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

Abstract

This paper proposes an SFNN (a sales factor model using a neural network), which uses a backpropagation 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 ecommerce enterprises to manipulate online reviews, undertake effective promotion and fulfill after-sales promises.

Exploring the Intention of Middle-Aged and Elderly Consumers to Participate in Inclusive Medical Insurance

“…Kai, et al [11] showed that perceived risk promotes elderly consumers' intention to purchase insurance and that it can be controlled. According to several studies [27,75,90], guarantee mechanisms reduce consumers' perceived risks and promote trust. Pavlou and Gefen [91] identified that IT-enabled guarantee institutional mechanisms promote customer's perceived effectiveness, such as personal information protection and service feedback attention, thereby helping to build buyer's trust in the seller's community.…”

“…At the same time, the guarantee mechanism enables sellers to guarantee their ability to reduce their customers' shopping risks, thus making it an effective marketing tool for promoting sales. Especially in online transactions, many scholars have suggested that online sellers should follow the principle of "no reason return" and have expanded research on online returns to multidimensional factors [75]. On the basis of their quality commitment, many institutions have also adopted a combination of service quality, after-sales, and compensation guarantees to reduce customers' perceived risks and enhance customers' confidence in their products.…”

Multidimensional analysis and prediction of E-commerce sales based on convolutional neural network

“…From the perspective of which characteristics of goods will affect users' purchase decisions, we reviewed the relevant literature and found that users' purchase decision-making process can be divided into four stages as follows: recognizing their own needs, commodity information collection, commodity utility perception and purchase decision promotion according to different goals of users. According to these four stages, combined with the previous user purchase decision research and sales forecast research, the formation stage of commodity sales and its influencing factors are obtained [10] [11]. The neural network can learn and summarize the laws of sales volume changes, so as to use these laws to predict.…”

Research on the Role of Influencing Factors on Hotel Customer Satisfaction Based on BP Neural Network and Text Mining

“…Therefore, BP neural network model is better than DBN, SVM, and RF in the analysis of influencing factors of customer satisfaction. We adopt Equation (3) to calculate the relative strength of the first-level influencing factors, i.e., the effect size of the input variables (first-level influencing factors) on the output variable (customer satisfaction) [19,77,78].…”

“…Artificial neural networks are represented as interconnected systems of neurons that can compute various values from the input information and can learn the intrinsic nature of patterns or processes from a dataset [18]. Recent studies have shown that neural networks are an effective alternative to traditional statistical techniques [19,20]. In particular, neural networks can provide better predictions than traditional regression methods and can be appropriately used to test large-scale data with a relatively large number of input variables [21].…”