Analisis Segmentasi Pelanggan Berbasis RFM dan Evaluasi Efektivitas Kampanye Pemasaran untuk Meningkatkan Retensi
DOI:
https://doi.org/10.61132/neptunus.v2i4.400Keywords:
RFM Analysis, Customer Segmentation, Marketing Campaign, Customer Retention, ROIAbstract
This research implements RFM (Recency, Frequency, Monetary) analysis to perform customer segmentation and evaluate the effectiveness of marketing campaigns in a retail company. Using a Kaggle dataset, this study identifies customers based on purchasing behaviour and assesses marketing campaign responses for each segment. The analysis reveals that Loyal, VIP, and New Customer segments showed the highest responses, especially in Campaign 6. The findings emphasize the importance of targeting resources on effective segments and campaigns to optimize marketing strategies and maximize ROI. Personalized campaigns based on segmentation can enhance customer retention and align product offerings with customer needs.
References
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79-95. https://doi.org/10.1016/j.jretai.2016.11.002
Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. https://doi.org/10.1509/jmkr.2005.42.4.415
Hermawan, A., Kahfi, R. A., Surya, E., Aini, U., & Hidayat, R. (2024). Penerapan metode RFM dengan Python dalam segmentasi pelanggan. Jurnal Bisnis Inovatif dan Digital, 1(3), 92–102. https://doi.org/10.61132/jubid.v1i3.222
Hughes, A. M. (1994). Strategic database marketing. Probus Publishing.
Kannan, P. K., & Li, H. A. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22-45. https://doi.org/10.1016/j.ijresmar.2016.11.006
Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3, 57-63. https://doi.org/10.1016/j.procs.2010.12.011
Kotler, P., & Armstrong, G. (2018). Principles of marketing (17th ed.). Pearson Education Limited.
Kumar, V., Ramachandran, D., & Kumar, B. (2020). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 125, 864-877. https://doi.org/10.1016/j.jbusres.2019.08.034
Miguéis, V. L., Van den Poel, D., Camanho, A. S., & Cunha, J. F. (2012). Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. Advances in Data Analysis and Classification, 6(4), 337-353. https://doi.org/10.1007/s11634-012-0121-3
Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109-127. https://doi.org/10.1509/jmkg.68.1.109.24030
Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4), 106-125. https://doi.org/10.1509/jmkg.68.4.106.42728
Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13(3), 247-252. https://doi.org/10.1177/1094670510375461
Wedel, M., & Kamakura, W. A. (2012). Market segmentation: Conceptual and methodological foundations (Vol. 8). Springer Science & Business Media.
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
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