The Forecasting Sales Volume and Satisfaction of Organic Products through Text Mining on Web Customer Reviews

Abstract

The purpose of this study was to predict the online sales volume for organic products, identify important factors for selling organic products, and suggest web marketing strategies for organic product sales. Through the review of organic products on Taobao’s platform, the emotional analysis method is used to divide the review of crawling organic products into positive reviews and negative reviews. Using the Latent Dirichlet Allocation (LDA) method, extracting keywords, identifying important factors for selling organic products, using online survey methods and regression analysis methods, obtaining customers’ purchase intentions, and suggesting web marketing strategies for organic product sales, and by collecting data on organic products’ price, current price, free delivery, sales volume, number of customer reviews, customer reviews, organic labeling, and product fans on Taobao’s platform, the neural network analysis method is used to predict the online sales volume for organic products. This study found that packaging design, nutritional information, food quality, delivery risk, freshness, and source risk are the important online factors in the buying of organic products and the products’ fans, price discount, and number of customer reviews affected the sales volume. Therefore, the promotion of online services and logistics can be used to increase the sales of organic products. This research has an important role in promoting the sale of organic products and improving consumer satisfaction, providing consumers with safe and reliable products, and at the same time has important significance for promoting sustainable development.