AI-Driven Customer Experience Optimization in Digital Platforms

  • Authors

    • Thabo Nkosi Faculty of Engineering, Johannesburg Technical University, South Africa Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2022PII9Q8A

    Published 07-05-2022

  • Artificial Intelligence, Customer Experience, Digital Platforms, Machine Learning, Personalization, Predictive Analytics, Natural Language Processing, Recommendation Systems, User Engagement, Data Analytics

    Issue

    Section

    Articles

    How to Cite

    [1]
    T. Nkosi, “AI-Driven Customer Experience Optimization in Digital Platforms”, IJAIDT, vol. 5, no. 2, pp. 01–16, Jul. 2022, doi: 10.67228/30713315/IJAIDT-2022PII9Q8A.
  • Abstract

    The rapid growth of digital platforms has changed customer interactions from transactional to experience-based approaches. AI plays a key role in enabling personalized and adaptive customer experiences at scale. This paper discusses AI-based customer experience optimization in digital platforms, including methodologies, frameworks, and pre-2018 results. Customer experience optimization improves user interactions across websites, mobile apps, and social media, replacing traditional rule-based systems with scalable AI techniques. Machine learning, NLP, and predictive analytics help in predicting customer needs, automating interactions, and improving service delivery. AI models such as collaborative filtering, reinforcement learning, and deep neural networks are used for real-time recommendations, chatbots, sentiment analysis, and behavior prediction. Big data technologies like cloud computing and data lakes support these systems. The study uses literature review and architectural analysis to propose a framework covering data collection, preprocessing, training, and deployment. Key performance indicators include customer satisfaction, engagement, conversion, and retention. Results show that AI improves personalization, response time, and efficiency, but challenges like data privacy, bias, and system complexity remain. Overall, AI is a key enabler of improved customer experience in digital platforms.

  • References

    [1] P. Lemon and P. Verhoef, “Understanding Customer Experience Throughout the Customer Journey,” Journal of Marketing, vol. 80, no. 6, pp. 69–96, 2016.

    [2] M. B. Holbrook and E. C. Hirschman, “The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun,” Journal of Consumer Research, vol. 9, no. 2, pp. 132–140, 1982.

    [3] K. N. Lemon and P. C. Verhoef, “Customer Experience Management: A Review and Research Agenda,” Journal of Marketing Management, vol. 32, no. 3–4, pp. 1–25, 2016.

    [4] T. Davenport, A. Guha, D. Grewal, and T. Bressgott, “How Artificial Intelligence Will Change the Future of Marketing,” Journal of the Academy of Marketing Science, vol. 48, pp. 24–42, 2020.

    [5] E. Brynjolfsson and A. McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY, USA: W. W. Norton, 2014.

    [6] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

    [7] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010.

    [8] J. Manyika et al., “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute, 2011.

    [9] X. Amatriain and J. Basilico, “Recommender Systems in Industry: A Netflix Case Study,” in Proc. IEEE Int. Conf. Data Mining Workshops, 2012, pp. 1–8.

    [10] G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art,” IEEE Trans. Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.

    [11] T. Chen, X. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proc. ACM SIGKDD, 2016, pp. 785–794.

    [12] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.

    [13] A. Vaswani et al., “Attention Is All You Need,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.

    [14] D. Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, vol. 529, pp. 484–489, 2016.

    [15] F. Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge, MA, USA: Harvard University Press, 2015.

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