Utilizing Predictive Analytics to Enhance Retail Business Performance

Hallimsyore Kalegowda, Aravind (2023) Utilizing Predictive Analytics to Enhance Retail Business Performance. Masters thesis, Dublin, National College of Ireland.

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Abstract

The study focuses on the changing world of e-commerce, specifically looking at how important it is to group customers and predict sales to improve business strategies, with a special interest in customer behavior. The goal is to use predictive analytics in retail to make better business decisions and improve how businesses operate. The research uses predictive models and K-means clustering to make better decisions and work more efficiently in retail. The findings show that machine learning models like Random Forest and CatBoost are very good at predicting retail sales, with an R² score of 0.98. K-means clustering effectively groups customers based on how recently and frequently they buy, and how much they spend, leading to a Silhouette score of 0.93. This helps in creating focused marketing strategies. Overall, the study shows that advanced machine learning models are great for predicting retail sales and that K-Means clustering is useful for grouping customers and planning sales strategies, which helps in making smarter business decisions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jain, Mayank UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > Economics > Business H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Retail Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	08 May 2025 15:31
Last Modified:	08 May 2025 15:31
URI:	https://norma.ncirl.ie/id/eprint/7523

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