Analisa Algoritma K-Means Untuk Menentukan Strategi Marketing - Bias Yulisa Geni; Okto Kurnia; Nova Hayati; Muhammad Thoriq; Kiki Hariani Manurung (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2024)
This research explored the application of the K-Means algorithm in the marketing field to increase the effectiveness of ABC Cosmetic Store marketing strategies. Sales data can be processed using data mining to be used as decision making at ABC Cosmetic Stores. One of the techniques in data mining is Clustering, which is used to categorize data. The K Means algorithm is used to identify hidden patterns in the data. By using bodylotion sales data, this research aims to classify consumers into several groups. The data group in question is sales data that is of great interest to consumers and data that is of little interest to consumers. The results of clustering using k=2 show that cluster 1 consists of 5 products with product transactions sold being 1295 products. In this case, it shows that cluster 1 is a group of product data whose quantity sold has increased and can provide profits at the ABC Cosmetic Store. Meanwhile, cluster 2 has 1 data with 214 product transactions sold and is grouped as data with products that are less popular with consumers so there is no need to increase the stock available in the warehouse by ABC Cosmetic Shop. The results of this research show that K Means-based customer segmentation can increase personalization in marketing communications and increase the efficiency of marketing resource allocation. This study provides new insights into how data mining techniques can be involved in marketing strategies to determine product availability for the future.