Customer Purchase Patterns and Loyalty in MSME Catering Businesses Using the RFM Method

Authors

DOI:

https://doi.org/10.34306/itsdi.v7i2.724

Keywords:

RFM Analysis, Customer Segmentation, Customer Loyalty, MSME Catering, Data-Driven Marketing

Abstract

The increasing competition in the catering industry necessitates a data-driven understanding of consumer behavior to support effective customer retention strategies. This study aims to identify customer purchase patterns and loyalty in MSME Catering using the RFM (Recency, Frequency, Monetary) method based on transaction data from January 2025 to May 2026. This research addresses a gap in the literature regarding the application of transaction-based analytics for customer loyalty in MSMEs, which previously relied more on surveys or subjective perceptions. A total of 564 transactions from 80 unique customers were analyzed quantitatively, processed using Google Sheets, and visualized with Tableau. The results indicate that 55 customers (68.75%) are classified as loyal, while 25 customers (31.25%) are new customers. RFM segmentation grouped customers into Champions, Loyal Customers, Potential Loyalist, Need Attention, and Lost Customers, with the majority in the Potential Loyalist, Need Attention, and Lost segments. In addition to descriptive statistics, the relationship between purchase frequency and monetary value was examined to provide deeper insights into customer behavior. The findings demonstrate that the RFM approach provides a structured understanding of customer loyalty and transaction value, supporting the development of targeted marketing strategies, including loyalty programs, reminder promotions, reactivation campaigns, and customer engagement initiatives. This study contributes a practical RFM-based analytical framework applicable to MSMEs to enhance retention and marketing effectiveness.

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References

D. A. Zaltz, L. E. Bisi, G. Ruskin, and C. Hoe, “How independent is the international food information council from the food and beverage industry? a content analysis of internal industry documents,” Globalization and health, vol. 18, no. 1, p. 91, 2022.

Badan Pusat Statistik (BPS), “Food and beverage provision statistics 2023,” https://www.bps.go.id/id/publication/2024/12/23/f2c7743c4712aaeaa4abf694/statistik-penyediaan-makanan-dan-minuman-2023.html.

T. Handra and V. P. K. Sundram, “The effect of human resource information systems (hris) and artificial intelligence on defense industry performance,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 4, no. 2, pp. 155–163, 2023.

R. Aghaei, A. A. Kiaei, M. Boush, J. Vahidi, M. Zavvar, Z. Barzegar, and M. Rofoosheh, “Harnessing the potential of large language models in modern marketing management: Applications, future directions, and strategic recommendations,” arXiv preprint arXiv:2501.10685, 2025.

Y. A. Ajani, E. K. Adefila, S. A. Olarongbe, R. T. Enakrire, and N. Rabiu, “Big data and the management of libraries in the era of the fourth industrial revolution: implications for policymakers,” Digital Library Perspectives, vol. 40, no. 2, pp. 311–329, 2024.

I. Shantilawati, O. I. Suri, R. A. Sunarjo, S. A. Anjani, and D. Robert, “Unveiling new horizons: Ai-driven decision support systems in hrm-a novel bibliometric perspective,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 1, pp. 252–263, 2025.

S. Ahmadi, F. Barkhi, and S. Nikhashemi, “Uncovering consumer loyalty behavior: A data mining approach in the fast-moving consumer goods sector,” Journal of Retailing and Consumer Services, vol. 89, p. 104634, 2026.

F. Manes-Rossi and G. Nicolo’, “Exploring sustainable development goals reporting practices: From symbolic to substantive approaches—evidence from the energy sector,” Corporate Social Responsibility and Environmental Management, vol. 29, no. 5, pp. 1799–1815, 2022.

V. Meilinda, S. A. Anjani, and M. Ridwan, “A platform based business revolution activates indonesia’s digital economy,” Startupreneur Business Digital (SABDA Journal), vol. 2, no. 2, pp. 155–174, 2023.

