Customer Clustering Using The K-Means Clustering Algorithm in Shopping Mall in Indonesia - Iksan Adityo Mulyo; Jerry Heikal (Management Analysis Journal, 2022)
interest in purchasing a product is difficult because the number of visitors varies. As a marketing plan strategy, it is supposed to market the new product similar to customers' interests. This study aims to identify the customer's characteristics, product categories and target segmentation in the shopping mall using the K-Means Clustering algorithm. There were ten variables, age, sex, annual income, spending score, marital status, home location, work location, product interest, social media and children. The application of SPSS V.25 assisted data processing. The total population in this study were 200 participants. In this case, there were 3 clusters with each persona. First Cluster was Gadget Lovers, the second Cluster was Beauty Influencer, and 3rd Cluster was Mature Beauty. The target segment was the customer with profile men & women ages 29 and 40 years old and annual incomes IDR 65,000,000 and 87,000,000, with Instagram as their favorite social media. The product that should be more focused on was Gadget (hand phone & Tablet + accessories) since 1st Cluster (Gadget Lovers) & 2nd the Cluster (Beauty Influencers), who had a total of 58% of the total population, was likely to buy that product in Shopping Mall. Based on the author's buyer persona, Online Valuable Proposition (OVP) for this product is Trusted.