Abstract
In this study, we aim to apply data mining techniques to uncover hidden patterns in consumer behaviour. We use a cross-sectional data representing a random sample of consumers in Kuwait. A novel data mining technique known as market basket analysis, which was first introduced to the marketing research literature in the 1990s, is applied for the first time to investigate consumer behaviour in Kuwait. This technique is also known in the literature as association mining or affinity analysis as it attempts to extract association rules by determining which sets of products or items tend to purchased together. We also use multidimensional scaling technique in order to visualise perceived similarities among various products. Our results indicate that significantly better decisions might be made by discovering and visualising hidden patterns in readily-available supermarket transactional data. We believe that knowledge discovered might be used in making better supermarket layout decisions. In fact once association rules are discovered, MBA results might be applied in several scenarios, including market segmentation, designing catalogues for up-selling or cross-selling, and designing promotion and advertising strategies.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Data Analysis Techniques and Strategies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.