Machine Learning and Ai in Marketing–Connecting Computing Power to Human Insights


  • Pooja Nagpal, C. Vinotha, Lucky Gupta, Gunjan Sharma, Khyati Kapil, Vijay Kumar Yadav, Akhil Sankhyan


Artificial intelligence (AI), Machine learning, Business Managers, Marketing Sectors


Researchers' interest in artificial intelligence (AI) agents that are driven by machine learning methods has been piqued as a result of the rapid changes that these technologies are creating in the marketing sector. In the framework of this article, we investigate and argue in favour of the use of methods related to machine learning to marketing research. We provide a comprehensive overview of the common aims and methodologies of machine learning and compare them to the traditional statistical and econometric approaches that are employed by marketing professionals. In this research, we claim that machine learning approaches can analyse vast volumes of unstructured data, make accurate predictions, and have model structures that are adaptable. In addition to being difficult to understand, these methodologies are also unclear with reference to the models. We provide scalable and automated decision support capabilities, which are essential for business managers.  We investigate the most important business trends and practices that are being driven by AI, as well as academic marketing research that combines machine learning approaches. Most significantly, we provide both a detailed plan for further research as well as an extensive conceptual framework.


Download data is not yet available.


Corbo, L., Costa, S., & Dabi, M. (2022). The evolving role of artifcial intelligence in marketing: A review and research agenda. Journal of Business Research, 128(March 2020), 187–203.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artifcial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42.

de Cosmo, L. M., Piper, L., & Di Vittorio, A. (2021). The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing, 1, 83–102.

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artifcial intelligence for decision making in the era of Big Data—Evolution, challenges and research agenda. International Journal of Information Management, 48(February), 63–71.

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904.

Huang, M. H., & Rust, R. T. (2021). A strategic framework for artifcial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50.

Kurachi, Y., Narukawa, S., & Hara, H. (2018). AI chatbot to realize sophistication of consumer contact points. Fujitsu Scientifc and Technical Journal, 54(3), 2–8.

Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504.

Neha Sharma, P. William, Kushagra Kulshreshtha, Gunjan Sharma, Bhadrappa Haralayya, Yogesh Chauhan, Anurag Shrivastava, “Human Resource Management Model with ICT Architecture: Solution of Management & Understanding of Psychology of Human Resources and Corporate Social Responsibility”, JRTDD, vol. 6, no. 9s(2), pp. 219–230, Aug. 2023.

William, P., Shrivastava, A., Chauhan, P.S., Raja, M., Ojha, S.B., Kumar, K. (2023). Natural Language Processing Implementation for Sentiment Analysis on Tweets. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore.

K. Maheswari, P. William, Gunjan Sharma, Firas Tayseer Mohammad Ayasrah, Ahmad Y. A. Bani Ahmad, Gowtham Ramkumar, Anurag Shrivastava, “Enterprise Human Resource Management Model by Artificial Intelligence to Get Befitted in Psychology of Consumers Towards Digital Technology”, JRTDD, vol. 6, no. 10s(2), pp. 209–220, Sep. 2023.

Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal & Abhay Chaturvedi (2023): IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & on Defined Area, Cybernetics and Systems.

William, G. R. Lanke, D. Bordoloi, A. Shrivastava, A. P. Srivastavaa and S. V. Deshmukh, "Assessment of Human Activity Recognition based on Impact of Feature Extraction Prediction Accuracy," 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2023, pp. 1-6, doi: 10.1109/ICIEM59379.2023.10166247

Mariani, M., & Fosso Wamba, S. (2020). Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research, 121, 338–352. Mayring, P. (2008). Qualitative Inhalts analyse (p. 6). Beltz Deutscher Studien Verlag.

Nguyen, B., & Simkin, L. (2017). The Internet of Things (IoT) and marketing: The state of play, future trends and the implications for marketing. Journal of Marketing Management, 33(1–2), 1–6.

Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 10021

Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–72

Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321




How to Cite

Pooja Nagpal, C. Vinotha, Lucky Gupta, Gunjan Sharma, Khyati Kapil, Vijay Kumar Yadav, Akhil Sankhyan. (2024). Machine Learning and Ai in Marketing–Connecting Computing Power to Human Insights. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 548–561. Retrieved from



Research Article