Revitalizing Corporate Customer Loyalty Through the Integration of RFM Analysis and Ethnography: Data-Based Retention Strategies at Pt Indonesia Comnets Plus (ICON+) for the Period 2024–2025
DOI:
https://doi.org/10.46799/adv.v4i9.635Keywords:
RFM analysis, ethnography, customer segmentation, churn, customer retentionAbstract
This study examined the decline in customer attachment to PT Indonesia Comnets Plus (ICON+), a telecommunications subsidiary of PLN, by integrating Recency-Frequency-Monetary (RFM) analysis and ethnographic segmentation with transaction data and corporate customer deactivation records from 2024 and 2025. Using SPSS-based RFM scoring of 518 customers in 2024 and 482 customers in 2025, along with ethnographic coding of 1,761 transaction records involving 940 unique accounts, the study found that although the active customer base decreased by 7.0%, the total transaction value declined substantially from approximately IDR 47.2 billion to IDR 5.7 billion. Furthermore, 17 of the 60 retained customers (28.3%) experienced an average decrease of 2.24 points in their RFM scores, equivalent to the impact of negative transactions amounting to approximately IDR 2.8 billion. The ethnographic analysis identified two structural customer segments: Industrial/National Account customers (44.4%) and Regional/SME customers (55.6%), with customer churn primarily driven by administrative cycles (41.3%) and budget sensitivity (31.1%). Five priority accounts with significant declines in Recency scores (from 3 to 1) were analyzed in depth to formulate an Online Value Proposition (Smart Continuity Partner Portal), four SMART tactics, and operational work programs mapped within the 8Ps service marketing framework. The study concluded that the integration of quantitative RFM segmentation and qualitative ethnographic pain point analysis provided a stronger foundation for corporate customer retention strategies than the application of either method independently.
References
Anwar, S. (2026). Strategic recovery responses to service failures in Pakistan’s public banks. University of Gloucestershire.
Bortey, L. (2025). Predictive modelling of incident risk to pre-empt risk in highway operations: A machine learning approach. Birmingham City University.
Booms, B. H., & Bitner, M. J. (1981). Marketing strategies and organization structures for service firms. In J. Donnelly & W. R. George (Eds.), Marketing of services (pp. 47–51). American Marketing Association.
Carvalho, H., & Maciel, F. (2026). RFM-based customer segmentation: A pedagogical case study for marketing analytics education. Journal for Advancement of Marketing Education, 34(1).
Gbadamosi, A., Sani, K. F., Al Abdulrazak, R. M., & Salem, S. (2026). The nexus of internal marketing, workplace dynamics, and consumer value co-creation for contemporary corporate branding. In Auto-psychology, psychological marketing, well-being, and the workplace: Implications for theory and practice (pp. 7–44). Springer.
Heikal, J. (2024). Customer segmentation based on RFM analysis as the basis of marketing strategy: Case study of crude palm oil product in palm oil industry. Enrichment: Journal of Multidisciplinary Research and Development, 2(8). https://doi.org/10.55324/enrichment.v2i8.186
Hughes, A. M. (1994). Strategic database marketing: The masterplan for starting and managing a profitable customer-based marketing program. Probus Publishing.
Lovelock, C., & Wirtz, J. (2016). Services marketing: People, technology, strategy (8th ed.). World Scientific Publishing.
Madjid, R., & Yusuf, H. (2025). The effect of service innovation, corporate image, and customer loyalty on human capital strategy and business performance moderated by the external environment at Bank Sultra in Southeast Sulawesi. Architecture Image Studies (AIS), 6(4), 1374.
Majeed, M., Chaudhary, A., & Chadha, R. (2025). Digital transformation in the customer experience. CRC Press.
Mar, J., & Armaly, P. (2024). Mastering customer success: Discover tactics to decrease churn and expand revenue. Packt Publishing Ltd.
Morgeson, F. V., III, Hult, G. T. M., Mithas, S., Keiningham, T., & Fornell, C. (2020). Turning complaining customers into loyal customers: Moderators of the complaint handling–customer loyalty relationship. Journal of Marketing, 84(5), 79–99.
Nisa, K., & Heikal, J. (2023). Manja Beauty Skincare customer segmentation strategy using the analytical RFM model. Student Journal of Computer Science.
Passalaras, R. A., Daulay, R. Y., & Heikal, J. (2024). Segmentation strategy of consumer interest in contemporary coffee shop using RFM model. BUDGETING: Journal of Business, Management and Accounting, 5(2), 741–756.
Perdhana, R. B., & Heikal, J. (2024). Enhancing customer segmentation in online transportation services: A comprehensive approach using K-Means clustering and RFM model.
Putra, F. A. (2023). Navigating the development of battery electric vehicle business ecosystem in Indonesia: Business strategy and scenario planning for PLN Icon Plus.
Rajendran, N. (2025). Enhancing customer segmentation and behaviour analysis with RFM clustering: A machine learning approach. National College of Ireland.
Rohman, I. K., Naufal, M. M., & Naufal, I. (2025). Indonesian service sector review: Telecommunications. Economic Research Institute for ASEAN and East Asia (ERIA).
Schlimm, J. (2026). Customer engagement strategy: Using emerging technologies to transform the customer journey. Kogan Page Publishers.
Singh, S., & Singh, S. (2016). Accounting for risk in the traditional RFM approach. Management Research Review, 39(2), 215–234.
Spradley, J. P. (1979). The ethnographic interview. Holt, Rinehart and Winston.
Van der Borgh, M. (2023). Preventing churn behavior in SaaS startups: An exploratory multiple case study of Danish startups.
Zhang, Q., Zhang, L., & Chai, Y. (2026). An enhanced multi-task deep learning framework for joint prediction of customer churn and downsell. Applied Sciences, 16(8), 4014.
