Hadi, Humaira Nurul (2025) Segmentasi Pelanggan Berdasarkan Variabel Recency, Frequency, dan Monetary (RFM) untuk Mengoptimalkan Strategi Pemasaran pada Penyedia Layanan E-Wallet. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dengan meningkatnya jumlah pelanggan pembayaran digital dan kompleksitas transaksi, diperlukan strategi yang dapat mengidentifikasi pelanggan bernilai tinggi serta meningkatkan efektivitas pemasaran. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan e-wallet berdasarkan customer lifetime value dan menganalisis jenis promosi yang tepat untuk masing-masing segmen berdasarkan hasil survei. Untuk mencapai tujuan tersebut, pertama digunakan algoritma K-means untuk melakukan klasterisasi pelanggan berdasarkan nilai variabel RFM (recency, frequency, dan monetary) pelanggan. Setiap klaster yang terbentuk akan diberi peringkat berdasarkan rata-rata nilai CLV (customer lifetime value) klaster. Hasil penelitian menunjukkan bahwa jumlah klaster optimal adalah empat, yang masing-masing mewakili segmen pelanggan. Berdasarkan nilai CLV segmen, dari tertinggi sampai terendah, berturut-turut adalah Loyal Users, Recent Users, High Potential Users, dan Dormant Users. Analisis terhadap data demografi dan perilaku menunjukkan bahwa pelanggan didominasi oleh karyawan berusia 26–40 tahun, dengan puncak aktivitas transaksi terjadi pada hari Senin, Kamis, dan Sabtu pukul 06.00–10.00 WIB. Survei preferensi promosi mengungkap bahwa promo Marketplace, F&B, dan Flash Sale memiliki tingkat penggunaan tertinggi, khususnya di segmen Loyal Users dan Recent Users. Sementara itu, Dormant Users dan High Potential Users menunjukkan potensi reaktivasi melalui promo Payday, Tagihan, dan Flash Sale. Strategi retensi yang disusun mencakup pendekatan umum seperti penguatan promosi pada high traffic days dan jam aktif, serta strategi spesifik per segmen berdasarkan preferensi dan perilaku masing-masing. Pendekatan ini memungkinkan perusahaan untuk menyusun strategi promosi yang lebih tepat sasaran dan memaksimalkan profitabilitas jangka panjang di tengah persaingan industri e-wallet yang semakin kompetitif.
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With the increasing number of digital payment users and the growing complexity of transactions, there is a need for strategies that can identify high-value customers and enhance marketing effectiveness. This study aims to segment e-wallet customers based on customer lifetime value (CLV) and to analyze the most suitable types of promotions for each segment based on survey results. To achieve this objective, the K-Means algorithm was used to cluster customers based on their RFM (recency, frequency, and monetary) values. Each resulting cluster was then ranked based on the average CLV within the group. The findings indicate that the optimal number of clusters is four, each representing a distinct customer segment. Based on CLV ranking from highest to lowest, the segments are Loyal Users, Recent Users, High Potential Users, and Dormant Users. Demographic and behavioral analysis reveals that most customers are employees aged 26–40 years, with peak transaction activity occurring on Mondays, Thursdays, and Saturdays between 6:00–10:00 AM. The promotion preference survey shows that Marketplace, F&B, and Flash Sale promotions are most frequently used, especially among Loyal and Recent Users. Meanwhile, Dormant and High Potential Users show reactivation potential through Payday, Bill Payment, and Flash Sale promotions. The retention strategy developed is divided into two which are general approach and targeted strategies. General strategies have approaches such as intensifying promotions during high-traffic days and peak hours. Targeted strategies are tailored to each segment based on their preferences and behaviors. This approach enables the company to design more precise marketing campaigns and maximize long-term profitability amid the increasingly competitive e-wallet industry.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | CLV, Customer Relationship Management, K-Means, RFM, Segmentasi Pelanggan =========================================================== CLV, Customer Relationship Management, K-Means, RFM, Segmentasi Pelanggan |
| Subjects: | T Technology > T Technology (General) T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T58.8 Productivity. Efficiency T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26201-(S1) Undergraduate Thesis |
| Depositing User: | Humaira Nurul Hadi |
| Date Deposited: | 28 Jul 2025 10:20 |
| Last Modified: | 28 Jul 2025 10:20 |
| URI: | http://repository.its.ac.id/id/eprint/122381 |
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