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Contemporary Tools for Creating Customer Value

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  • Sebastian Zupok

Abstract

Purpose: The purpose of this paper is to explore the integration of Artificial Intelligence (AI) in marketing and its transformative role in creating customer value. The study focuses on how AI technologies-such as machine learning, natural language processing, and predictive analytics-enhance personalization, predict customer behavior, and improve operational efficiency in business practices. The research also examines the challenges businesses face in integrating AI into marketing strategies and offers recommendations for maximizing customer satisfaction and loyalty through AI-driven approaches. Design/Methodology/Approach: The paper is based on a selective literature review combined with an analytical approach to evaluate the application of AI technologies in marketing. It reviews key academic sources and industry reports, focusing on case studies and examples of contemporary AI tools used by businesses to personalize customer experiences, enhance predictive analytics, and optimize customer support operations. This method allows for a focused analysis of the most relevant and impactful research and applications within the field. Findings: The research highlights that AI significantly improves customer value by enabling businesses to offer highly personalized experiences, predict consumer needs more accurately, and streamline operations. However, it also identifies key challenges, such as data privacy concerns and the high cost of technology adoption, which need to be addressed for effective AI implementation. The findings provide actionable recommendations for businesses on how to integrate AI technologies strategically to enhance customer satisfaction, loyalty, and overall value. Practical Implications: The paper provides practical guidance for businesses seeking to leverage AI to improve customer engagement and value. It outlines best practices for incorporating AI into marketing strategies, focusing on investment priorities, data management practices, and staying updated on technological advancements. The insights can be valuable for companies aiming to enhance their competitive advantage through AI-driven marketing approaches. Originality/Value: This research contributes to the growing body of knowledge on AI in marketing by offering a detailed examination of how AI tools create customer value. The originality of the study lies in its integrated perspective on the interplay between AI technologies and customer-centric strategies, making it a useful resource for both academics and practitioners.

Suggested Citation

  • Sebastian Zupok, 2024. "Contemporary Tools for Creating Customer Value," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 545-559.
    • Handle: RePEc:ers:journl:v:xxvii:y:2024:i:4:p:545-559
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    References listed on IDEAS

    as
    1. Adrian Payne & Pennie Frow & Andreas Eggert, 2017. "The customer value proposition: evolution, development, and application in marketing," Journal of the Academy of Marketing Science, Springer, vol. 45(4), pages 467-489, July.
    2. Przegalinska, Aleksandra & Ciechanowski, Leon & Stroz, Anna & Gloor, Peter & Mazurek, Grzegorz, 2019. "In bot we trust: A new methodology of chatbot performance measures," Business Horizons, Elsevier, vol. 62(6), pages 785-797.
    3. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    4. Martin Eling & Irina Gemmo & Danjela Guxha & Hato Schmeiser, 2024. "Big data, risk classification, and privacy in insurance markets," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 75-126, March.
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    More about this item

    Keywords

    Artificial Intelligence; Customer Value; Personalization; Predictive Analytics; Customer Support; Operational Efficiency; Marketing Strategies.;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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