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Architectural Framework for Building Chatbots in Omni-Channel Retail Businesses

Author(s) Devender Yadav
Country United States
Abstract The convergence of several customer touchpoints, which results in an omnichannel experience, is what defines the contemporary retail environment. Businesses must provide smooth and consistent consumer interactions across all channels as a result of this change. The architectural framework for creating chatbots that can operate in this omnichannel retail environment is examined in this research study. It investigates a hybrid system that makes use of the advantages that both rule-based and AI-powered chatbot architectures have to offer. The suggested framework places a strong emphasis on integration, customization, and flexibility - all of which are essential for providing positive customer experiences in a challenging retail setting. The extent of this framework, its possible effects on consumer engagement and corporate efficiency, and the practical applications it offers in the fast-paced retail industry of today are all further examined in this article. In addition to the technical architecture, the investigation takes into account the consequences for data management, customer journey mapping, and the changing role of human agents in a customer service environment that is becoming more automated. This research aims to provide a more sophisticated, customer-centric strategy to chatbot application in omnichannel retail by transcending the current paradigms.
Keywords Omnichannel Retail, Chatbot Architecture, Customer Experience (CX), Hybrid Chatbots, Conversational AI, Retail Automation, Customer Engagement, Natural Language Processing (NLP), Machine Learning (ML), Personalization, Integration, Contextual Awareness
Field Engineering
Published In Volume 13, Issue 4, October-December 2022
Published On 2022-12-06
Cite This Architectural Framework for Building Chatbots in Omni-Channel Retail Businesses - Devender Yadav - IJSAT Volume 13, Issue 4, October-December 2022. DOI 10.5281/zenodo.14866167
DOI https://doi.org/10.5281/zenodo.14866167
Short DOI https://doi.org/g84xk3
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