Sales Monitoring And Analysis Of New Retail Under Big Data Background

In the last decade,great change has taken place in our lives with the wide use of smart phones and the development of Internet technology,E-commerce is also growing rapidly;more and more people enjoy shopping online.Online shopping without region and time's limit not only brings a certain price advantage and a brand new consumption experiences to the consumers,but also brings new user groups,business information and huge data,which cannot be reached in the traditional retail store to business.After years of development,the growth of user groups has slowed down gradually.Some well-known companies have predicted that if they do not seek a new development model,in a few years,the growth of companies will meet their bottlenecks,pure e-commerce companies may disappear.As a result,the concept of "New Retail" is emerging constantly,which will also bring new growth points to the traditional retail industry.Driven by this win-win situation,the new retail industry has flourished in the past two years,Big data,AI,IoT have been used in the retail industry.Sales volume is the core assessment index of retail industry,so how to monitor and analysis the sales volume through big data is an important issue to be studied.Sales monitoring and analysis is mainly based on the historical data of new retail stores,analyses the influencing factors of sales and the regression forecast of sales.Good analysis results are very important for new retailers to monitor and optimize their business.For this purpose,ten key influencing factors recommended by experts are selected for analysis in this paper.Firstly,the single factor is analyzed.Because of the non-normal distribution and uneven variance of some influencing factors in the sample,this paper adopts the method of variance analysis and non-parametric test.Secondly,multivariate analysis is used to select meaningful combinations to extract some effective information through multivariate analysis,combined with descriptive information such as mean graph and number of cases.Finally,because the selected influencing factors are numeric and classified variables,together with a large amount of data,we select time series and random forest to regression the whole factors and get the important degree of the influence of each factor,The fitting results is good and can be used for monitoring and analysis of sales volume.The empirical results show that the selected factors have significant impact on sales,and the different levels of each factor have significant differences,Random forest fitting effect is better,so we mainly use random forest model to monitor and analyze sales from two aspects of factor analysis and regression prediction.