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Journal : Journal of Applied Data Sciences

Integration of Sentiment Analysis and RFM in Restaurant Customer Segmentation: A 7P-Based CRM Model with Clustering Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Rachmawati, Rina
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.633

Abstract

The increasing use of digital platforms like Tripadvisor has created opportunities to transform customer review data into strategic insights for Customer Relationship Management (CRM). This study proposes a novel CRM model by integrating the Recency, Frequency, Monetary (RFM) framework with the 7P marketing mix to segment restaurant customers more effectively. Using 3,716 Tripadvisor reviews, annotated based on 7P elements and clustered through unsupervised learning, three key customer segments were identified: acquisition, retention, and win-back. Evaluation metrics show strong clustering performance with a Silhouette Score of 0.73 and a Davies-Bouldin Score of 0.08. The acquisition cluster (Product) demonstrates the highest Frequency (37,664) and Monetary value (64.94), signifying high engagement and revenue potential. The retention cluster (Physical Evidence, Place, Process, Promotion, Traveler) shows stable interaction patterns with Recency values of 1261–1262 and moderate Frequency (378–2,079). The win-back cluster (Price, People) reflects lower Frequency (198–946) but equal Recency (1259), indicating recent but infrequent activity, which is ideal for reactivation strategies. By mapping customer reviews to 7P labels and analyzing them using RFM, the model uncovers specific behavioral patterns tied to service quality, pricing, and promotions. This integration allows restaurants to apply tailored strategies: offering loyalty rewards to high-frequency customers, promotional incentives for those with high Recency, and prioritizing high-monetary customers for exclusive programs. The novelty of this research lies in its combined use of sentiment-based review analysis and RFM–7P segmentation, offering a scalable, data-driven framework for enhancing customer satisfaction, loyalty, and long-term business growth in the restaurant industry.
Co-Authors Abd. Muqit Adani, Salma Putri Ade Novi Nurul Ihsani Adhi Kusumastuti AHMAD, RORO HASINA Aminudin Afandhi Anindya Ardiansari Awitya Anggara Prabawadi Ayu Rosmayuningsih Ayu Sulasari Azizah, Aurora Sylva Brilianta Nurul B, Fitriyah Bachtiar Rachmad Sugiyono Bambang Sugeng Suryatna, Bambang Sugeng Bambang Tri Rahardjo Budi Sunarko Damayanti, Afifah Indira Dyah Nugraheny Priastuti Eka Listianing Rahayu Evana Nuzulia Pertiwi Fadhila Herdatiarni Falah, Sobah Al Fedy Setyo Pribad Fernando, Ito Firdaus, Fauzan Fitriyah, Vebriyanti Gatot Mudjiono Godham Eko Saputro Hadameon, Bagas Hapsari, Femita Hasanah, Nely Uswatun Hastawulan, Anastassia Wita Aryandini Hendra Dedi Kriswanto, Hendra Dedi Hufroni, Muhammad Ifa Nurhayati Karina, Anisya Putri Kristanti, Bela Siska Kusumastuti , Adhi Ludji Pantja Astuti Luqman Qurata Aini Mahmudi, Zaid Maulidina, Ahsani Muhidin, Ahmad Musdalifah Musdalifah Musdalifah Musdalifah Nindya Resha Pramesti Novitasari, Siska Octavianti Paramita Pamungkas, Bayu Aji Prastiwi, Arlinda Bayu Putri, Silvia Nouvelia Rahmawati, Meisa Retno Dyah Puspitarini Retno Sri Iswari Rosidah Rosidah Rumrapuk, Juanita T D Sakti, Adinda Oktaviani Sandy, Yohana Avelia Saptariana - Saptiyani, Annisa Dwi Setiyaningsih, Mitta Setyari, Fidya Peni Sicilia Sawitri Sicilia Sawitri Sigit Rahmansah Sinatrya, Jatrifia Ongga Siti Fathonah Siti Fathonah Sri Endah Wahyuningsih Sri Ria Vidia Antika Sulistyaningrum, Bety Syahroni Hidayat Syib’li, Muhammad Akhid Tita Widjayanti Toto Haryadi, Toto Toto Himawan Tri Astuti Handayani Unarto, Tirto Uswatun Hasanah Vallent, Ellyta Fernanda Dwi Wibowo, Muhammad Yusuf Wicaksono, Athur Wahyu Wulansari Prasetyaningtyas, Wulansari Wulansari Prawetyaningtyas, Wulansari Yogo Setiawan