Please use this identifier to cite or link to this item: http://10.1.7.192:80/jspui/handle/123456789/9555
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMehta, Keta-
dc.date.accessioned2021-01-06T04:28:16Z-
dc.date.available2021-01-06T04:28:16Z-
dc.date.issued2020-06-01-
dc.identifier.urihttp://10.1.7.192:80/jspui/handle/123456789/9555-
dc.description.abstractRetail business has a huge volume of data like sales data, customer data, promotion data, store data. In today's ambitious world for the preservation and extension of a business, the retailer needs to keep up and monitor their information. Retail Analytics is a combination of two terms: Retail which means selling and Analytics which means making a green choice. Retail Analytics is used to handle unstructured data of the retailer in a structured manner. This structured data is being applied for more analysis to make business decisions. Retail Analytics can be used by a retailer of any size for tracking and analyzing data. For analysis, it uses historical and current data. By using customer purchase pattern it generates customer segments, based on customer comments identifies whether a customer is preferring a product or not.en_US
dc.publisherInstitute of Technologyen_US
dc.relation.ispartofseries18MCEN08;-
dc.subjectComputer 2018en_US
dc.subjectProject Report 2018en_US
dc.subjectComputer Project Reporten_US
dc.subjectProject Reporten_US
dc.subject18MCENen_US
dc.subject18MCEN08en_US
dc.subjectNTen_US
dc.subjectNT 2018en_US
dc.subjectCE (NT)en_US
dc.titleRetail Analytics Automationen_US
dc.typeDissertationen_US
Appears in Collections:Dissertation, CE (NT)

Files in This Item:
File Description SizeFormat 
18MCEN08.pdf18MCEN081.85 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.