Author:
Tzi Min Ong,Tong Ming Lim
Abstract
In today's fast-paced consumer electronics industry, staying ahead of the competition and satisfying customers are top priorities. This research investigates the use of AI-powered tools, particularly conversational AI and chatbots, to improve customer interaction and boost sales in electronic retail. As digital platforms become more dominant over traditional sales channels, these AI tools offer significant benefits by delivering personalized, efficient, and timely customer service. The analysis examines various AI technologies, including Large Language Models (LLMs) and retrieval- augmented generation, which enhance consumer interaction. The study also explores the practical implications and challenges of implementing these technologies, with a focus on how they can streamline operations, improve customer experiences, and drive sales. Different models like DialoGPT, Flan-T5, and Mistral 7B are evaluated for their effectiveness in real- time consumer interactions, highlighting the importance of continuous adaptation and learning within AI systems to meet consumer demands and keep up with technological advancements.
Publisher
International Journal of Innovative Science and Research Technology
Reference33 articles.
1. N. Patel and S. Trivedi, “Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty | Empirical Quests for Management Essences,” researchberg.com, Aug. 2022, Available: https://researchberg.com/index.php/eqme/article/view/46.
2. A. Pradana, O. Goh, Sing, and Y. Kumar, “SamBot -Intelligent Conversational Bot for Interactive Marketing with Consumer-centric Approach,” International Journal of Computer Information Systems and Industrial Management Applications, vol. 6, pp. 265–275, 2014, Available: https://mirlabs.org/ijcisim/regular_papers_2017/IJCISIM_61.pdf.
3. Y. Afandi, Maskur, and T. R. Arjo, “Use of Chatbot on Online Store Website as Virtual Customer Service to Improve Sales,” Proceedings of 2nd Annual Management, Business and Economic Conference (AMBEC 2020), 2021, doi: https://doi.org/10.2991/ aebmr.k.210717.012.
4. R. Pillai, B. Sivathanu, and Y. K. Dwivedi, “Shopping intention at AI-powered automated retail stores (AIPARS),” Journal of Retailing and Consumer Services, vol. 57, no. 1, p. 102207, Nov. 2020, doi: https://doi.org/10.1016/j.jretconser.2020.102207.
5. L. Cao, “Artificial intelligence in retail: applications and value creation logics,” International Journal of Retail & Distribution Management, vol. 49, no. 7, Mar. 2021, doi: https://doi.org/10.1108/ijrdm-09-2020-0350.