This study explores the integration of data mining, customer relationship management (CRM), and strategic management to enhance the understanding of customer behavior and drive revenue growth. The main goal is the use of application of data mining techniques in customer analytics, focusing on the Extended RFM (Recency, Frequency, Monetary Value and count day) model within the context of online retailing. The Extended RFM model enhances traditional RFM analysis by incorporating customer demographics and psychographics to segment customers more effectively based on their purchasing patterns. The study further investigates the integration of the BCG (Boston Consulting Group) matrix with the Extended RFM model to provide a strategic view of customer purchase behavior in product portfolio management. By analyzing online retail customer data, this research identifies distinct customer segments and their preferences, which can inform targeted marketing strategies and personalized customer experiences. The integration of the BCG matrix allows for a nuanced understanding of which segments are inclined to purchase from different categories such as “stars” or “cash cows,” enabling businesses to align marketing efforts with customer tendencies. The findings suggest that leveraging the Extended RFM model in conjunction with the BCG matrix can lead to increased customer satisfaction, loyalty, and informed decision-making for product development and resource allocation, thereby driving growth in the competitive online retail sector. The findings are expected to contribute to the field of Infrastructure Finance by providing actionable insights for firms to refine their strategic policies in CRM.

Alghanam, O. A., Al-Khatib, S. N., & Hiari, M. O. (2022). Data Mining Model for Predicting Customer Purchase Behavior in E-Commerce Context. International Journal of Advanced Computer Science and Applications, 13(2). https://doi.org/10.14569/ijacsa.2022.0130249

Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34(5), 1785–1792. https://doi.org/10.1016/j.jksuci.2019.12.011

Asmat, F., Suryadi, K., & Govindaraju, R. (2023). Data mining framework for the identification of profitable customer based on recency, frequency, monetary (RFM). In: AIP Conference Proceedings. https://doi.org/10.1063/5.0130290

Audzeyeva, A., Summers, B., & Schenk-Hoppé, K. R. (2012). Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective. International Journal of Forecasting, 28(2), 507–518. https://doi.org/10.1016/j.ijforecast.2011.05.005

Bali, S., Bali, V., Gaur, D., et al. (2023). A framework to assess the smartphone buying behaviour using DEMATEL method in the Indian context. Ain Shams Engineering Journal, 102129. https://doi.org/10.1016/j.asej.2023.102129

Bellou, V., & Andronikidis, A. (2008). The impact of internal service quality on customer service behaviour. International Journal of Quality & Reliability Management, 25(9), 943–954. https://doi.org/10.1108/02656710810908098

Ben Ncir, C. E., Ben Mzoughia, M., Qaffas, A., et al. (2023). Evolutionary multi-objective customer segmentation approach based on descriptive and predictive behaviour of customers: application to the banking sector. Journal of Experimental & Theoretical Artificial Intelligence, 35(8), 1201–1223. https://doi.org/10.1080/0952813x.2022.2078886

Chayjan, M. R., Bagheri, T., Kianian, A., et al. (2020). Using data mining for prediction of retail banking customer’s churn behaviour. International Journal of Electronic Banking, 2(4), 303. https://doi.org/10.1504/ijebank.2020.114770

Chen, Y., Liu, L., Zheng, D., et al. (2023). Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model. Journal of Retailing and Consumer Services, 74, 103433. https://doi.org/10.1016/j.jretconser.2023.103433

Corbos, R. A., Bunea, O. I., & Breazu, A. (2023). Influence of online consumer reviews on the sales of large household appliances: a survey in Romania. Electronic Commerce Research. https://doi.org/10.1007/s10660-023-09758-6

Corbos, R. A., Bunea, O.-I., & Triculescu, M. (2023). Towards Sustainable Consumption: Consumer Behavior and Market Segmentation in the Second-Hand Clothing Industry. Amfiteatru Economic, 25(Special 17), 1064. https://doi.org/10.24818/ea/2023/s17/1064

Cui, J. (2023). Deep Mining Algorithm of Online Purchase Behavior Data Based on Decision Tree Model. Journal of Testing and Evaluation, 51(3), 1398–1407. https://doi.org/10.1520/jte20220094

Gholamiangonabadi, D., Shahrabi, J., Hosseinioun, S. M., et al. (2019). Customer Churn Prediction Using a New Criterion and Data Mining; A Case Study of Iranian Banking Industry. In: Proceedings of the International Conference on Industrial Engineering and Operations Management.

