References
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
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