Utilizing Deep Belief Networks for Consumer Behaviour Analysis in Retail Management

Consumer Behaviour Analysis in Retail Management is a crucial endeavour for businesses aiming to comprehend and respond to the diverse preferences and behaviours of their customer base. This paper presents a comprehensive exploration of methodologies., focusing on the adaptation of Deep Belief Networks (DBNs) to enhance the analysis of consumer behaviour in the retail context. Leveraging a Kaggle dataset., the study incorporates multidimensional features., spanning customer information., product details., promotional data., and transactional insights. Min-Max Normalization is applied to ensure unbiased data preprocessing. The DBN architecture is adapted to accommodate transactional., promotional., spatial., and temporal features., capturing the complexity of retail dynamics. The study unfolds with a systematic exploration of consumer behaviour patterns., emphasizing the significance of DBN s in unravelling intricate relationship. The hierarchical representations extracted by DBNs contribute to a nuanced understanding of consumer interactions and preferences., enabling businesses to tailor their strategies to the dynamic nature of retail operations. The proposed DBN model exhibits superior performance with accuracy 99.3%., through Python., the study surpassed existing methods in accuracy., precision., F1-Score., and ROC values. Comparative analysis reveals its superiority against Deep Neural Networks (DNN)., Convolutional Neural Networks (CNN)., and Naive Bayes., with a notable 3.4 % enhancement in overall performance.