G. Garc´ıa-Vidal, A. S´anchez-Rodr´ıguez, L. Guzm´an-Vilar, R. Mart´ınez-Vivar, and R. P´erez-Campdesu˜ner, “Exploring msme owners’ expectations of data-driven approaches to business process management,” Systems, vol. 13, no. 4, p. 265, 2025.

J. P. Pertiwi and A. U. Hana, “Data-driven decision making in msmes: Leveraging free analytics tools for financial planning and efficiency,” Journal of Management and Informatics, vol. 4, no. 1, pp. 633–648, 2025.

A. Haris, “Consumer behavior shifts in digital age: Impact on brand loyalty,” Advances: Jurnal Ekonomi & Bisnis, vol. 3, no. 1, pp. 38–51, 2025.

M. R. Anwar and L. D. Sakti, “Integrating artificial intelligence and environmental science for sustainable urban planning,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 179–191, 2024.

O. I. Kingsley, “Understanding customer buying patterns through business analytics.”

S. Roosta, S. J. Sadjadi, and A. Makui, “Predicting customer loyalty in omnichannel retailing using purchase behavior, socio-cultural factors, and learning techniques,” Plos One, vol. 20, no. 8, p. e0330338, 2025.

D. Y. Ryu, Y. K. Ko, and Y. D. Ko, “Rfm analysis for profiling profitable customers based on characteristics of the hotel industry,” International Journal of Hospitality Management, vol. 129, p. 104176, 2025.

F. P. Oganda, N. Lutfiani, Q. Aini, U. Rahardja, and A. Faturahman, “Blockchain education smart courses of massive online open course using business model canvas,” in 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2020, pp. 1–6.

C.-K. Hou, “Driving impulsive purchases and building loyalty: the power of experiential marketing in fast fashion apps,” Journal of Fashion Marketing and Management: An International Journal, vol. 30, no. 1, pp. 20–48, 2026.

A. J. Putra, T. K. Pertiwi, and D. Ichsanuddin, “Marketing mix and relationship marketing on customer loyalty with customer satisfaction as a mediation variable at bprs botani bina rahmah bogor,” Economics and Business Journal (ECBIS), vol. 3, no. 6, pp. 551–570, 2025.

V. Meilinda, L. W. Ming, M. Muhtarom, J. Zanubiya, M. R. Kusuma, and R. Yaputra, “Artificial intelligence and iot integration for intelligent decision-making systems,” Sundara Advanced Research on Artificial Intelligence, vol. 2, no. 1, pp. 1–13, 2026.

D. Jonas, E. Maria, I. R. Widiasari, U. Rahardja, T. Wellem et al., “Design of a tam framework with emotional variables in the acceptance of health-based iot in indonesia,” ADI Journal on Recent Innovation, vol. 5, no. 2, pp. 146–154, 2024.

A. Bakhrun, Y. Maghfyra, R. N. Putri, and D. A. Larassati, “Data visualization to analyze consumer behavior for strategic business decision making in the retail industry: Walmart case study,” CSRID (Computer Science Research and Its Development Journal), vol. 17, no. 3, pp. 354–371, 2025.

I. K. Himmy’az, S. Pranata, M. Yusup, H. Kusumah, J. Zanubiya, E. A. Natalia et al., “A blockchain based framework for improving data quality and predictive accuracy in business intelligence systems,” in 2025 4th International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2025, pp. 1–7.

J. Akter, A. Roy, S. Rahman, S. Mohona, and J. Ara, “Artificial intelligence-driven customer lifetime value (clv) forecasting: Integrating rfm analysis with machine learning for strategic customer retention,” Journal of Computer Science and Technology Studies, vol. 7, no. 1, pp. 249–257, 2025.

U. Rahardja, M. Miftah, M. Rakhmansyah, and J. Zanubiya, “Revolutionizing financial services with big data and fintech: A scalable approach to innovation,” ADI Journal on Recent Innovation, vol. 6, no. 2, pp. 118–129, 2025.

