Uncovering Useful Patterns in Shopping Cart Data
Ali Haider Hussein Ghazala, Naeem M. Asif, Baig Mirza Farhaan, Jamil Noreen
Journal of E-Technology, Vol. 7, No. 4, 2016.
DOI: 
Link: http://www.dline.info/jet/fulltext/v7n4/jetv7n4_2.pdf
Abstract:
Understanding the shopping and purchasing behaviours of customers is an essential task for business and retail organizations. While customers look for useful information from retailers as they shop, businesses seek to collect increasing amounts of data in order to deliver added value to their customers. This requires an intensive analysis of sales data. Extracting shopping patterns across the many levels of information is a non-trivial task as datasets on sales transactions can contain many levels of information such as item category, brand name, colour, and price. This paper examines the use of multi-level association rules to uncover purchasing patterns at multiple levels of detail. It shows how different kinds of purchasing patterns can emerge at different association levels of analysis. This type of analysis is indeed helpful in assisting retailers to make wise decisions for their customers.