Design product placement layout and personalized discount based on Customer Travel Path

Abstract

In today’s competitive market, understanding its consumers is key to the success of any business. The market contains various consumer subgroups that can be distinguished based on purchasing habits, time spent, product selection, and travel path. To identify the pattern hidden inside these subgroups, real data is needed as it reflects the ordinary behaviour of consumers. Analysis of the travel path data that consumers make inside the shopping mall enables retailers to understand and predict consumer behaviour, which has become a critical point in effective decision making. Based on the travel path through the proposed methodology, it demonstrates an approach which uses the Frequent Pattern Growth (FP Growth) algorithm in order to improve sales based on personalized discount schemas and an improved store layout. The RFM (Recency, Frequency, Monitory value) analysis method has been used in order to identify the customer segments based on the dataset of Instacart from the Kaggle website. An FP growth algorithm has been used to identify the frequent locations and frequent products of consumers. An improved version of the supermarket layout has been suggested based on the frequent travel areas of consumers. The findings of this approach can be used by retailers to improve the in-store shopping experience of consumers.