I. Introduction:
Customer segmentation is a common practice in marketing and business analytics where customers are grouped together based on certain characteristics or behaviors. This segmentation allows businesses to tailor their marketing strategies and offerings to specific groups, ultimately improving customer satisfaction and business outcomes.
Unsupervised machine learning techniques are often employed for customer segmentation because they don’t require labeled training data. Clustering algorithms, such as K-means, hierarchical clustering, or DBSCAN, are commonly used in this context. These algorithms group customers based on similarities in their features or behaviors.
This Customer Market Segmentation project aims to revolutionize our marketing strategies and business performance by leveraging Unsupervised Machine Learning techniques to group customers into distinct segments based on shared characteristics and behaviors. Through this data-driven approach, we seek to tailor products, services, and marketing efforts to meet the unique needs of each segment, enhancing customer satisfaction and driving overall profitability.
