Customer segmentation and churn behavior analysis : a case study of a ride hailer company in Vietnam | |
| Author | Nguyen My Linh |
| Call Number | AIT PJPR PMDS no.25-08 |
| Subject(s) | Behavioral assessment--Data processing--Vietnam--Case studies Customer relations--Forcasting--Vietnam--Case studies Machine learning |
| Note | A project report submitted in partial fulfillment of the requirements for the Degree of Master of Science (Professional) in Data Science and Artificial Intelligence Applications |
| Publisher | Asian Institute of Technology |
| Abstract | Customer retention is a critical determinant of profitability in ride-hailing platforms, as the cost of acquiring new riders consistently exceeds the cost of retaining existing ones. This study explores customer segmentation and churn prediction using a large-scale dataset of ride-hailing trip records, incorporating behavioral signals such as trip frequency, spending patterns, service mode usage, and recency of travel. Motivated by recent mobility-focused research (Loureiro et al., 2025; Forecasting Client Retention, 2020; Comparative Analysis of Ride-Hailing vs Taxi, 2024), this work extends churn analytics beyond traditional dashboards by modelling disengagement using machine learning. User-level features were engineered to capture temporal engagement, cross-service usage, monetary contribution, cancellation behavior, and activity gaps. Churn was defined as prolonged inactivity, and predictive models—Logistic Regression, Random Forest, and Support Vector Machine—were trained and evaluated against a baseline majority classifier. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC to measure both discrimination ability and reliability under class imbalance.Findings indicate that customer behavior patterns—particularly ride frequency, inter-trip gap duration, and service type preference—are strongly predictive of churn. The results demonstrate that churn modelling can move beyond descriptive reporting into proactive churn risk identification, offering direct managerial value for retention planning. This study contributes to the literature by applying machine-learning-based churn prediction to the ride hailing domain and presents a data-driven foundation for segmentation-specific retention strategy design. |
| Year | 2025 |
| Type | Project |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Professional Master in Data Science and Artificial Intelligence Applications (PMDS) |
| Chairperson(s) | Chutiporn Anutariya; |
| Examination Committee(s) | Chaklam Silpasuwanchai;Vatcharaporn Esichaikul; |
| Degree | Master of Science (Professional) - Asian Institute of Technology, 2025 |