亚马逊客户服务:大数据分析

Q4 Mathematics
Suyash Sharma, Mansha Kalra, Ashu Sharma
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引用次数: 1

摘要

“亚马逊大数据”利用大数据对电子商务行业进行了彻底的分析,以及某些趋势如何影响该领域的组织运作。随着电子商务的发展,在线消费者的足迹显著增加。亚马逊、Flipkart和其他电子商务平台等公司积累了大量消费者信息,尤其是自疫情开始以来。在这个行业中,对产品的评价和评级在决定客户对最终购买的情绪方面发挥着至关重要的作用。这些因素决定了品牌的销售和形象。在今天的环境中,细心的客户会仔细查看产品的评级,以及作为筛选媒介的评论。在两种类似产品之间的平局中,客户购买的产品具有更高的评级和更好的评价。因此,这使我们开发了一个对产品销售具有重要意义的理想评级指标。此外,成为产品差异化的工具。这份手稿是一种标准化客户评级和维护数据神圣性的方法。我们讨论的模型是客户评级、他们各自的评论以及从同一评论中得出的情绪评分的融合。这些模型还帮助我们根据客户群的评价和评级来定义不同个性的客户群。除此之外,客户细分是未来深入研究销售数据和了解客户财务行为的一个领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Amazon customer service: Big data analytics
“Amazon Big Data”, conducts a thorough analysis on the e-commerce industry using big data and how certain trends can affect the functioning of the organizations delving in the field. With the growth of e-commerce, there has been a significant rise of the online consumers’ footprint. Companies such as Amazon, Flipkart and other e-commercial platforms have accrued huge chunks of consumer information, especially since the start of the pandemic. In this industry, reviews and ratings given to a product play a crucial role in determining the sentiments of the customers associated towards making the final purchase. Such factors account for the brand’s sales and image. In today’s landscape, a careful customer goes through the ratings of the product, its reviews which serve as a medium of screening. In a tie between two similar products, customers purchase a product with higher ratings and better reviews. Therefore, this leads us to the development of an ideal rating metric that is significant for the sales of the product. Moreover, become a tool for product differentiation. This manuscript is a method to standardize the ratings of customers and preserve the sanctity of the data. We discuss models which are an amalgamation of customer ratings, their respective reviews and a sentiment scored derived from the same review. These models also help us define customer clusters with different personalities based on their reviews and ratings. In addition to this, customer segmentation is a future scope to deep dive into the sales data and understand the financial behavior of a customer.
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来源期刊
Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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