Customer Segmentation and Analysis Based on Gaussian Mixture Model Algorithm

DOI

10.2991/978-94-6463-618-5_8How to use a DOI?

Keywords

clustering analysis; Gaussian Mixture Model; marketing strategies

Abstract

The digital age has changed the business paradigm with digital marketing becoming a key element in dealing with modern market dynamics. Changes in consumer behavior in online content consumption encourage companies to utilize digital technology to reach a wider audience and connect personally. A deep understanding of consumer buying behavior is essential, enabling companies to design responsive and relevant marketing strategies. This research also highlights the importance of segmenting customer buying behavior in the face of intense competition. Through clustering analysis using the Gaussian Mixture Model (GMM) algorithm, consumer spending data is reduced and grouped into clusters that allow companies to understand consumer preferences and tendencies. The experiment shows that there are 4 optimal cluster as basic information for further analysis. Each cluster leads to marketing strategies, such as emphasis on health and active lifestyles, increased sales of specific products, and education of low-spending clusters. This analysis also emphasizes the importance of data preprocessing and feature selection in ensuring the accuracy of clustering results.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Eka Angga Laksana
AU  - Marchel Maulana Fahrezi
PY  - 2024
DA  - 2024/12/29
TI  - Customer Segmentation and Analysis Based on Gaussian Mixture Model Algorithm
BT  - Proceedings of the Widyatama International Conference on Engineering 2024 (WICOENG 2024)
PB  - Atlantis Press
SP  - 67
EP  - 75
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-618-5_8
DO  - 10.2991/978-94-6463-618-5_8
ID  - Laksana2024
ER  -