F. M. Wuaten, “The role of sustainable finance and technology at bank bjb in supporting the” sustainable development goals”,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 1Sp, pp. 97–108, 2023.

G. Ramkumar, J. Bhuvaneswari, S. Venugopal, S. Kumar, C. K. Ramasamy, and R. Karthick, “Enhancing customer segmentation: Rfm analysis and k-means clustering implementation,” in Hybrid and advanced technologies. CRC Press, 2025, pp. 70–76.

A. S. Bist, B. Rawat, U. Rahardja, Q. Aini, and A. G. Prawiyogi, “An exhaustive analysis of stress on faculty members engaged in higher education,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 3, no. 2, pp. 126–135, 2022.

G. Mar´ın D´ıaz, “A fuzzy-xai framework for customer segmentation and risk detection: Integrating rfm, 2-tuple modeling, and strategic scoring,” Mathematics, vol. 13, no. 13, p. 2141, 2025.

Y. Syahra, A. Fadlil, and H. Yuliansyah, “Customer segmentation using rfm and k-means clustering to support crm in retail industry,” Sinkron: jurnal dan penelitian teknik informatika, vol. 9, no. 3, pp. 1120–1131, 2025.

E. P. Lestari, S. D. W. Prajanti, F. Adzim, E. Primayesa, M. I. A.-B. Ismail, and S. L. Lase, “Understanding technopreneurship in agricultural e-marketplaces,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 369–389, 2024.

M. A. Wardana, A. Masliardi, N. Afifah, M. Sajili, and H. P. Kusnara, “Unlocking purchase preferences: Harnessing psychographic segmentation, promotion and location strategies,” Jurnal Informatika Ekonomi Bisnis, pp. 713–719, 2023.

M. Alves Gomes and T. Meisen, “A review on customer segmentation methods for personalized customer targeting in e-commerce use cases,” Information Systems and e-Business Management, vol. 21, no. 3, pp. 527–570, 2023.

L. P. Dewanti, L. Sitoayu, and A. Idarto, “Digital tele-counseling for sustainable maternal health services in indonesia focus on telelactation,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 6, no. 1, pp. 10–20, 2024.

A. Griva, E. Zampou, V. Stavrou, D. Papakiriakopoulos, and G. Doukidis, “A two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery data,” Journal of decision systems, vol. 33, no. 1, pp. 1–29, 2024.

K. Tabianan, S. Velu, and V. Ravi, “K-means clustering approach for intelligent customer segmentation using customer purchase behavior data,” Sustainability, vol. 14, no. 12, p. 7243, 2022.

E. Dolan, S. Kosasi, and S. N. Sari, “Implementation of competence-based human resources management in the digital era,” Startupreneur Business Digital (SABDA Journal), vol. 1, no. 2, pp. 167–175, 2022.

O. N. Akande, H. B. Akande, E. O. Asani, and B. T. Dautare, “Customer segmentation through rfm analysis and k-means clustering: Leveraging data-driven insights for effective marketing strategy,” in 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024, pp. 1–8.

A. Alsayat, “Customer decision-making analysis based on big social data using machine learning: a case study of hotels in mecca,” Neural Computing and Applications, vol. 35, no. 6, pp. 4701–4722, 2023.

M. D. Bauer and E. T. Swanson, “A reassessment of monetary policy surprises and high-frequency identification,” NBER Macroeconomics Annual, vol. 37, no. 1, pp. 87–155, 2023.

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Published

2026-04-19

How to Cite

Shokory, S. M., Wahab, N. N. A., Zanubiya, J., & Zainol, Z. (2026). Customer Purchase Patterns and Loyalty in MSME Catering Businesses Using the RFM Method. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 7(2), 188–197. https://doi.org/10.34306/itsdi.v7i2.724

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Articles