Segmentasi Pelanggan Menggunakan Algoritma K-Means dan Analisis RFM di Ova Gaming E-Sports Arena Kediri
Submission Date: 2021-08-16 20:36:52
Accepted Date: 2021-12-22 11:51:20
Abstract
Selama sepuluh tahun berdiri, Ova Gaming E-sports Arena belum menerapkan strategi retensi pelanggan. Persaingan bisnis di daerah ini dapat dibilang cukup ketat, karena dalam radius 500 m terdapat dua kompetitor bisnis di bidang yang sama. Dengan semakin banyaknya e-sports arena di Kediri, Ova tentu harus melakukan perancangan strategi retensi pelanggan di samping meningkatkan kualitas layanan. Penelitian ini melaku-kan segmentasi pelanggan Ova Gaming E-Sport Arena mengguna-kan model RFM dan algoritma K-Means. Algoritma K-Means dipilih karena memiliki hasil clustering yang lebih baik diban-dingkan metode lainnya. Jumlah segmen optimum didapatkan dengan menggunakan metode Elbow dan Silhouette Coefficient. Dilakukan perhitungan Customer Live Value (CLV) dengan meng-gunakan bobot RFM perhitungan AHP untuk mengetahui urutan prioritas strategi retensi berdasarkan rata-rata CLV segmen terbesar. Setiap segmen pelanggan yang terbentuk selanjutnya dilakukan analisis karakteristik RFM, demografi, dan perilaku sebagai landasan penyusunan strategi retensi pelanggan. Melalui segmentasi pelanggan, diharapkan dapat menjadi upaya dalam meningkatkan pertumbuhan jangka panjang dan profitabilitas perusahaan dengan mengetahui menerapkan strategi retensi pelanggan yang tepat. Hasil penentuan jumlah segmen optimal menggunakan metode Elbow dan Silhouette Coefficient sebesar empat. Berdasarkan hasil tersebut, dalam penelitian ini digu-nakan segmen pelanggan sebesar empat. Berdasarkan analisis karakteristik, masing-masing segmen diurutkan sesuai hasil perhitungan CLV menggunakan pembobotan AHP diberi label superstar, everyday, occasional, dan dormant. Hasil analisis demo-grafi menggunakan atribut usia dan pekerjaan menghasilkan pelanggan usia muda dan berstatus pelajar sebagai target pasar utama perusahaan. Hasil analisis perilaku menunjukkan bahwa hari jumat dan sabtu sebagai waktu ramai. Berdasarkan ketiga hasil analisis yang telah dilakukan, strategi retensi pelanggan menghasilkan antara lain penawaran program loyalitas, pemberian reward, publisitas pemberlakuan protokol kesehatan, dan pemberian informasi layanan dan produk baru.
Keywords
Algoritma K-Means; Analytical Hierachy Process; Clustering; Model RFM; Segmentasi Pelanggan
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