SEGMENTASI PELANGGAN MENGGUNAKAN K-MEANS CLUSTERING  DI TOKO RETAIL

Syifa Latifah Achmad; Ahmad Fauzi; Rahmat Rahmat; Jamaludin Indra

doi:10.37600/tekinkom.v7i2.1226

Syifa Latifah Achmad Universitas Buana Perjuangan
Ahmad Fauzi Universitas Buana Perjuangan
Rahmat Rahmat Universitas Buana Perjuangan
Jamaludin Indra Universitas Buana Perjuangan

DOI: https://doi.org/10.37600/tekinkom.v7i2.1226

Abstract

The accumulated customer data can be processed to become a reference for a store's marketing strategy. In addition, understanding and recognizing customer characteristics can help create communication in order to offer products and recommend products that suit the needs of each customer, so that customers feel happy and comfortable shopping at the store. So far, customer data is only stored as an archive without knowing the benefits that can be taken from these customer data. In order to improve promotions to attract new customers and retain old customers, customer segmentation is needed to be able to group customers according to their characteristics and shopping behavior. This research uses customer data from Dan+Dan Telukjambe 2 store which has 2000 rows with 8 attributes. The data is grouped using the K-Means algorithm and the Elbow method in providing the best number of clusters. The number of clusters given by the Elbow method is k = 4, 5, and 6, then evaluated using the Silhouette Coefficient and gives the result that k = 6 is the most optimal cluster with the Silhouette Coefficient result almost close to 0, which is 0.3722797110917646. So that the customer data is grouped into 6 clusters, namely cluster 0, cluster 1, cluster 2, cluster 3, cluster 4, cluster 5, cluster 6. These results can be a reference in developing marketing. These results can be used as a reference in developing a marketing strategy that suits the characteristics of customers according to the existing cluster.

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