An Approach of Deep Clustering Applied for Customer Segmentation to Escalate Businesses

Customer segmentation is a pivotal task in marketing and business analytics to identify distinct groups within a customer base. In this study, we applied the Deep Clustering approach for customer segmentation and compared it with traditional clustering approaches like DBSCAN and K-Means Clustering. The objective is to understand the strengths and limitations of each method in uncovering meaningful customer groups based on their purchasing behaviors and preferences. The research commences with a detailed overview of the methodologies, highlighting their underlying principles, advantages, and possible challenges. Performance evaluation metrics including silhouette score and visual assessment are employed to compare the clustering outcomes across the different methods. The results indicate that while K-Means is efficient and simple, it struggles with capturing complex data patterns. DBSCAN excels in identifying dense regions but may falter in handling sparse clusters. Deep Clustering techniques demonstrate promise in learning intricate feature representations but require careful architecture design and substantial computational resources. Each technique presents distinct advantages and challenges, making them suitable for different scenarios depending on the data characteristics and desired outcomes. This study equips businesses and researchers with valuable insights to make informed decisions when selecting clustering approaches for customer segmentation in diverse marketing and analytics applications.