Ethical Considerations in AI-Driven CRM Leveraging Cloud Computing - A Systematic Analysis
Keywords:
Artificial Intelligence, Customer Relationship ManagementAbstract
Artificial intelligence (AI) and machine learning are reshaping customer relationship management (CRM) strategies, leveraging advanced capabilities such as predictive analytics, personalized engines, and customer segmentation within cloud computing environments. This paper provides an in-depth exploration of how AI-driven functionalities, hosted on cloud computing platforms, are enabling tailored and relevant experiences, enhancing customer relationships, and fostering loyalty over time. Modern CRM systems harness vast datasets on customer interactions and behaviors stored in the cloud, which fuel AI algorithms to unveil concealed insights regarding individual preferences, future behaviors, and optimal cross-selling recommendations tailored to each customer. Key AI methodologies powering next-generation CRM systems within cloud environments, including reinforcement learning, neural networks, natural language processing, and computer vision, are analyzed. The paper delves into sample use cases and real-world examples of AI-driven CRM initiatives from industry leaders, showcasing the seamless integration of AI capabilities with cloud-based CRM solutions. Additionally, emerging technologies on the horizon such as affective computing, virtual reality, and the metaverse are explored, presenting novel opportunities to comprehend customers and fulfill their needs in highly personalized, emotionally intelligent ways within cloud environments. Critical considerations for firms implementing AI-enabled CRM in the cloud, such as data privacy, transparent AI, and mitigating algorithmic bias, are also examined. With responsible implementation, AI hosted on cloud computing platforms stands poised to revolutionize CRM, delivering unprecedented levels of personalized relevance at scale and ultimately driving growth in customer lifetime value.
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Copyright (c) 2024 International Journal of Open Publication and Exploration, ISSN: 3006-2853
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.