Gholamveisy, S. (2021a). Discovering hidden cluster structures in citizen complaint call via Som and association rule technique. Journal of Mechanics of Continua and Mathematical Sciences, 16, 79–92. https://doi.org/10.26782/jmcms.2021.07.00007

Gholamveisy, S. (2021b). Gasoline consumption prediction via data mining technique. Journal of Mechanics of Continua and Mathematical Sciences, 16, 74–84. https://doi.org/10.26782/jmcms.2021.09.00007

Gholamveisy, S., Momen, A., Hatami, M., et al. (2023). The effect of perceived social media marketing activities on brand loyalty. Apuntes Universitarios, 13(3), 105–118. https://doi.org/10.17162/au.v13i3.1374

Gull, M., & Pervaiz, A. (2018). Customer Behavior Analysis Towards Online Shopping using Data Mining. 2018 5th International Multi-Topic ICT Conference (IMTIC). https://doi.org/10.1109/imtic.2018.8467262

Hambrick, D. C., MacMillan, I. C., & Day, D. L. (1982). Strategic Attributes and Performance in the BCG Matrix—A PIMS-Based Analysis of Industrial Product Businesses. Academy of Management Journal, 25(3), 510–531. https://doi.org/10.2307/256077

Kazmi, S. H. A., Ahmed, R. R., Soomro, K. A., et al. (2021). Role of Augmented Reality in Changing Consumer Behavior and Decision Making: Case of Pakistan. Sustainability, 13(24), 14064. https://doi.org/10.3390/su132414064

Khade, A. A. (2016). Performing Customer Behavior Analysis using Big Data Analytics. Procedia Computer Science, 79, 986–992. https://doi.org/10.1016/j.procs.2016.03.125

Khalili-Damghani, K., Abdi, F., & Abolmakarem, S. (2018). Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries. Applied Soft Computing, 73, 816–828. https://doi.org/10.1016/j.asoc.2018.09.001

Larsen, N. M., Sigurdsson, V., & Breivik, J. (2017). The Use of Observational Technology to Study In-Store Behavior: Consumer Choice, Video Surveillance, and Retail Analytics. The Behavior Analyst, 40(2), 343–371. https://doi.org/10.1007/s40614-017-0121-x

Luna-Romera, J. M., del Mar Martinez-Ballesteros, M., Garcia-Gutierrez, J., et al. (2016). An approach to silhouette and dunn clustering indices applied to big data in spark. In: Proceedings of the Advances in Artificial Intelligence: 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016; 14–16 September 2016; Salamanca, Spain.

Martínez, R. G., Carrasco, R. A., Sanchez-Figueroa, C., et al. (2021). An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business. Mathematics, 9(16), 1836. https://doi.org/10.3390/math9161836

Ncir, C. E. B., Hamza, A., & Bouaguel, W. (2021). Parallel and scalable Dunn Index for the validation of big data clusters. Parallel Computing, 102, 102751. https://doi.org/10.1016/j.parco.2021.102751

Sharma, A., Pratap, A., Vyas, K., et al. (2022). Machine Learning Approach: Consumer Buying Behavior Analysis. 2022 IEEE Pune Section International Conference (PuneCon). https://doi.org/10.1109/punecon55413.2022.10014928

Solomon, M., Russell-Bennett, R., & Previte, J. (2012). Consumer behaviour: Pearson Higher Education AU. In: Australia.

Taşabat, S. E., Özçay, T., Sertbaş, S., & Akca, E. (2023). A new RFM model approach: RFMS. In: Industry 4.0 and the Digital Transformation of International Business. Springer; pp. 143–172.

Vasiljeva, T., Kreituss, G., & Kreituss, I. (2021). The implications of customer behaviour for banking service management: evidence from Latvia. Reliability and Statistics in Transportation and Communication. In: Proceedings of the 20th International Conference on Reliability and Statistics in Transportation and Communication, RelStat2020; 14–17 October 2020; Riga, Latvia.

Zhang, Y. (2023). Cluster analysis of perceptual demands of users’ internet consumption behaviours based on improved RFM model. International Journal of Web Based Communities, 19(1), 15. https://doi.org/10.1504/ijwbc.2023.128408