数据分析带来的体验式零售

Urshita Ghosh Dastidar, S. Ambekar, M. Hudnurkar, A. Lidbe
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引用次数: 1

摘要

这项研究的目的是建立如何在零售行业的消费者数据可以利用和分析,以提供客户一个增强购物体验。与文本挖掘相关的流行机器学习算法有助于解析自然语言,并有助于理解品牌形象和品牌当前缺乏的内容。在过去十年中,尽管电子商务给零售业带来了一场革命,但购物趋势表明,消费者在线下商店的支出高于线上。全渠道零售的兴起和数据驱动决策正在将零售商的重点转移到提供增强的店内客户体验上。零售商们正在努力寻找在竞争激烈的环境中脱颖而出的方法。解决这个问题的办法是提供零售娱乐。这项研究有助于了解如何分析可用的客户数据,以创造独特的体验,并使基于体验的商店成为可能。本研究的结果将有助于零售公司了解全渠道如何在创建客户参与策略中发挥重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experiential Retailing Leveraged by Data Analytics
The purpose of the study is to establish how in retail industry consumer data can be leveraged and analysed to provide customers an enhance shopping experience. Popular machine learning algorithms related to text mining aids in parsing the natural language and helps to understand the brand image and what the brand currently is lacking. In the last decade, although ecommerce brought a revolution in retail industry, shopping trends show that consumers spend more in offline store than online. The rise of omni-channel retailing and data-driven decision-making are shifting retailer focus to providing enhanced in-store customer experiences. Retailers are trying to find ways to stand out in the highly competitive environment. The solution to this problem is providing retailtainment. This study helps to understand how the available customer data is to be analysed to create unique experiences and enable experience-based stores. The results of this study will help a retail company understand how omnichannel play an important role creating customer engagement strategies.
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