Research On Customer Satisfaction Of Online Retail From The Perspective Of Big Data

Nowadays,online shopping relying on the development of the Internet has become a common social phenomenon.Online retail customers increased year by year,and online retail sales accounted for a growing proportion of the total retail sales of consumer goods in China.The online retail platform is flooded with a large number of product evaluation data,which directly reflect customer satisfaction.In recent years,the utilization efficiency of data resources affects the development of enterprises.Customer satisfaction determines the future development of the online retail market and the improvement of the online retail platform.Therefore,the study of customer satisfaction from the perspective of big data has not only practical value for different types of enterprises to improve customer satisfaction and competitiveness,but also data mining significance.Based on the theory of customer satisfaction measurement,this paper constructs a customer satisfaction measurement model which includes brand image,customer expectation,online retail platform,perceived quality,perceived value,customer satisfaction,customer complaint and customer loyalty by using structural equation model and customer evaluation.According to different types of e-commerce platforms,JD,suning tesco and Tmall are selected to represent B2 C,while taobao represents C2 C.The python web crawler was used to collect the evaluation data of142 specific intelligent digital products of five brands including apple,huawei,samsung,xiaomi and OPPO,of which 186,656 were useful.Then,the text data is quantified into numerical data in accordance with the structural equation model based on dictionary segmentation,entity matching and text emotion analysis.Amos was used to substitute the data of B2 C and C2 C platforms into the initially constructed structural equation model,and the model suitability test was carried out.Finally,a CSI model suitable for product evaluation is obtained.Based on the estimation of model parameters and the analysis of path coefficients,the main influencing factors of customer satisfaction of different types of platforms and the similarities and differences between them are obtained.Combined with the research results,from the four perspectives of customer demand,communication,convenience and purchase cost,the 4C theory is used to propose countermeasures to improve customer satisfaction for B2 C and C2 C online retail platforms.