Execution of market basket analysis and recommendation systems in physical retail stores to advance sales revenues

ABSTRACT

Pandemic COVID-19 struck numerous regions of the planet in the first few months of 2020. India and other emerging nations in particular saw negative growth over a few quarters of the previous fiscal year. With a contribution of over 10%, retailing is one of the major industries that contribute to India's GDP. As a result, the retail industry must recover, which may be done with the effective application of new digital technology. Here, association rules that may be utilised to create discounts and package deals are extracted using market basket analysis. Additionally, similar guidelines may be applied to determine where to arrange a product in a retail setting. Items purchased in bulk can be arranged adjacent to one another to improve sales. To suggest the products that consumers should buy, recommendation algorithms are most frequently employed in e-commerce websites like Amazon, Flipkart, etc. and streaming platforms like Netflix. Although there are numerous online and mobile apps that use recommendation engines, physical retail businesses have not yet adopted them owing to the various consequences they have, such as infrastructure, cost, etc. In this project, we've used market basket research and recommendation algorithms to develop a model that can be used in retail establishments to boost sales and improve customer satisfaction. © 2022 IEEE.