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Walmart's Sales Data Analysis - A Big Data Analytics Perspective

机译：沃尔玛的销售数据分析-大数据分析的视角

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Information technology in this 21st century is reaching the skies with large-scale of data to be processed and studied to make sense of data where the traditional approach is no more effective. Now, retailers need a 360-degree view of their consumers, without which, they can miss competitive edge of the market. Retailers have to create effective promotions and offers to meet its sales and marketing goals, otherwise they will forgo the major opportunities that the current market offers. Many times it is hard for the retailers to comprehend the market condition since their retail stores are at various geographical locations. Big Data application enables these retail organizations to use prior year's data to better forecast and predict the coming year's sales. It also enables retailers with valuable and analytical insights, especially determining customers with desired products at desired time in a particular store at different geographical locations. In this paper, we analysed the data sets of world's largest retailers, Walmart Store to determine the business drivers and predict which departments are affected by the different scenarios (such as temperature, fuel price and holidays) and their impact on sales at stores' of different locations. We have made use of Scala and Python API of the Spark framework to gain new insights into the consumer behaviours and comprehend Walmart's marketing efforts and their data-driven strategies through visual representation of the analysed data.
机译：在21世纪，信息技术正在飞速发展，需要处理和研究的大量数据使传统方法不再有效的数据有意义。现在，零售商需要对消费者有360度全方位的了解，否则，他们可能会错过市场竞争优势。零售商必须进行有效的促销和报价才能实现其销售和营销目标，否则零售商将放弃当前市场提供的主要机会。由于零售商的零售店位于不同的地理位置，因此很多时候零售商很难理解市场状况。大数据应用程序使这些零售组织能够使用上一年的数据来更好地预测和预测来年的销售额。它还使零售商获得有价值的分析见解，尤其是在特定地理位置，不同地理位置的特定商店中，以所需的时间确定具有所需产品的客户。在本文中，我们分析了全球最大零售商沃尔玛商店的数据集，以确定业务驱动因素，并预测哪些部门受不同情况（例如温度，燃油价格和假期）的影响以及它们对门店销售额的影响。不同的位置。我们利用Spark框架的Scala和Python API来获得对消费者行为的新见解，并通过可视化表示所分析数据来理解沃尔玛的营销工作及其数据驱动策略。

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