消费者视角下电商发展前景的挖掘与分析--以亚马逊市场为例

Analysis on the Development Prospect of E-commerce from the Perspective of Consumers--Taking Amazon Market as an Example

本文以亚马逊电商平台中吹风机、微波炉、奶嘴三个典型家居热门商品为例,对其相关市场营销数据进行深度挖掘分析建立模型。本文的核心亮点为借助LDA模型以及斯皮尔曼相关性分析将消费者文本评论量化,展开指标关联性分析,实证了商品的评分与相应文本评论之间关联性较强,再凭借线性加权模型客观的良好特性,赋予上述指标不同的权重,构建了一个市场综合评价体系,用于评估商品的市场发展情况。本文还运用Python进行文本分析,绘制文本评论词云图;并引入情感分析数据集,对不同消费者的消费喜好进行了相应划分。同时本文挖掘发现,市场综合评价指标与每年市场销售份额呈现极大的相关性,在大数据的背景下,较高的市场份额可以极大程度地保证市场声誉的稳定性。基于上述结论,本文借助GM(1,1)模型进一步预测商品未来市场认可度的发展趋势,以挖掘商品进入电商市场的最优时机与策略。通过上述数据分析手段,建立的模型体系在访问足够数据之后将会有出色的表现,凭借严谨的结构和广泛的应用,期望数据分析出消费者的喜好,能快速且准确地反馈相关商品在电商市场的满意度和趋势信息,为经销商和供应商提供决策服务。

this paper takes three typical hot household products, such as hair dryer, microwave oven and pacifier, as examples to analyze the related marketing data and set up a model. The key point of this paper is to quantify consumer text reviews by using LDA model and Spilman correlation analysis, and to analyze the cor-relation between product ratings and corresponding text reviews, by virtue of the good objective character-istics of the linear weighted model, we give different weights to the above-mentioned indexes, and construct a comprehensive evaluation system for evaluating the market development of commodities. This paper also uses Python for text analysis, drawing text comment cloud graph, and introduces sentiment analysis data set to di-vide different consumers’ consumption preferences. In the background of big data, the higher market share can guarantee the stability of the market reputation to a great extent. In the background of big data, the higher market share can guarantee the stability of the market reputation. Based on the above conclusions, this paper uses GM 1,1 model to further forecast the development trend of commodity market approval degree, in order to excavate the optimal time and strategy of commodity entering e-commerce market. Through the above data analy-sis means, the established model system will have excellent performance after access to enough data, with rigorous structure and extensive application, expect the data to be analyzed to consumers’ preferences, can quickly and accurately feedback related products in the e-commerce market satisfaction and trend informa-tion, for distributors and suppliers to provide decision-making services.