Application of Artificial Intelligence (AI) Technologies to Accelerate Market Segmentation

Authors

  • Mounika Mandapuram EKIN Solutions, 13800 Coppermine Rd, Herndon, VA 20171, USA
  • Sai Srujan Gutlapalli Interior Architect, Slce Architects LLP, New York, USA
  • Manjunath Reddy Customer Engineering Lead, Qualcomm, San Diego, CA, USA
  • Anusha Bodepudi Staff Engineer, Intuit, Plano, TX, USA

DOI:

https://doi.org/10.18034/gdeb.v9i2.662

Keywords:

Artificial Intelligence, Sales and Marketing, Market Segmentation, Machine Learning, Digitalization Trends

Abstract

In recent years, rapid advancements have been made in information technology, processing power, data handling systems, robotics, and artificial intelligence. These advancements have been made possible by recent developments in robotics. As a result of its tremendous potential and usefulness, it is currently being utilized in a wide variety of industries, including information technology, the retail sector, space science, the automotive industry, the entertainment industry, medical, transportation, medical, social sciences, and business management, amongst others. This article focuses on the exciting connotation between market segmentation and artificial intelligence (AI), which has emerged due to recent developments in the industry. Even while the propositions are being made, the ways of AI engagement in developing applications are being developed. Digital marketing, a legitimate application of marketing science, has successfully boosted customer engagement and provided value for businesses. This is performed by utilizing various digital and electronic services. In this article, we will discuss what artificial intelligence (AI) is and how recent AI breakthroughs influence the expansion and development of market segmentation. In addition, this article explores how the activities and functions of sales and marketing are affected by the various AI techniques and methodologies currently available.

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References

Autor, D. H., Dorn, D., & Hanson, G. H. (2015), Untangling trade and technology: Evidence from local labor markets, The Economic Journal, 125 (584), 621–646. DOI: https://doi.org/10.1111/ecoj.12245

Balakrishnan, P. S., Cooper, M. C., Jacob, V. S., & Lewis, P. A. (1996). Comparative performance of the FSCL neural net and K-means algorithm for market segmentation. European Journal of Operational Research, 93(2), 346- 357. https://doi.org/10.1016/0377-2217(96)00046-X DOI: https://doi.org/10.1016/0377-2217(96)00046-X

Bodepudi, A., Reddy, M., Gutlapalli, S. S., & Mandapuram, M. (2019). Voice Recognition Systems in the Cloud Networks: Has It Reached Its Full Potential? Asian Journal of Applied Science and Engineering, 8(1), 51–60. https://doi.org/10.18034/ajase.v8i1.12 DOI: https://doi.org/10.18034/ajase.v8i1.12

Casabayó, M., Agell, N., & Sánchez-Hernández, G. (2015). Improved market segmentation by fuzzifying crisp clusters: A case study of the energy market in Spain. Expert Systems with Applications, 42 (3), 1637-1643. https://doi.org/10.1016/j.eswa.2014.09.044 DOI: https://doi.org/10.1016/j.eswa.2014.09.044

Chen, H., & Zimbra, D. (2010). AI and opinion mining, IEEE Intelligent Systems, 25(3), 74-80. DOI: https://doi.org/10.1109/MIS.2010.75

Florez-Lopez, R., & Ramon-Jeronimo, J. M. (2009). Marketing segmentation through machine learning models: An approach based on customer relationship management and customer profitability accounting. Social Science Computer Review, 27(1), 96-117. https://doi.org/10.1177%2F0894439308321592 DOI: https://doi.org/10.1177/0894439308321592

Gutlapalli, S. S. (2017a). Analysis of Multimodal Data Using Deep Learning and Machine Learning. Asian Journal of Humanity, Art and Literature, 4(2), 171–176. https://doi.org/10.18034/ajhal.v4i2.658 DOI: https://doi.org/10.18034/ajhal.v4i2.658

Gutlapalli, S. S. (2017b). The Role of Deep Learning in the Fourth Industrial Revolution: A Digital Transformation Approach. Asian Accounting and Auditing Advancement, 8(1), 52–56. Retrieved from https://4ajournal.com/article/view/77

