Research On Quality Evaluation Of Automobile After Sales Service Based On Online Review

In recent years,the automobile industry has continued to flourish,and automobiles have become more and more popular.The increasing maturity of automobile manufacturing processes,the increasing homogeneity of automobile design,and the improvement of automobile sales front-end services have led to the phenomenon that automobile sales are declining year by year and industry competition continues to intensify.The saturated automotive Red Sea market needs to open up new business areas to upgrade the industrial structure to gain benefits.After-sales service has received more attention.Improving the quality of auto after-sales service has become a crucial factor for the automotive industry to increase its attractiveness and stimulate purchase desire.The vigorous development of the Internet enables people to publish their feelings about car after-sales service experience on the Internet.In the after-sales service,online reviews have become the main feedback channel for consumers because of their real-time convenience and the ability to provide reference for other customers..The theoretical guidance brought by the mainstream research methods in the current field cannot match it.Traditional questionnaire surveys are time-consuming and laborious,and the respondents are not actively cooperating and it is difficult to express their true thoughts.At the same time,although online reviews have great data value,they lack scientific methods for screening and extracting valuable information.Therefore,the research chooses to evaluate user satisfaction with after-sales service by analyzing online comment data.The research first uses the method of text mining to collect and process the online review data of automobile after-sales in various regions and different brands and processes it.Through the method of keyword extraction,the word cloud map of the various dimensions of automobile after-sales service quality is obtained,and then the text data is collected by the method of machine learning.Combined with the commonly used SERVPERF model for service quality,an automobile after-sales service quality evaluation model based on online reviews is constructed,which can not only provide a reference for the formulation of the SERVPERF model scale,but also combine machine learning to quantify online reviews in five dimensions.Optimized predictive analysis.This thesis takes the online reviews of 4S stores in Beijing and other cities as an example to carry out an empirical analysis of the evaluation model.After obtaining a high accuracy rate to prove the feasibility of the model,statistical analysis is carried out based on the prediction results to obtain scores in various dimensions and establish IPA Analysis chart.The results show that there is a lot of room for improvement in the dimensions of responsiveness and reliability,and targeted improvement suggestions are given based on the analysis results.