Explainable Ai In Customer Experience Management: Personalization Algorithms in Crm Systems

Аннотация

The article examines the features of CITATION integrating artificial intelligence algorithms (Explainable Vijayaprabhuvel Rajavel. ( AI, XAI) into CRM systems aimed at enhancing customer experience (Customer Experience, CX). Based on an COPYRIGHT © 2025 Original content from this work may be used under the terms analysis of recent publications, the study explores the of the creative commons attributes 4.0 License. principles of personalization as well as approaches to the explainability of machine learning algorithms, including chatbots and recommendation systems. It demonstrates that transparency and interpretability of model outputs positively influence customer trust and loyalty while simultaneously improving the efficiency of internal business processes. The article analyzes the implementation experience of XAI in the banking sector, insurance call centers, and online retail, which has led to improvements in retention, conversion, and satisfaction metrics. The information presented in the article is intended for researchers and professionals in the field of artificial intelligence focused on developing interpretable machine learning algorithms, as well as for analysts seeking to optimize CRM systems to enhance customer experience management. In addition, the material is useful for professionals in corporate governance and marketing who aim to integrate advanced Explainable AI methods into personalization strategies and decision-making processes, ensuring the transparency and adaptability of services under dynamic market conditions. Keywords: artificial intelligence, Explainable AI (XAI), Customer Experience (CX), personalization, CRM systems, machine learning, chatbots, recommendation systems The American Journal of Engineering and Technology 57 https://www.theamericanjournals.com/index.php/tajet The American Journal of Engineering and Technology Introduction: Modern companies increasingly rely on marketing, revealing moderating effects on the artificial intelligence (AI) tools to manage customer perception of customer experience, while the studies by relationships, including the personalization of services Abdo A. and Yusof S. M. [8] demonstrate how voice and products [4]. It is important not only to enhance chatbots contribute to improving the quality of the effectiveness of interactions but also to ensure the customer interactions. The research of McLean G. and transparency of the algorithmic decisions made, since Osei-Frimpong K. [9] and Nguyen D. M., Chiu Y. T. H., and explainability (Explainable AI) becomes a decisive Le H. D. [13] focuses on the determinants of adopting factor in users' trust [3]. In the context of ensuring a such technologies in the banking sector, emphasizing positive customer experience (Customer Experience, the importance of adaptive algorithms in establishing CX), such algorithmic "transparency" is gaining sustainable customer relationships. importance, as a lack of understanding of the The banking and financial services sectors represent mechanics behind recommendations or data analysis another area of AI application, where personalization results can negatively affect consumer satisfaction and and risk assessment play a key role. In the study by Ho loyalty [5]. S. P. S. and Chow M. Y. C. [11], the influence of AI on The literature review demonstrates a variety of shaping customer preferences in retail banking is approaches, ranging from conceptual models and examined, while Bhattacharya C. and Sinha M. [14] theoretical reviews to empirical studies focused on the underscore the strategic significance of AI for enhancing practical application of AI technologies across various competitiveness through improved customer industries. In the theoretical realm, Peruchini M., da experience. In addition, Zhou J. et al. [16] demonstrate Silva G. M., and Teixeira J. M. [1] review the the application of integrated methods that combine interrelation between AI and customer experience, increased data volume and model refinement for emphasizing the interdisciplinary nature of the issue, multistage credit risk assessment, an important aspect which is confirmed by the findings of Ameen N. et al. of personalization in financial services. [3] and Dwivedi Y. K. et al. [4], who examine both the Equally interesting are studies dedicated to the specifics challenges and opportunities that arise when of AI application in the entertainment and events integrating AI into organizational processes. An industries. Neuhofer B., Magnus B., and Celuch K. [7], additional contribution to the theoretical foundation is using a scenario-based approach, analyze the influence provided by the work of Robinson S. et al. [10], which of AI on event perception, while Puntoni S. et al. [6] offer proposes an evolved model of service interaction, as an experiential perspective that reveals new facets of well as by the study of Phillips-Wren G., Daly M., and consumer interaction with artificial intelligence in the Burstein