Retail Price Optimization Using Machine Learning Algorithms

Muralidhar, Sneha (2023) Retail Price Optimization Using Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

Within the framework of a nation's economic advancement, the retail industry plays a crucial role in promoting expansion and general enhancement. Increased retailer competition highlights the significance of successful customer acquisition and retention strategies as the business landscape keeps growing. Appropriate product pricing is the cornerstone of these tactics, and it has a big impact on customer loyalty and revenue generation. Inadequate pricing optimization may result in negative financial consequences and decreased customer satisfaction. Understanding how important pricing is to retail businesses' success, this study explores price optimization as a means of striking the right balance between profitability and customer satisfaction. Using machine learning methods—in particular, decision tree regression—becomes a powerful instrument for retailers. Retailers can determine accurate pricing for their products, anticipate demand patterns, evaluate price elasticity, and create competitive pricing strategies by utilizing machine learning. Furthermore, machine learning helps with effective inventory control, guaranteeing that stores can fulfill customer demand while reducing surplus inventory. The creation of a machine learning model especially for retail price optimization is suggested by this study. By comparing pricing strategies with competitor data, forecasting demand fluctuations, and recognizing price sensitivity, the model seeks to equip retailers with data-driven decision-making capabilities to tackle pricing challenges. The model aims to provide a comprehensive solution for retailers navigating the intricacies of a constantly changing market by implementing a comprehensive approach that incorporates a variety of factors influencing retail pricing. The principal aim of the suggested machine learning model is to furnish retailers with practical insights, enabling them to make well-informed decisions grounded in a sophisticated comprehension of market dynamics. The model is a valuable asset for retailers looking to gain a competitive edge in a dynamic marketplace because of its ability to adjust to changes in competitor strategies, respond to shifts in demand, and optimize pricing for sustained profitability.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Subhnil, Shubham UNSPECIFIED
Uncontrolled Keywords:	Retail; Price Optimization; Machine Learning in retail; Competitor Analysis; Decision Tree Regressor
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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:	18 May 2025 14:05
Last Modified:	18 May 2025 14:05
URI:	https://norma.ncirl.ie/id/eprint/7572

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