Revolutionizing Retail: Machine Learning Applications for Enhanced Customer Experience and Operational Efficiency

AUTHOR(S)

Prithi M, Tamizharasi K

DOI: https://doi.org/10.46647/ijetms.2023.v07i05.049

ABSTRACT
In today's highly competitive retail landscape, businesses are constantly seeking innovative ways to enhance customer experience while optimizing operational efficiency. This paper explores the transformative potential of machine learning (ML) applications in addressing this dual challenge. Leveraging diverse datasets encompassing customer behaviours, inventory management, and supply chain operations, our research employs state-of-the-art ML algorithms to develop predictive models and personalized recommendation systems. Our methodology demonstrates the ability of ML to analyze vast amounts of data in real-time, leading to precise demand forecasting, dynamic pricing strategies, and tailored customer experiences. Through a comprehensive evaluation, we reveal significant improvements in revenue generation and cost reduction. Moreover, the discussion highlights the ethical considerations and challenges associated with ML adoption in retail. In conclusion, this study underscores the pivotal role of machine learning in redefining the retail landscape, offering a competitive edge through data-driven insights, and ultimately revolutionizing the industry.

Page No: 408 - 411

References:

1) Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
  2) Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188.
3) Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press.
4) Loh, H. T., & Sheng, O. R. L. (2018). The role of big data analytics in predicting customer behavior. Journal of Business Research, 81, 120-129.
5) Hajli, M. N. (2016). Social commerce constructs and consumer's intention to buy. International Journal of Information Management, 36(6), 877-889.
6) McAfee,  A.,  Brynjolfsson,  E.,  Davenport,  T.  H.,  Patil,  D.  J.,  &  Barton,  D.  (2012).  Big  data:  The management revolution. Harvard Business Review, 90(10), 60-68.
7) Wang, X., Chen, H., & Liang, X. (2019). Application of big data analytics in retail: A literature review. International Journal of Information Management, 48, 86-98.
8) Ray,  S.  (2020).  How  COVID-19  changed  dimensions  of  human  suffering  and  poverty  alleviation: economic analysis of humanitarian logistics. Вестник Астраханского государственного технического университета. Серия: Экономика, (4), 98-104.
9) Pallathadka, H., Leela, V. H., Patil, S., Rashmi, B. H., Jain, V., & Ray, S. (2022). Attrition in software companies: Reason and measures. Materials Today: Proceedings, 51, 528-531.

How to Cite This Article:
Prithi M, Tamizharasi K . Revolutionizing Retail: Machine Learning Applications for Enhanced Customer Experience and Operational Efficiency . ijetms;7(5):408-411. DOI: 10.46647/ijetms.2023.v07i05.049