Uncovering Hidden Patterns: Customer Segmentation using Machine Learning
Keywords:
Customer Segmentation, CRM (Customer Relationship Management), K-Means Clustering, Machine Learning, Unsupervised Learning, Data Mining, Consumer Behavior Analysis, Data PreprocessingAbstract
Understanding customer preferences is crucial in today’s competitive business world. It helps businesses improve their marketing strategies and provide personalised, tailor-made services to their customers. One approach used by businesses is customer segmentation—the process of dividing customers into segments or clusters based on shared preferences and purchasing characteristics. Customer Relationship Management (CRM) plays a vital role in collecting and analysing customer data. In this era of artificial intelligence, customer segmentation can be enhanced using machine learning algorithms. This paper explores the use of the K-means clustering algorithm—an unsupervised machine learning algorithm—over a sample customer dataset, segmenting customers based on age, gender, marital status, profession, education, and demographics. This segmentation helps businesses manage their marketing efforts and focus on targeted audiences for specific products, thereby enhancing their business growth.
DOI: https://doi.org/10.24321/2456.9925.202501
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