For businesses seeking to figure out and satisfy the different needs and preferences of their consumer base, customer segmentation is an essential take on. Methods using machine learning, such as K-means clustering, have grown in popularity recently because of their ability to instinctively divide clients into different segments based on their commonalities. The use of K-means clustering in consumer segmentation is examined in this abstract, along with its advantages, disadvantages, and possible solutions.
An effective method of unsupervised learning known as the K-means clustering algorithm divides a given dataset into a number of groups based on how similar the data points are to one another. K-means clustering can be used in customer segmentation to find homogeneous groups of customers who have similar traits, behaviors, or preferences.
K-means clustering, which offers simplicity, scalability, and interpretability, is an effective tool for customer segmentation. Businesses can better understand their diverse audience and target their marketing efforts by splitting their customer base into separate groups. The challenges associated with K-means clustering must be carefully analysed however, and suitable methods must be used to mitigate their adverse impact on the results.