Review on machine learning based customer segmentation (Record no. 23336)

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control field 20250814155043.0
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fixed length control field 250814b xxu||||| |||| 00| 0 eng d
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Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
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9 (RLIN) 27079
Author Siddiqui, Samreen
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Title Review on machine learning based customer segmentation
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Volume, Issue number Vol.10(3), Sep-Dec
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Place of publication, distribution, etc. Ghaziabad
Name of publisher, distributor, etc. MAT Journals
Year 2024
300 ## - PHYSICAL DESCRIPTION
Pagination 13-24p.
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Summary, etc. The process of gathering and grouping clients into groups of people who share similar traits is known as customer segmentation. To segment a company's client base, keep consumers, and make money from them, customer segmentation has gained popularity in recent years. This divide increases the likelihood that a potential customer will buy an item by enabling advertisers to target a specific group of consumers. It allows them to create and employ clear lines of communication to interact with different clients regarding their offerings and draw them in. A simple strategy would be for the organizations to use radio advertising to reach older listeners and online media posts to attract younger audiences. This aids in establishing stronger client relationships and the organization's overall image as an association. Created clusters assist the business in focusing on specific clients and promoting material to them via social media platforms and marketing campaigns that genuinely interest them.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4622
Topical term or geographic name entry element Computer Engineering
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9 (RLIN) 27080
Co-Author Soni, Sunita
773 0# - HOST ITEM ENTRY
Title Journal of computer science engineering and software testing
International Standard Book Number 2581-6969
Place, publisher, and date of publication Ghaziabad MAT Journals
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URL https://matjournals.net/engineering/index.php/JOCSES/article/view/943
Link text Click here
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    Dewey Decimal Classification     School of Engineering & Technology School of Engineering & Technology Archieval Section 14/08/2025   2025-1322 14/08/2025 14/08/2025 Articles Abstract Database
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