Customer Segmentation Based On Age, Gender, Income, And Spending Score At Mall Abc Using The K-Means Clustering Model With SPSS Program

Abstract

Due to covid 19 pandemic, retail market has been rapidly changing and malls have been impacted quite significantly ever since. Therefore, it is important for malls’ management to understand the characteristics of their customers better in order to develop effective marketing strategies. Customer segmentation (STP) is a good and simple method that can be used to understand customer characteristics. The objective of this research is to create a customer segmentation model (STP) using the K-Means Clustering algorithm on Mall ABC customer profile data. This research utilizes Mall ABC’s two hundred customer profile data which includes variables such as age, gender, income, and spending score. Data is analysed using the K-Means Clustering algorithm to divide customers into six groups. The persona of these six groups are average housewife, older housewife, successful working female, young male entrepreneur, average working class, and female supervisor. The most strategic target segment is female supervisor. This segment can be attracted by inviting more tenants that are suitable for their needs and wants such as mid-end cafes, restaurants, female clothing stores, and children apparel stores. Creating cross-merchant promotions and point redemption programs can also create better experience and increase customer loyalty. The results of this segmentation can be a reference for Mall ABC to develop a more targeted and effective marketing strategy. Malls can focus on offering products and services that suit the needs and characteristics of each customer segment.