Abstract

This paper explores the development of a conceptual model for Data-Driven Customer Value Management (CVM) aimed at enhancing product lifecycle performance and market penetration. The proposed model integrates customer insights, data analytics, and product lifecycle management to create a comprehensive framework for maximizing customer value throughout the product journey. It emphasizes the importance of data collection, personalization, segmentation, and cross-functional collaboration in driving customer engagement, loyalty, and retention. By leveraging advanced technologies such as CRM systems, machine learning, and analytics platforms, businesses can gain actionable insights to optimize product offerings, marketing strategies, and customer interactions. The study highlights how businesses can align customer value strategies with each stage of the product lifecycle—from development to decline—ensuring sustained relevance and competitiveness in the market. Furthermore, the paper provides practical recommendations for businesses to enhance product lifecycle performance and improve market penetration by focusing on customer-centric strategies, data-driven decision-making, and the optimization of communication channels. The findings underscore the need for businesses to continuously monitor and refine their CVM strategies based on customer feedback and data insights. Future research could explore the integration of emerging technologies and industry-specific adaptations further to enhance the effectiveness of the proposed conceptual model.