MARKET-DRIVEN STRATEGY DENGAN PEMANFAATAN BIG DATA ANALYTIC TOOLS: KLASTERISASI UNTUK OPTIMALISASI STRATEGI PEMASARAN DI SOULJA COFFEE

    Melani Defina Maharani, - and Adam Hermawan, - and Muhammad Rizki Nugraha, - (2025) MARKET-DRIVEN STRATEGY DENGAN PEMANFAATAN BIG DATA ANALYTIC TOOLS: KLASTERISASI UNTUK OPTIMALISASI STRATEGI PEMASARAN DI SOULJA COFFEE. S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    Penelitian ini bertujuan untuk mengoptimalkan strategi pemasaran pada Soulja Coffee melalui penerapan pendekatan market-driven strategy berbasis data. Dalam konteks persaingan bisnis kopi yang semakin kompetitif, pemahaman terhadap perilaku konsumen menjadi kunci utama dalam menyusun strategi pemasaran yang efektif dan efisien. Dengan memanfaatkan data transaksi pelanggan yang dihimpun melalui sistem CRM, penelitian ini menerapkan metode K-Means Clustering untuk melakukan segmentasi konsumen serta algoritma Decision Tree untuk mengidentifikasi karakteristik utama dari masing-masing klaster. Hasil segmentasi menunjukkan terbentuknya empat klaster pelanggan dengan pola perilaku yang berbeda, seperti jenis produk, waktu pembayaran, jenis order, dan metode pembayaran. Decision Tree digunakan untuk mengevaluasi akurasi segmentasi dan menghasilkan aturan-aturan klasifikasi yang mudah diinterpretasikan. Penelitian ini memberikan rekomendasi strategi pemasaran 4P pada keseluruhan segmen guna meningkatkan efisiensi biaya pemasaran dan loyalitas pelanggan. Hasil penelitian membuktikan bahwa pemanfaatan big data dan algoritma machine learning dapat memberikan insight yang signifikan dalam merancang strategi pemasaran yang lebih adaptif dan berbasis pada perilaku konsumen aktual. Pendekatan ini relevan untuk diterapkan oleh pelaku usaha UMKM di era digital. This study aims to optimize the marketing strategy of Soulja Coffee through the application of a data-driven market strategy approach. In the context of an increasingly competitive coffee business landscape, understanding consumer behavior becomes a key factor in formulating effective and efficient marketing strategies. By utilizing customer transaction data collected through the CRM system, this research applies the K-Means Clustering method to segment consumers and the Decision Tree algorithm to identify the key characteristics of each cluster. The segmentation results reveal the formation of four distinct customer clusters, differentiated by behavioral patterns such as product type, payment time, order type, and payment method. The Decision Tree is also employed to evaluate the segmentation accuracy and generate classification rules that are easy to interpret. This study proposes a unified 4P marketing strategy for all segments to improve marketing cost efficiency and customer loyalty. The findings demonstrate that leveraging big data and machine learning algorithms can provide significant insights in designing adaptive marketing strategies based on actual consumer behavior. This approach is highly relevant for MSME businesses operating in the digital era.

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    Official URL: https://repository.upi.edu/
    Item Type: Thesis (S1)
    Additional Information: https://scholar.google.com/citations?view_op=new_profile&hl=id ID SINTA Dosen Pembimbing Adam Hermawan: 6681172 Muhammad Rizki Nugraha: 6770726
    Uncontrolled Keywords: Market-Driven Strategy, K-Means Clustering, Decision Tree, Data-Driven Marketing, Soulja Coffee, Segmentasi Konsumen Market-Driven Strategy, K-Means Clustering, Decision Tree, Data-Driven Marketing, Soulja Coffee, Customer Segmentation
    Subjects: L Education > L Education (General)
    Divisions: UPI Kampus Tasikmalaya > S1 Bisnis Digital
    Depositing User: Melani Defina Maharani
    Date Deposited: 13 Aug 2025 04:14
    Last Modified: 13 Aug 2025 04:14
    URI: http://repository.upi.edu/id/eprint/135425

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