Analysis of Client and Product Base : Specifics of Retail Transaction Data - Sokol, Ondřej (2020)
Every retail transaction must be recorded. This means thousands to millions of transaction data rows per day for retail chains. Automatic processing of this data is crucial for the company's competitiveness. The importance of efficient processing using data driven methods -- which minimize the analyst's personal opinion in order to maximize automation -- is increasing. The proposed methods are based on the assumption that there is a large amount of data describing the purchase of a particular product, along with additional information such as baskets, links to a particular customer and his properties, and location and time of purchase.Based on this information, methods are proposed for (1) clustering the products into groups of mutual substitutes, (2) segmentation of customers based on their shopping mission, and (3) estimating the number of unique customers of a~retail chain.Methods are formulated as optimization problems. Their properties are verified by simulation studies, or compared with alternative approaches.All methods are applied to real data from a Czech retail chain.The results show that the presented methods are better or at least comparable with the common used methods. Their main advantage is a high degree of automation resulting in efficiency. The methods proposed are robust and allow to get additional new information that cannot be obtained by other methods.