Optimizing Customer Support in Ride-Hailing Platforms: Leveraging Data-Driven Decision-Making and Workflow Automation
Author(s): Fnu Nagarajan
Publication #: 2503092
Date of Publication: 04.11.2023
Country: United States
Pages: 1-4
Published In: Volume 9 Issue 6 November-2023
DOI: https://doi.org/10.5281/zenodo.15087303
Abstract
The ride-hailing industry has rapidly transformed urban mobility, offering convenient, on-demand transportation services to millions of users. However, as the industry has grown, customer expectations regarding service quality, reliability, and support have increased. Efficient customer support plays a critical role in retaining users and maintaining a competitive edge. Traditional support models, which rely heavily on manual intervention, often result in delays, inconsistencies, and inefficiencies. To address these challenges, ride-hailing platforms have embraced workflow automation and data-driven decision-making, enabling predictive customer service, AI-powered chatbots, and automated issue resolution. This paper explores the role of AI, machine learning, and workflow automation in optimizing customer support, with an in-depth case study on Lyft’s help portal revamp. The discussion extends to implementation challenges, ethical considerations, and future trends shaping AI-driven customer service. By integrating advanced analytics, proactive customer assistance, and seamless automation workflows, ride-hailing companies can significantly enhance service efficiency and user satisfaction.
Keywords: Customer Support, Ride-Hailing, Data-Driven Decision Making, Workflow Automation, AI in Customer Service, Chatbots, Self-Service, Predictive Analytics.
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