This empirical study investigates the strategic equilibrium between consumer privacy concerns and perceived utility in the deployment of hyper-personalized marketing frameworks powered by predictive analytics and machine learning algorithms. As contemporary e-commerce, digital streaming, and financial technology platforms shift toward real-time behavioral tracking and automated decision-making engines, marketing managers face an escalating dilemma: balancing enhanced customer lifetime value with consumer pushback regarding intrusive surveillance and unauthorized profiling. Grounded in the Privacy Calculus Theory and the Extended Technology Acceptance Model (TAM), this paper constructs a conceptual model evaluating how personalization convenience, cognitive load reduction, and predictive accuracy interact with perceived privacy risks, third-party data sharing fear, and algorithmic opacity. Utilizing a structural equation modeling (SEM) approach based on primary survey data collected from active e-commerce participants (N = 150), the empirical findings demonstrate that while perceived personalization value exerts a strong positive influence on purchase intention and platform stickiness, this relationship is heavily attenuated by high privacy risk perception. Furthermore, algorithmic transparency and user-controlled data management dashboards emerge as vital moderating variables capable of rebuilding consumer trust. The paper concludes with actionable strategic managerial recommendations for embedding privacy-by-design principles into digital marketing architecture while sustaining competitive algorithmic differentiation.

Upadesh K. Malame, Dr. Atul B. Tekade, "Consumer Privacy Concerns versus Perceived Value of Personalised Marketing: A Study of Online Shoppers" International Journal of Scientific Research and Engineering Development, V9(5): Page(1413-1415) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.