Fauzi, Muhammad Ihsan and Dr.Dian Eka Ratnawati,, S.Si., M.Kom and Bayu Rahayudi, S.T., M.M. (2024) Segmentasi Pelanggan Menggunakan Algoritma KMEANS CLUSTERING Dan Model RFM (STUDI KASUS ZIVAA BEAUTY JAKARTA). Sarjana thesis, Universitas Brawijaya.
Abstract
Zivaa Beauty adalah perusahaan yang berfokus pada bidang kecantikan. Saat ini, Zivaa Beauty diketahui belum pernah menerapkan strategi Customer Relationship Management atau CRM untuk melakukan segmentasi pelanggan yang bertujuan meningkatkan penjualan dan menjaga loyalitas pelanggan. Segmentasi pelanggan adalah proses pengelompokan konsumen berdasarkan karakteristik yang sama. Penelitian ini memfokuskan pada karakteristik pelanggan menggunakan model RFM, yang mengukur Recency (waktu terakhir transaksi), Frequency (jumlah transaksi), dan Monetary (total pengeluaran). Algoritma KMeans Clustering digunakan sebagai metode segmentasi, mengorganisir data ke dalam cluster berdasarkan kesamaan karakteristik. Jumlah cluster optimal ditentukan melalui grafik Elbow kemudian diuji dengan Davies Bouldin Index dan Silhoutte Score yang mendapatkan nilai pengujian yaitu 0.868 dan yaitu 0.506. Hasil dari pengujian menunjukkan bahwa empat cluster adalah jumlah optimal. Penelitian root cause analysis dilakukan dengan cara mengambil sampel pelanggan dari setiap cluster sebesar 20% dan meneliti berdasarkan nilai RFM-nya. Pencarian kemungkinan akar permasalahan dilakukan dengan metode observasi, kemudian akar permasalahan tersebut akan dikelompokkan berdasarkan kategori high recency, low frequency, dan low monetary. Akar permasalahan yang telah dikelompokkan berdasarkan kategori tersebut telah divalidasi oleh stakeholder. Informasi segmentasi hasil clustering disajikan dalam bentuk dashboard di Google Data Studio.
English Abstract
Zivaa Beauty is a company specializing in the beauty industry. It is known for not having implemented a Customer Relationship Management (CRM) strategy to segment customers, which is aimed at boosting sales and retaining customer loyalty. Customer segmentation is the process of classifying consumers based on shared characteristics. This research focuses on customer characteristics analyzed through the RFM model, which assesses Recency (time since last transaction), Frequency (number of transactions), and Monetary (total spending). The K-Means Clustering algorithm is employed for segmentation, organizing data into clusters of similar characteristics. The optimal cluster number is identified using the Elbow graph, further evaluated with the Davies Bouldin Index and Silhouette Score, yielding values of 0.868 and 0.506, respectively. Test results confirm that the ideal number of clusters is four. Root cause analysis is conducted by sampling 20% of customers from each cluster and reviewing them based on their RFM values. The identification of potential root causes is performed using the observation method, subsequently categorizing root causes into groups of high recency, low frequency, and low monetary. These categorized root causes are validated by stakeholders. Information on clustering segmentation is displayed through a dashboard in Google Data Studio.
| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | 0524150183 |
| Uncontrolled Keywords: | Segmentasi pelanggan, K-Means Clustering, model RFM, Root Cause Analysis, Dashboard-Customer segmentation, K-Means Clustering, RFM model, Root Cause Analysis, Dashboard |
| Divisions: | Fakultas Ilmu Komputer > Teknologi Informasi |
| Depositing User: | soegeng sugeng |
| Date Deposited: | 28 Feb 2024 08:10 |
| Last Modified: | 28 Feb 2024 08:10 |
| URI: | http://repository.ub.ac.id/id/eprint/216110 |
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