Abstract
Over the past few years, technology has evolved at an exponential rate and has played a pivotal role in reshaping interactions between businesses and customers across varied industries, including retail. As a consequence, retailers today face a deluge of options when it comes to interacting with and serving their customers. Retailers, however, face the significant challenge of addressing the expectations of customers by providing efficient support services just as quickly as the market is producing options. Furthermore, many customers prefer to "Do-It-Yourself" solutions and rely on self-service options to find what they are looking for. Consequently, retail customer service is moving from individual service channels, like phone agents, to multi-channel solutions that include website customer service components. However, the current self-service options are limited by the effectiveness of keyword-based search functionality that can often confuse if a user’s spelling or expectation is different from the data language, lack of personalization, deeper insights, and limitations in search depth.
References
S. Kumari, “AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging Machine Learning to Automate Threat Detection, Vulnerability Management, and Incident Response”, J. of Art. Int. Research, vol. 2, no. 1, pp. 286–305, Apr. 2022
Tamanampudi, Venkata Mohit. "A Data-Driven Approach to Incident Management: Enhancing DevOps Operations with Machine Learning-Based Root Cause Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 419-466.
Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.
Singh, Jaswinder. "Sensor-Based Personal Data Collection in the Digital Age: Exploring Privacy Implications, AI-Driven Analytics, and Security Challenges in IoT and Wearable Devices." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 785-809.
Tamanampudi, Venkata Mohit. "Natural Language Processing for Anomaly Detection in DevOps Logs: Enhancing System Reliability and Incident Response." African Journal of Artificial Intelligence and Sustainable Development 2.1 (2022): 97-142.
J. Singh, “How RAG Models are Revolutionizing Question-Answering Systems: Advancing Healthcare, Legal, and Customer Support Domains”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 850–866, Jul. 2019
Tamanampudi, Venkata Mohit. "AI and NLP in Serverless DevOps: Enhancing Scalability and Performance through Intelligent Automation and Real-Time Insights." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 625-665.