Simple analytics in Retail Sales Data using Hadoop

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2018 by IJCTT Journal
Volume-58 Number-1
Year of Publication : 2018
Authors : R.R. Karthikeyan , Dr. B Raghu
  10.14445/22312803/IJCTT-V58P102

MLA

R.R. Karthikeyan , Dr. B Raghu , "Simple analytics in Retail Sales Data using Hadoop". International Journal of Computer Trends and Technology (IJCTT) V58(1):14-19, April 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract
Data is collected from point of sale transactions, inventory status and pricing, competitive intelligence, social media, weather, and customers (scrubbed of personal identification) and then pulled together on the Hadoop Platform, allowing for a centralized analysis of correlations and patterns that are relevant to improving business. In-store and online purchases, Twitter trends, local sports events, and weather buying patterns are analyzed by big data algorithms to build innovative applications that personalize customer experience while increasing the efficiency of logistics. Point of sale transactions are analyzed to provide product recommendations or discounts, based on which products were bought together or before another product. Predictive analytics is used to know what products sell more on particular days in certain kinds of stores, to reduce overstock and to remain properly stocked on the most in-demand products, helping to optimize the supply chain.

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Keywords
– Big Data Analytics, Hadoop,Retail analytics.