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This paper proposes an SFNN (a sales factor model using a neural network), which uses a back-propagation multilayer perceptron neural network and weight matrix operation, to study the mechanism of the influencing factors of online product sales in the e-commerce platform. To achieve this objective, this study analyzes the factors and relative strength of online product sales based on four aspects: online reviews, review system curation, online promotional marketing, and seller guarantees. The empirical analysis of the SFNN model based on the data of Taobao.com shows whether the 14 factors, in relation to the four aspects, have any impact on product sales. In addition, the findings indicate that the number of sentiment words greatly affects product sales. Other factors affecting online product sales significantly include the review volume, the number of uploaded pictures, the negative review rate, the discount rate, 7+day returns and money-back guarantees, and the freight insurance. This study examines the interactions among the various factors affecting product sales on the e-commerce platform and provides management inspiration for e-commerce enterprises to manipulate online reviews, undertake effective promotion and fulfill after-sales promises.

What Factors Influence Online Product Sales? Online Reviews, Review System Curation, Online Promotional Marketing and Seller Guarantees Analysis