The Future of Customer Experience is now: How AI is Leading the Charge in Customer-Centric Innovation |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-11 |
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| Year of Publication : 2024 | ||
| Authors : Mohan Mannava | ||
| DOI : 10.14445/22312803/IJCTT-V72I11P111 | ||
How to Cite?
Mohan Mannava, "The Future of Customer Experience is now: How AI is Leading the Charge in Customer-Centric Innovation," International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 105-115, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I11P111
Abstract
In today’s ever-changing world with new advancements, Customer Experience or CX plays a critical role in a value proposition, thereby affecting customer loyalty, top-line metrics and business growth. Artificial Intelligence (AI) is becoming very important because AI provides adequate, rapid, and smooth Mass-Customized interactions. Self-checkout to virtual assistants, current innovation reveals how tools supported by Artificial Intelligence increase customer-centric experience, helping to decipher and estimate the customer’s wants and needs in real-time. Thus, this paper focuses on the revolutionary impact of AI in recasting customer experience management initiatives, which includes understanding AI applications in customer marketing, customer service, customer emotion analysis, and prediction of the probable behavior of customers. By means of AI, organizations can design and deliver unique experiences for their customers, fine-tune contacts, and improve satisfaction, loyalty, and customer retention. This article thus presents a critical discussion of prior and current literature on AI and customer experience and the research methodology adopted for assessing AI quality in improving CX innovations. It expounds on results from recent case studies on the value of integrating AI. It is not a matter of whether customer experience will be powered by AI, but when and by how much, as customer data drives decisions, interactions with customers, and learning in near-real time. Therefore, as AI technologies advance, companies adopting AI in customer experience approaches will close distance or gain ground on their competitors since they will efficiently offer superior, preemptive and personalized experiences.
Keywords
Artificial Intelligence (AI), Customer Experience (CX), Personalization, Chatbots, Predictive Analytics, Sentiment Analysis.
Reference
[1] Jillian C. Sweeney, and Geoffrey N. Soutar, “Consumer Perceived Value: The Development of a Multiple Item Scale,” Journal of Retailing, vol. 77, no. 2, pp. 203-220, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Wayne D. Hoyer et al., “Transforming the Customer Experience Through New Technologies,” Journal of Interactive Marketing, vol. 51, no. 1, pp. 57-71, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Tabendang Ebaietaka, “The Use of Artificial Intelligence (AI) in Enhancing Customer Experience,” Vytautas Magnus University, Master Thesis, pp. 1-95, 2024.
[Google Scholar] [Publisher Link]
[4] Y. Kim et al, “Analytics for Understanding Customer Behavior in the Energy and Utility Industry,” IBM Journal of Research and Development, vol. 60, no. 1, pp. 1-13, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Avi Yaeli et al., “Understanding Customer Behavior Using Indoor Location Analysis and Visualizatio,” IBM Journal of Research and Development, vol. 58 , no. 5/6, pp. 1-20, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Katherine N. Lemon, and Peter C. Verhoef, “Understanding Customer Experience Throughout the Customer Journey,” Journal of Marketing, vol. 80, no. 6, pp. 69-96, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Muhammad Rafiqul Islam et al., “UCBVIS: Understanding Customer Behavior Sequences with Visual Interactive System,” International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Prashant Chauhan, and Samar Sarabhai, “Customer Experience Management: Evolution and The Paradigm Shift in Marketing,” Business Perspectives, vol. 17, no. 1, pp. 18-34, 2018.
[Google Scholar] [Publisher Link]
[9] Vijay Panwar, “AI-Driven Query Optimization: Revolutionizing Database Performance and Efficiency,” International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 18-26, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Baptiste Kotras, “Mass Personalization: Predictive Marketing Algorithms and the Reshaping of Consumer Knowledge,” Big Data and Society, vol. 7, no. 2, pp. 1-14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Surendranadha Reddy Byrapu Reddy, “Predictive Analytics in Customer Relationship Management: Utilizing Big Data and AI to Drive Personalized Marketing Strategies,” Australian Journal of Machine Learning Research and Applications, vol. 1, no. 1, pp. 1-12, 2024.
[Google Scholar] [Publisher Link]
[12] Patricia Pulliam Phillips, and Cathy A. Stawarski, Data Collection: Planning for and Collecting All Types of Data, John Wiley and Sons, 2008.
[Google Scholar] [Publisher Link]
[13] Metin Çelik, “Overview of Compaction Data Analysis Techniques,” Drug Development and Industrial Pharmacy, vol. 18, no. 6-7, pp. 767-810, 1992.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Catherine Prentice, Sergio Dominique Lopes, and Xuequn Wang, “The Impact of Artificial Intelligence and Employee Service Quality on Customer Satisfaction and Loyalty,” Journal of Hospitality Marketing and Management, vol. 29, no. 7, pp. 739-756, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Nitin Ran, “Enhancing Customer Loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience,” SSRN, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mohd Asad Siddiqui, “The Significance of AI Enhanced Customer Feedback for Providing Insights on Customer Retention and Engagement Strategies for Mobile Companies,” International Journal of Engineering and Management Research, vol. 13, no. 6, pp. 182 206, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Luca Tangi, Colin van Noordt, and A. Paula Rodriguez Müller, “The Challenges of AI Implementation in the Public Sector. An In-Depth Case Studies Analysis,” dg.o ‘23: Proceedings of the 24th Annual International Conference on Digital Government Research, pp. 414 422, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Manu Sharma et al., “Implementing Challenges of Artificial Intelligence: Evidence From Public Manufacturing Sector of An Emerging Economy,” Government Information Quarterly, vol. 39, no. 4, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Laxmi Pandit Vishwakarma, and Rajesh Kumar Singh, “An Analysis of the Challenges to Human Resource in Implementing Artificial Intelligence,” The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B, pp. 81-109, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Hamed Taherdoost, and Hamed Taherdoost, “Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research,” Computers, vol. 12, no. 2, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]