The Study Of Online Reviews’ Impact On Customer Satisfaction And Product Sales

With the rapid development of Internet, the network has become an important way for consumers to obtain product and service information. Online shopping has become a common behavior of people’s lives now. With time going, the number of online reviews grow rapidly. In order to help making reliable decisions, more and more users start to browse online reviews to grasp products and service information. At the same time, enterprise managers are eager to know the key factors in consumer satisfaction, in order to make marketing strategies.In this paper, we use web crawler tool to obtain online reviews on Taobao, and use text content analysis to dig out the key factors affecting consumer satisfaction from online reviews. Then, based on Elaboration Likelihood Model(ELM), we analyze how online reviews effect goods sales, and the moderating effect of product type.Firstly, we use web crawler tools to gather the reviews of search goods and experience goods on Taobao. Then we dig out the key factors affecting consumer satisfaction by content analysis, and build high frequency words matrix. The analysis revealed that the quality of goods, experience feelings and service levels are three most important factors that influence consumer satisfaction. At the same time, the data show that consumers who give added reviews are not effected by product type. Finally, we find that if the length of online reviews is longer, it will contain more key words, so the reviews are more useful.Moreover, based on the research literature at home and abroad, we put forward the concept model and hypothesis based on ELM,which is about how online reviews influence the goods sales. We create the two paths, namely the central route and the peripheral route. Afterwards, we use web crawler tools to grasp data on Taobao website. After finishing the pretreatment, we use SPSS statistical analysis tools to do descriptive analysis and correlation analysis. We use multiple regression analysis to explore how online reviews effect goods sales, and use hierarchical regression analysis to explore the moderating effects of product type. Finally, we draw the conclusions as follows:in the central route, praise reviews, neutral reviews, store reputation have positive impact on goods sales, bad reviews has negative impact. In the peripheral route, the collection amount, picture reviews and added reviews have positive impact on goods sales. Compared to search product, praise reviews, neutral reviews, bad reviews, the collection amount, picture reviews and added reviews have stronger effect for experience product sales. But product type does not moderate the relationship between the store reputation and product sales.