UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 3 Issue 5
May-2016
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1605052


Registration ID:
160229

Page Number

301-305

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Title

Optimizing Sales and Marketing through Data Mining

Abstract

The present retail sector uses market rumours and advertisements in mass media and social networking to determine the stock levels and the type of stocks for the market. This causes many unexpected risks due to gap what these channels broadcast and reality. So we propose to find the marketing value of different products by collecting details from the retail outlets themselves and analyze these data programmatically to identify patterns in these sales data. These patterns can be used to create a more accurate picture of real market demand of every product.

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"Optimizing Sales and Marketing through Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 5, page no.301-305, May-2016, Available :http://www.jetir.org/papers/JETIR1605052.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Optimizing Sales and Marketing through Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 5, page no. pp301-305, May-2016, Available at : http://www.jetir.org/papers/JETIR1605052.pdf

Publication Details

Published Paper ID: JETIR1605052
Registration ID: 160229
Published In: Volume 3 | Issue 5 | Year May-2016
DOI (Digital Object Identifier):
Page No: 301-305
Country: Idukki, Kerala, India .
Area: Science & Technology
ISSN Number: 2349-5162


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