Retail Analytics to anticipate Covid-19 effects Using Big Data Technologies

Abstract

Retail analytics helps a company gain a deeper understanding of customer demand, making shopping more relevant, personalized, and convenient and boosting sales using optimal pricing. This paper aims to demonstrate retail analytics through a prototype that uses big data technologies. Using the big data technologies, the raw data is stored, analyzed and visualized to get valuable decision-making insights. The project objective is to help companies get retail analytics from which they can make decisions to anticipate the Covid-19 effects. The design for the system includes Hadoop Distributed File System (HDFS), Apache Pig, Apache Hive, SparkSQL, Spark MLLib, and Apache Zeppelin. The prototype uses a dataset that contains information for the transactions in the United Kingdom. Therefore it does not relate to covid-19 retail data but helps answer relevant questions. The dataset is used to investigate revenue aggregate by the country for the top 5 countries, daily sales activity, hourly sales activity, basket size distribution, top 20 Items sold by frequency, and market basket analysis. This paper can be used to produce a production possibility curve, reduce shortage, avoid surplus, illustrate demand and supply curves, and detect current economic conditions. All these would help the decision-makers to develop strategies to help them anticipate the impacts of Covid-19.