Analysis and FP-Growth Algorithm Design on Discount Data of Department Store Members

Abstract

AbstractThis paper mainly studies the discount data of department store members. The researsh shows that the total supply of discounted goods and the number of reward points issued have the most significant relationship with customer activation rate, the increase of discount rate and coverage scale would increase the activation rate of inactive members and invalid members. The increase of the score rate may have a stronger incentive effect on active members, but it has no obvious incentive effect on inactive and ineffective members. In addition, by integrating the commodity records of each purchase, and analyzing association rules, commodity combinations with associated consumption relationships are obtained, and the analysis model of commodity portfolio association rules is established. This paper is mainly based on the data of the member information, the sale water meter, the member consumption detailed list, the merchandise information table, through the data processing and analysis, rejects the abnormal data, prepares for the following processing. By analyzing the characteristics of member consumption and the difference between member and non-member consumption, we can provide marketing suggestions for the store manager FP-growth Algorithm is designed to evaluate the purchasing power of members based on their gender, length of membership, age and consumption frequency, and each parameter of the model is explained, so as to improve the management level of the shopping mall. On this basis, Suggestions for promotional activities in shopping malls are given.