| dc.description.abstract | In today’s competitive market, understanding its customers is a key to the success of any business. The market contains various customer subgroups that can be distinguished based on purchasing habits,time spent, product selection,and travel path.To identify the pattern hidden inside these subgroups is needed to use real data as it reflects the ordinary behaviour of the customers. Analysis of the travel path data that customers make inside the shopping mall enables retailers to understand and predict customer behaviour, which has become a critical point in effective decision-making for increasing sales with more customer comfort. Introducing the right discount for the right products acts as an important mediating factor in the customer relationship. Traditional methods of determining the discount and layout have dealt only with customer transactions, which have missed other important characteristics of customers’ purchasing behaviour. This paper addresses the problem of sales increase based on personalized discount schemas and improved store layout using customers’ hopping travel paths. It uses the Frequent Pattern Growth(FPGrowth) algorithm to improve the sales and the RFM(Recency, Frequency,and Monitory value) analysis 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 a customer’s purchases. An improved version of the supermarket layout has been suggested based on the frequent travel paths of customers. The findings of this approach can be used by retailers to improve the in-store shopping experience of customers. | en_US |
