Role of Machine Learning in Predicting Pharmacy Customer Behavior

DOI: https://doi.org/10.63345/ijrmp.v12.i7.3

Tashi Lama

Independent Researcher

Arunachal Pradesh, India

Abstract

Machine learning (ML) has emerged as a pivotal technology for analyzing and predicting consumer behavior across various industries, including pharmacy services. This study explores the application of ML algorithms to predict pharmacy customer behavior by integrating historical data, transaction records, and demographic information. By developing predictive models using supervised learning techniques, this research demonstrates how ML can optimize inventory management, personalize marketing strategies, and enhance overall customer experience. The results indicate that ML-based predictive analytics offer superior accuracy in forecasting customer demands and preferences when compared to traditional statistical methods. This manuscript reviews the evolution of research in this domain, outlines the methodology employed, presents statistical analyses, and discusses the implications of the findings on pharmacy operations and customer relationship management.

Keywords

Machine Learning; Predictive Analytics; Pharmacy Customer Behavior; Supervised Learning; Inventory Management; Personalization

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