Enhancing Retail Decision-Making Through Data Mining Techniques

Abstract

In an era where a deep understanding of consumer behaviour increasingly drives retail decision-making, this research explores how data mining techniques can transform the retail sector. According to [3], millions of transactions are made, and data is generated and stored in the software of those retailers, waiting for further analysis to become useful for decision-making. This thesis ventures beyond traditional analysis, employing advanced algorithms to examine transactional data from a selected supermarket. The research aims to reveal hidden patterns in supermarket transactional data. The main objective is to collect, pre-process, and analyse supermarket transactional data. This thesis employs algorithms such as Apriori, FP-growth, and NOVA classification. The Apriori algorithm revealed frequent co-purchases and significant associations between products. NOVA classification provided insights into the processing levels of purchased foods, revealing a predominance of ultra-processed foods, which has implications for consumer health and marketing strategies. The study concludes that data mining techniques can significantly enhance retail decision-making by providing actionable insights into consumer behaviour in Africa.