Leveraging Data Science and Machine Learning for Enhanced Retail Operations


Authors : Shankar Karande; Sainath Kolpe; Gayatri Korbad; Onkar Komatwar; Radhika Adki

Volume/Issue : Volume 9 - 2024, Issue 3 - March

Google Scholar : https://tinyurl.com/4h4sazwy

Scribd : https://tinyurl.com/4d354ysj

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAR199

Abstract : In today’s business environment, the combination of advanced technology and data-driven insights has revolutionized key aspects of operational efficiency and strategic decision- making. This abstract summarizes the essence of four critical areas in this context: demand forecasting using machine learning methods, inventory management systems, location-based data analytics, and market sales prediction using machine learning. These domains play a key role in shaping the success of businesses by enabling them to proactively respond to market trends, increase customer satisfaction, optimize supply chains and make informed decisions. This paper offers an extensive perspective on these domains and highlights their importance in driving business value, competitiveness and adaptability in today’s fast-changing markets.

Keywords : Retail Analytics, Data Science, Geospatial Analysis, Market Basket Analysis, Sales Prediction, Pricing Optimization, Assortment Planning, Customer Segmentation, Association Rules, Maximize Profit, Business Data Processing, Location analytics, Machine Learning, Big Data, Retail Operations.

In today’s business environment, the combination of advanced technology and data-driven insights has revolutionized key aspects of operational efficiency and strategic decision- making. This abstract summarizes the essence of four critical areas in this context: demand forecasting using machine learning methods, inventory management systems, location-based data analytics, and market sales prediction using machine learning. These domains play a key role in shaping the success of businesses by enabling them to proactively respond to market trends, increase customer satisfaction, optimize supply chains and make informed decisions. This paper offers an extensive perspective on these domains and highlights their importance in driving business value, competitiveness and adaptability in today’s fast-changing markets.

Keywords : Retail Analytics, Data Science, Geospatial Analysis, Market Basket Analysis, Sales Prediction, Pricing Optimization, Assortment Planning, Customer Segmentation, Association Rules, Maximize Profit, Business Data Processing, Location analytics, Machine Learning, Big Data, Retail Operations.

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