Journal of Advances in Developmental Research

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Automating Customer Support in Ride-Hailing and Its Impact on Customer Satisfaction

Author(s) Fnu Nagarajan
Country United States
Abstract The growth of ride-hailing services has transformed urban mobility, offering seamless transportation experiences to millions of users worldwide. However, as these services expand, customer support challenges related to fare disputes, cancellations, safety concerns, and service quality become more complex. Traditional customer service models reliant on human agents are often inefficient, leading to increased resolution times, inconsistent service quality, and operational inefficiencies. Machine learning (ML), and natural language processing (NLP) have revolutionized customer support by automating dispute resolution, improving service personalization, and reducing costs. This paper explores the integration of AI-driven automation in ride-hailing customer support systems, its impact on user satisfaction, and the operational benefits for ride-hailing companies. Case studies from Uber and Lyft are analyzed to assess the effectiveness of automated support mechanisms. Ethical considerations, AI biases and data privacy issues are also discussed. Future trends, including AI-powered hyper-personalization and multilingual customer support, highlight how automation will continue to evolve in ride-hailing services. The study concludes with recommendations for optimizing AI-driven support systems while maintaining fairness, transparency, and user trust.
Keywords AI-driven customer support, ride-hailing, automation, machine learning, natural language processing, customer satisfaction, operational efficiency, chatbot automation, predictive analytics, dispute resolution.
Field Engineering
Published In Volume 15, Issue 2, July-December 2024
Published On 2024-08-08
Cite This Automating Customer Support in Ride-Hailing and Its Impact on Customer Satisfaction - Fnu Nagarajan - IJAIDR Volume 15, Issue 2, July-December 2024. DOI 10.5281/zenodo.15104214
DOI https://doi.org/10.5281/zenodo.15104214
Short DOI https://doi.org/g897z8
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