Enhancing Customer Segmentation through AI: Leveraging K-Means Clustering and Neural Network Classifiers

Abstract

This research paper explores the integration of K-Means clustering and neural network classifiers to advance customer segmentation practices, crucial for optimizing marketing strategies and improving customer relationship management. The study presents a novel approach that combines unsupervised and supervised machine learning techniques to enhance the accuracy and relevance of customer segmentations. Initially, K-Means clustering is employed to identify distinct groups within vast customer datasets, leveraging its efficiency in handling large volumes of data and discovering natural groupings based on purchasing behavior, demographics, and psychographic attributes. Subsequently, these preliminary segments serve as input for neural network classifiers, which refine the segments by learning intricate patterns and relationships within the data that traditional methods might overlook. The proposed model's efficacy is evaluated against conventional segmentation techniques using datasets from various industries. Results indicate significant improvements in segment cohesion and predictive precision, allowing businesses to develop more targeted marketing campaigns and personalized customer interactions. This innovative dual-approach not only enhances segmentation accuracy but also offers scalable solutions adaptable to dynamic market conditions, demonstrating substantial promise for enterprises seeking to leverage artificial intelligence for competitive advantage.