Year: 2025
Type: article
Abstract: Targeted Customer Classification Using Machine Learning Techniques aims to optimize marketing strategies through data-driven customer segmentation and personalization. Traditional models, often limited by simplistic attributes like income, fail to capture the nuanced behaviors and preferences of diverse customer bases, resulting in static clusters and generalized offers that do not adapt to evolving consumer trends. To address these challenges, this project proposes an advanced model leveraging machine learning for dynamic and precise customer classification. The system employs KMeans clustering to segment customers into distinct groups based on demographic attributes such as age and spending patterns. Following this, a Random Forest classifier is used to predict and classify new customers into these predefined clusters. By tailoring personalized offers aligned with each cluster's characteristics, the system enhances marketing efficiency, improves customer engagement, and drives sales. Implemented using Python-based frameworks and integrated with a user-friendly interface, the model supports seamless data input and real-time predictions. This approach empowers businesses to gain actionable insights and make more informed decisions. Future enhancements include integrating real-time data processing, expanding feature sets, and exploring deep learning techniques to improve segmentation and prediction accuracy further.
Authors: Mrs. Vijayajyothi Chiluka, Pravalika Bandi, Pranathi Enumula, Laxmi Sowjanya Korvi, Varun Teja Seelam
Language: English
Subfield: Marketing
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4407229382