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Understand AI Driven Marketing Capabilities: Empowering Customer Experience and Deliver Value with Intelligent Tools and Technologies

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  • TANASE, George Cosmin

Abstract

The knowledge potential of a business can be improved with technology, and such technological advancements can also help in improving customer interactions. Intelligent technologies, such as artificial intelligence, are transforming businesses by gathering and analyzing huge amount of data which further improves customer interaction and experience. The biggest growth opportunity for companies nowadays is the customers’ transition from offline to online; being tech-savvy consumers, they spend most of their time online, and this also calls for a great online experience which today’s customers want. Companies are creating influential customer experiences, as with the advancements in technology, the complex things have become much easier with a single click. In order to provide enhanced customer experience, companies are utilizing artificial intelligence. Artificial intelligence (AI) is a disruptive technology enabling machines to mimic human and cognitive functions. The term is also used to represent the various capabilities of a learning system which are representative of the intelligence level perceived by humans. The different capabilities can be of different types like processing of natural language, automating, predicting, decision making, etc. Applications of artificial intelligence also include image and video recognition, understanding natural language, generating natural language, smart automation, interactive agents, analytics, and predicting. Artificial intelligence (AI) can perform various tasks like solving various problems and reasoning as using this technology machines can mimic human effective and cognitive which is required for performing such tasks. In order to take real-time decisions like predicting and other marketing-related actions, machines present, learn, and store the information on the basis of past and present knowledge as well as experience. Machines learn while assessing the decisions, and this enables machines to respond and adapt as per the dynamic business environment which was not possible earlier with traditional approaches.

Suggested Citation

  • TANASE, George Cosmin, 2024. "Understand AI Driven Marketing Capabilities: Empowering Customer Experience and Deliver Value with Intelligent Tools and Technologies," Romanian Distribution Committee Magazine, Romanian Distribution Committee, vol. 15(3), pages 26-32, September.
    • Handle: RePEc:rdc:journl:v:15:y:2024:i:3:p:26-32
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    References listed on IDEAS

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    1. Nisreen Ameen & Ali Tarhini & Alexander Reppel & Amitabh Anand, 2021. "Customer experiences in the age of artificial intelligence," Post-Print halshs-03045430, HAL.
    2. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    3. Marina Johnson & Rashmi Jain & Peggy Brennan-Tonetta & Ethne Swartz & Deborah Silver & Jessica Paolini & Stanislav Mamonov & Chelsey Hill, 2021. "Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(3), pages 197-217, September.
    4. Quentin André & Ziv Carmon & Klaus Wertenbroch & Alia Crum & Douglas Frank & William Goldstein & Joel Huber & Leaf Boven & Bernd Weber & Haiyang Yang, 2018. "Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 28-37, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    AI Technology; Conversational Commerce; Big Data; Customer Satisfaction; Internet of Things;
    All these keywords.

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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