The application of machine learning in predicting high customer satisfaction: An analytical framework for small and medium-sized restaurants
Abstract
Small and medium-sized enterprises (SMEs), especially those in the restaurant sector, often lack the resources and expertise needed for data-driven decision making, which puts them at a competitive disadvantage. This paper addresses this gap by proposing a practical two-stage machine-learning framework to predict when restaurant customers are likely to report high satisfaction. Using a publicly available dataset of 1,500 customer records, the first stage uses a Chi-square test to identify significant, actionable factors, such as service rating, online reservation and meal type, while removing demographic variables that do not contribute useful information. The second stage applies logistic regression to classify and predict high satisfaction. Because the dataset is heavily imbalanced (far fewer ‘high satisfaction’ cases), the study shows that undersampling is necessary, with random undersampling producing the best balance and an AUC—ROC of 0.833. The results give SME restaurant managers a clear set of evidence-based priorities for improving customer experience and a practical tool for forecasting how customers may respond to future changes, helping reduce operational risk and improve marketing efficiency. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com/business/.
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Author's Biography
Dung Hai Dinh is a lecturer and academic coordinator for the master’s programme in business information systems at the Vietnamese-German University. His research focuses on using business analytics and machine learning to understand consumer behaviour and uncover insights across a wide range of business and education problems. He received his PhD in operations research and business informatics from Saarland University.
Trang Pham Diem Le is a business administration student at the Vietnamese-German University. Her academic interests centre on the application of machine learning to business problems. She has developed experience in quantitative analysis and has applied this knowledge in research projects and academic coursework.
Thi Dang Minh Nguyen is a programme officer at SEAMEO RETRAC. Her research focuses on the intersection of marketing, finance and educational technology. She is interested in marketing research, financial management behaviour and the integration of technological applications in business as well as the potential of digital transformation in higher education.
Quoc Trung Pham is Head of the Department of Management Information Systems and Head of the Simulation Laboratory at Ho Chi Minh City University of Technology’s School of Industrial Management. His research spans management information systems, knowledge management, information retrieval systems, e-commerce, data science, entrepreneurship and innovation. Professor Dr Pham’s work has been published widely and he is an active speaker on information systems in business, knowledge management and digital transformation.
Citation
Dinh, Dung Hai, Diem Le, Trang Pham, Nguyen, Thi Dang Minh and Pham, Quoc Trung (2026, March 1). The application of machine learning in predicting high customer satisfaction: An analytical framework for small and medium-sized restaurants. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 11, Issue 4. https://doi.org/10.69554/JAJH8024.
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