Penerapan Machine Learning untuk Pencarian Pelanggan Loyal Berpotensi Menggunakan Metode Python Pandas Seaborn
DOI:
https://doi.org/10.32502/integrasi.v10i1.292Keywords:
Prediksi, RFM, Pelanggan Loyal, Transaksi, AlgoritmaAbstract
Kunci kesuksesan berbisnis agar target penjualan tercapai dengan menjaga kepercayaan dan minat daya beli konsumen untuk tetap menjadi pelanggan setia. Salah satu faktor penunjangnya di bidang pemasaran yaitu dengan cara memprediksi untuk pencarian pelanggan loyal berpotensi. Untuk membangun model tersebut, perlu data demografi, sosial, transaksional, metrik perilaku, dan fitur pendukung lainnya. Masalah utama yang terjadi saat ini terbatasnya bagian pemasaran memiliki data pelanggan dan hanya mengandalkan informasi yang disediakan oleh sistem ERP yang sebagian besar datanya berorientasi transaksional. Tujuan utama dari penelitian ini adalah untuk mengusulkan kombinasi analisis RFM (Recency, Frequency and Monetary) dan algoritma machine learning untuk memprediksi potensi pelanggan berdasarkan sebagian besar data transaksional. Dataset diambil dari sistem ERP perusahaan sheet metal di PT.ABC. Skor RFM dihitung untuk setiap pelanggan dalam jangka waktu 6 bulan sebelum tanggal akhir pemeriksaan. Nilai target untuk model prediksi ini adalah metrik pelanggan berpotensi yang menunjukkan apakah pelanggan telah melakukan transaksi dalam 6 bulan ke depan setelah analisis RFM atau tidak. Eksperimen dilakukan dengan metode Python Pandas Seaborn. Hasil menunjukkan batasan skor dan metrik RFM menggunakan algoritma machine learning, perusahaan dapat memprediksi pelanggan loyal berpotensi. Skor terbaik ditunjukkan pelanggan platinum cenderung berbelanja lebih sering dan lebih banyak berbelanja dibandingkan pelanggan lain.
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