Big Data Assisted Empirical Study for Business Value Identification Using Smart Technologies: An Empirical Study for Business Value Identification of Big Data Adaption in E-Commerce

Changrong Zhang, Bin Liu, B. Mohammed, A. Jumani
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Abstract

The main problem for the big data for an e-commerce site is getting a meaningful data analysis, which the big descriptive statistics consider the most crucial usage. The collection, segmentation, and analysis of customer insights are critical to developing an effective and precise tailored experience for each consumer. Analyzing and segmentation of customer insights are essential to creating an effective and personalized experience for each customer. Using price optimization (BDA-PO), big data analytics has been proposed, enabling enterprising services like tourism, shopping, transportation, and creative industries to provide variable rates for products and services using Smart Technologies for E-Business and Commerce. Price optimizing can be automated with machine learning algorithms to enhance profitability when pricing decisions are taken effectively. When pricing decisions are made correctly, it is possible to automate price optimization using machine learning algorithms.
基于智能技术的大数据辅助商业价值识别实证研究——电子商务中大数据适应的商业价值识别实证研究
电子商务网站大数据的主要问题是获得有意义的数据分析,这是大描述性统计认为最重要的用途。收集、细分和分析客户洞察对于为每个消费者开发有效而精确的定制体验至关重要。分析和细分客户洞察对于为每个客户创造有效和个性化的体验至关重要。利用价格优化(BDA-PO),提出了大数据分析,使旅游、购物、交通和创意产业等企业服务能够利用电子商务和商业智能技术为产品和服务提供可变费率。价格优化可以通过机器学习算法实现自动化,从而在有效做出定价决策时提高盈利能力。当定价决策正确时,可以使用机器学习算法自动优化价格。
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