CUSTOMER SEGMENTATION AND RECOMMENDATION SYSTEM

Abstract

The objective of this project is to amplify the efficiency of marketing strategies and boost sales through customer segmentation. To achieve this, we aim to transform transactional data into a customer-centric dataset by generating new features that enable the segmentation of customers into distinct groups using the K-means clustering algorithm. This approach will allow us to gain a deeper understanding of the distinct profiles and preferences of various customer groups, providing us with valuable insights into their purchasing behaviors. Leveraging this information, we intend to develop a recommendation system that suggests top-selling products to customers within each segment who have yet to purchase those items. This personalized recommendation strategy will enhance the efficacy of marketing campaigns and drive increased sales. By tailoring offerings to each segment's specific interests and needs, we can create more targeted and effective marketing efforts, leading to improved customer satisfaction and loyalty. Additionally, this approach allows us to engage customers more effectively, offering them relevant and appealing product recommendations based on their segment's shared interests and purchase history. This data-driven strategy will help foster stronger customer relationships and build long-term loyalty, ultimately leading to sustained sales growth and success for the business. Through continuous monitoring and refinement, this project seeks to optimize the recommendation system and marketing strategies, ensuring they remain aligned with customer preferences and market trends.