used strategy of expanding sales at low prices and low interests - “puerile”. Thus, we analyze the “puerile” strategy from three aspects: turnover, profit margin, and discount strength of the mall. Through mathematical modeling, charts are created by collecting the sales data of a shopping mall in 2017 and 2018. By separating and filtering the collected data, the data with missing cost prices is separated and filtered out. The abnormal data is excluded, and the required data is finally obtained. By finding minimum and maximum profit margins, the average daily profit margin and turnover of the mall based on small profits and high sales are calculated. By setting the four indicators to measure the discount strength of the anti-discount sum, sales volume ratio, turnover ratio, and cost ratio, a linear weighting mathematics model of the discount force is established. Finally, the daily discount strength is classified, and the size of the discount force is analyzed.