Vol. 8, Issue 2 (2019)

Author(s):
Chenlep Yakha Konyak and VK Vidyarthi

Abstract:
Customer satisfaction is a critical determinant of success in community pharmacies, influencing customer loyalty and overall business performance. This study aims to predict customer satisfaction scores in pharmacies by leveraging machine learning techniques on a comprehensive dataset of pharmacy operational metrics. Key factors examined include prescription accuracy and safety, service efficiency and accessibility, product availability, cost management, customer engagement, and the quality of pharmacy staff interactions. Using a Random Forest Regressor, the study evaluates model performance through cross-validation and test set results, employing metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The findings highlight the significant operational factors impacting customer satisfaction and demonstrate the potential of machine learning models in accurately predicting customer satisfaction scores. The results offer valuable insights for pharmacies to enhance their operational practices, ultimately leading to improved customer satisfaction and retention.