What method is commonly applied to segment customers using clustering?

K-Means clustering is a widely used method for segmenting customers due to its effectiveness and straightforwardness in grouping similar entities based on their characteristics. This unsupervised learning algorithm partitions data into distinct clusters, where each data point belongs to the cluster with the nearest mean.

When applied to customer segmentation, K-Means allows businesses to identify segments of customers that share common traits, making it easier to tailor marketing strategies and enhance customer experiences. By analyzing attributes like purchase behavior, demographics, or engagement levels, K-Means helps businesses unlock insights into customer preferences and trends.

The simplicity of K-Means also makes it scalable and efficient for handling large datasets, which is particularly useful in the contexts where organizations frequently deal with a significant number of customers and need to derive insights quickly. This method's ability to produce interpretable clusters allows analysts to draw valuable conclusions that inform business decisions effectively.

Linear regression, decision tree algorithms, and support vector machines are typically used for different types of predictive modeling and classification tasks rather than segmentation, making them less suitable for this specific use case.