Methods for Market Segmentation

DOI

10.2991/978-94-6463-098-5_182How to use a DOI?

Keywords

Market segmentation; K-means clustering; Principal Component Analysis

Abstract

In business analysis, market segmentation is a technique used to classify existing and potential customers based on their similarities, which lays the foundation for the company to maximize their profits. The market can be divided into five subgroups, including demographic factors, geographic factors, psychographic factors, customers’ benefit, and behavioral factors. In order to build appropriate market segmentation, several approaches could be applied in the marketing process. Specifically in this article, we will cover K-means clustering through partitions and Principal Component Analysis through color differentiation. For these two methods, each has its own strengths and weaknesses. The benefits of K-means clustering can be reflected by its simplicity, flexibility, and adaptivity in a large dataset, but its defects are also straightforward- manually choosing k values, dependent on the initial values and the chosen k value, and sensitive to outliers. PCA enables people to better visualize and reduce overfitting while it makes independent variables become less interpretable and loss some data.

Copyright

© 2023 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  - Shiqi Wang
PY  - 2022
DA  - 2022/12/27
TI  - Methods for Market Segmentation
BT  - Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022)
PB  - Atlantis Press
SP  - 1614
EP  - 1621
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-098-5_182
DO  - 10.2991/978-94-6463-098-5_182
ID  - Wang2022
ER  -