Analysis And Implementation Of Big Data Fusion For Selling Cosmetics

The big data is considered to be the hottest topic in recent years with the development of technology such as Internet application.While data keeps increasing explosively,some hidden valuable information has been excavated from the big data and starts to play more important role.Recently,with more and more importance being attached to the big data application,the excavated information can serve to analyze reasons for complex problems,to forecast the developing trend in future,and to provide valuable reference for decision-makers.For sale stores of high-end healthy skin-care cosmetics,ordinary seasonal prediction was usually not so convincing.It was difficult to explain the inner relationship between irregular customer flow and unrelated sales volume.With the sale stores eager for an explanation and an accurate forecast,we started the project on how to better develop customers and how to adapt to the market more flexibly,by means of data analysis of customer flow.With the sales history and a variety of other data sources,we could use factor analysis to fusion analyze and find the hidden factor that influences customer flow and sales volume.In this paper,based on the analysis of relationship among weather,air quality and day type,we managed to find the significant factor which impacts customer flow and sales volume through regression analysis.After model analysis and verification,we proposed a new big data based forecasting method,which would provide a theoretical and technical support for the flexible adaption of the stores to the changing environment.The forecast model we built will enable the high-end skin care sale stores to forecast the fluctuations of the customer flow in the near future,also to analyze and identify customers promptly.Meanwhile,the stores can adjust the inventory according to weather and air quality on time.