Exploring Customer Segmentation with Machine Learning for Smarter Marketing Decisions

Customers are the foundation of any business and its most important asset. Consumers are vital for several reasons, including generating revenue, influencing corporate choices, creating new products, and enhancing the brand. Customer segmentation is the process of breaking these clients into more manageable groups according to their preferences, shopping patterns, and demographics. Segmenting clients into distinct categories helps businesspeople better understand their customers, develop customized marketing strategies, and boost service needs, which are utilized to address particular requirements. Customers gain from a customized experience that is suited to their demands, while businesspeople seeking to boost their return on investment benefit from client segmentation. There are manual methods available for the segmentation of customers based on their purchasing behavior, but these methods have some disadvantages, such as being time-consuming, having limited accuracy, and being inefficient for decision-making. The clustering algorithm, which groups consumers, is one of the computerized techniques used to overcome these restrictions. However, these techniques have certain drawbacks and limitations, such as their inability to handle high-dimensional data, noisy data, or data containing outliers. The shortcomings of these clustering algorithms prevent them from providing scalable, reliable, and flexible client segmentation solutions. This research uses a hybridized approach for both preprocessing and clustering in order to close this research gap. This results in more accurate consumer segmentation, which is used to generate insightful recommendations for the business process. The primary focus of this research is on two crucial business-relevant segmentations: demographic and behavioral. The KNN imputation approach is used to fill in the missing attribute values after the label encoding preprocessing method transforms the text data into numerical data. Then normalization is applied to standardize the values in the dataset. After that, the principal component analysis method—which reduces dimensionality and eliminates noise—is applied. For consumer segmentation, the hybrid adaptive clustering system (HACS), which combines DBSCAN and K-means clustering, is implemented. Following the completion of the segmentation process, the numerical values are once more converted into text values using reverse label encoding. Finally, the algorithm's performance is assessed using a variety of performance metrics, including the Davies-Bouldin Index, Cluster Purity, and Silhouette Score. The results clearly demonstrate that the proposed HACS model exhibits superior accuracy when compared to the current approaches.