Gutlapalli, S. S., Mandapuram, M., Reddy, M., & Bodepudi, A. (2019). Evaluation of Hospital Information Systems (HIS) regarding their Suitability for Tasks. Malaysian Journal of Medical and Biological Research, 6(2), 143–150. https://doi.org/10.18034/mjmbr.v6i2.661 DOI: https://doi.org/10.18034/mjmbr.v6i2.661

He, M., Li, Z., Liu, C., Shi, D., & Tan, Z. (2020). Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge. Asia-Pacific Journal of Ophthalmology (Philadelphia, Pa.), 9(4), 299-307. https://doi.org/10.1097/APO.0000000000000301 DOI: https://doi.org/10.1097/APO.0000000000000301

Huang, J. J., Tzeng, G. H., & Ong, C. S. (2007). Marketing segmentation using support vector clustering. Expert Systems with Applications, 32(2), 313-317. https://doi.org/10.1016/j.eswa.2005.11.028 DOI: https://doi.org/10.1016/j.eswa.2005.11.028

Mandapuram, M. (2017). Application of Artificial Intelligence in Contemporary Business: An Analysis for Content Management System Optimization. Asian Business Review, 7(3), 117–122. https://doi.org/10.18034/abr.v7i3.650 DOI: https://doi.org/10.18034/abr.v7i3.650

Mandapuram, M., & Hosen, M. F. (2018). The Object-Oriented Database Management System versus the Relational Database Management System: A Comparison. Global Disclosure of Economics and Business, 7(2), 89–96. https://doi.org/10.18034/gdeb.v7i2.657 DOI: https://doi.org/10.18034/gdeb.v7i2.657

Mandapuram, M., Gutlapalli, S. S., Bodepudi, A., & Reddy, M. (2018). Investigating the Prospects of Generative Artificial Intelligence. Asian Journal of Humanity, Art and Literature, 5(2), 167–174. https://doi.org/10.18034/ajhal.v5i2.659 DOI: https://doi.org/10.18034/ajhal.v5i2.659

Morin, C. (2011). Neuromarketing: The new science of consumer behavior. Society, 48(2), 131-135. https://doi.org/10.1007/s12115-010-9408-1 DOI: https://doi.org/10.1007/s12115-010-9408-1

Neeli, A. K. (2020). Impact and Role of Artificial Intelligence in Sales and Marketing. I-Manager's Journal on Management, 15(1), 1-6. https://doi.org/10.26634/jmgt.15.1.17067 DOI: https://doi.org/10.26634/jmgt.15.1.17067

Pitt, C. S., Bal, A. S., & Plangger, K. (2020). New approaches to psychographic consumer segmentation: Exploring fine art collectors using artificial intelligence, automated text analysis, and correspondence analysis. European Journal of Marketing, 54(2), 305-326. https://doi.org/10.1108/EJM-01-2019-0083 DOI: https://doi.org/10.1108/EJM-01-2019-0083

Ren, S., Chan, H. L., & Siqin, T. (2019). Demand forecasting in retail operations for fashionable products: methods, practices, and real case study. Annals of Operations Research, 1-17. https://doi.org/10.1007/s10479-019-03148-8 DOI: https://doi.org/10.1007/s10479-019-03148-8

Tchelidze, L. (2019). POTENTIAL AND SKILL REQUIREMENTS OF ARTIFICIAL INTELLIGENCE IN DIGITAL MARKETING: ACCES LA SUCCESS. Calitatea, Suppl.Quality-Access to Success, 20, 73-78.

Yoseph, F., & Heikkilä, M. (2020). A new approach for association rules mining using computational and artificial intelligence. Journal of Intelligent & Fuzzy Systems, 39(5), 7233-7246. https://doi.org/10.3233/JIFS-200707 DOI: https://doi.org/10.3233/JIFS-200707

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Published

2020-12-31

Issue

Vol. 9 No. 2 (2020): July - December Issue

Section

Articles

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Copyright (c) 2020 Mounika Mandapuram, Sai Srujan Gutlapalli, Manjunath Reddy, Anusha Bodepudi

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How to Cite

Mandapuram, M., Gutlapalli, S. S., Reddy, M., & Bodepudi, A. (2020). Application of Artificial Intelligence (AI) Technologies to Accelerate Market Segmentation. Global Disclosure of Economics and Business, 9(2), 141-150. https://doi.org/10.18034/gdeb.v9i2.662
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