Determining the retail sales strategies using association rule mining

Abstract

Competitive competition in the retail industry requires retailers to maintain improvements and formulate accurate strategies to maintain their competitiveness. A small number of daily visitors visit retail store Y if compared to other retail stores, which leads to decreased store revenue due to the small number of products sold. Therefore, it is crucial to formulate the right business strategy to increase sales by utilizing customer shopping behavior derived from transaction data. The method used is association rule mining (ARM) with a frequent pattern growth (FP-growth) algorithm to determine consumer buying patterns. Data processing results generate five valid rules that meet the specified criteria for an association relationship. Utilization rules are acknowledged by determining retail sales strategies by recommending store layouts, shopping catalogs, and voucher discounts to attract customers.