The Impact Of Online Review On The Sales Of Apparel Products

With the rapid development of the Internet and the introduction of new e-commerce platforms,online shopping is becoming more and more common in people’s daily lives.The emergence of online commentary mechanism allows people to spread word-of-mouth through the Internet and help other buyers obtain more product information,thereby affecting other buyers’ shopping decisions and the sales of products.Researching the features of online reviews can provide a clearer understanding of how online reviews influence consumer purchasing decisions,thereby providing merchants with marketing advice to increase sales and improve user experience.This article discusses the combination of search-based and experience-based clothing products,and uses Taobao’s online reviews of apparel products as the research object.First,K-means clustering analysis methods are used to classify the popularity of products,and then multiple linear regression models are used.Statistical analysis was performed on different popular products,and a random forest algorithm was introduced to perform regression analysis on all product reviews.The research results show that for hot products,there are four factors,which are number of figure comments,number of additional comments,number of reviews,and product price,have a significant positive effect on sales.For non-hot products,there are only two factors,which are number of figure comments and number of reviews,have a significant positive impact on sales.In addition,according to the results of the random forest algorithm,it can be seen that the number of figure comments and the number of reviews have the largest impact on sales.Finally,based on the results of empirical research,reasonable suggestions are made for the marketing strategies of apparel product merchants and e-commerce platforms.