The Role of Artificial Intelligence in Digital Market Analysis

  • Authors

    • Chloe King Senior Consultant, PwC, UK. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2020PII6F4M

    Published 10-03-2020

  • Artificial Intelligence, Digital Marketing, Market Analysis, Machine Learning, Predictive Analytics, Consumer Behavior, Business Intelligence, Data Mining, Sentiment Analysis, Recommendation Systems

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

  • Abstract

    Artificial Intelligence (AI) has emerged as a transformative technology in digital market analysis, enabling organizations to process and analyze large volumes of consumer, transactional, and behavioral data with greater speed and accuracy. AI technologies such as machine learning, deep learning, natural language processing, predictive analytics, and recommendation systems support key marketing functions including customer segmentation, demand forecasting, sentiment analysis, competitive intelligence, and personalized marketing. By leveraging data from digital platforms, social media, e-commerce systems, and customer interactions, AI-driven tools provide real-time insights that enhance business intelligence and decision-making. This study examines the applications, methodologies, and impact of AI in digital market analysis through a comprehensive review of existing research. The findings indicate that AI significantly improves market forecasting, customer targeting, campaign optimization, and overall business performance. While challenges such as data privacy, algorithm transparency, infrastructure requirements, and implementation costs remain, AI continues to revolutionize digital market intelligence by providing scalable and intelligent solutions. The study concludes that AI is a critical enabler of next-generation marketing analytics, customer relationship management, and strategic business planning.

  • References

    [1] Philip Kotler, K. Kartajaya, and I. Setiawan, Marketing 4.0: Moving from Traditional to Digital. Hoboken, NJ, USA: Wiley, 2017.

    [2] V. Kumar, 101 Design Methods: A Structured Approach for Driving Innovation in Your Organization. Hoboken, NJ, USA: Wiley, 2013.

    [3] Tom M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.

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

    [5] Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. New York, NY, USA: Basic Books, 2015.

    [6] Bing Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA, USA: Morgan & Claypool Publishers, 2012.

    [7] Charu C. Aggarwal, Machine Learning for Text. Cham, Switzerland: Springer, 2018.

    [8] Foster Provost and Tom Fawcett, Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. Sebastopol, CA, USA: O’Reilly Media, 2013.

    [9] Andreas M. Kaplan and Michael Haenlein, “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence,” Business Horizons, vol. 62, no. 1, pp. 15–25, 2019.

    [10] Erik Brynjolfsson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY, USA: W.W. Norton & Company, 2014.

    [11] Jure Leskovec, A. Rajaraman, and J. D. Ullman, Mining of Massive Datasets, 3rd ed. Cambridge, U.K.: Cambridge University Press, 2020.

    [12] Ashok Charan, Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques. London, U.K.: Kogan Page, 2018.

    [13] Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York, NY, USA: Crown Publishing, 2016.

    [14] Virginia Dignum, Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Cham, Switzerland: Springer, 2019.

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