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2024 | OriginalPaper | Buchkapitel

Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting

verfasst von : Shweta Saraswat, Vaibhav Agrohi, Mahesh Kumar, Monica Lamba, Raminder Kaur

Erschienen in: Proceedings of Third International Conference on Computing and Communication Networks

Verlag: Springer Nature Singapore

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Abstract

Consumer segmentation is essential for accurate targeting and successful marketing efforts in today’s competitive business environment. Modern marketing groups individuals by interests and attributes. Segmentation drives targeting, personalization, and ROI. This article segments customers using K-means clustering. The Elbow approach and Silhouette score determine the appropriate number of clusters and increase segmentation accuracy. They also examine the possibility of precision targeting and customized marketing techniques across sectors. Businesses may optimize marketing, improve customer happiness, and increase profits by using K-means clustering. To compete in today’s market, this research helps marketers enhance targeting. Elbow and Silhouette K-means clustering may enhance client segmentation, engagement, loyalty, and economic success.

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Vorheriges Kapitel Convolution Neural Network Versus Transfer Learning in Image Classification

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Titel: Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting
verfasst von: Shweta Saraswat
Vaibhav Agrohi
Mahesh Kumar
Monica Lamba
Raminder Kaur
Verlag: Springer Nature Singapore
Buch: Proceedings of Third International Conference on Computing and Communication Networks
Print ISBN: 978-981-9708-91-8

Electronic ISBN: 978-981-9708-92-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-0892-5_28

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