Xây dựng mô hình dự đoán điểm chạm của khách hàng dựa trên dữ liệu hành trình mua sắm trực tuyến
Nội dung chính của bài viết
Tóm tắt
Nghiên cứu sử dụng hệ thống gợi ý (recommendation system) để huấn luyện mô hình dự đoán các điểm chạm trong hành trình và quyết định mua hàng của khách hàng trên website. Phân nhóm khách hàng và xác định nhóm khách hàng mục tiêu thông qua thuật toán K-means. Sau đó áp dụng phương pháp lọc cộng tác (Collaborative Filtering) với Low Rank Matrix Factorization, huấn luyện mô hình Neural Network để dự đoán tần suất các điểm chạm trong hành trình mua sắm của khách hàng. Dựa vào kết quả dự đoán dữ liệu tần suất điểm chạm, nghiên cứu sử dụng các mô hình như Logistic Regression, Decision Tree, Random Forest, KNN và XGBoost để dự đoán quyết định mua hàng của khách hàng. Nghiên cứu này có giá trị tham khảo đối với các ứng dụng dự đoán các điểm chạm mà khách hàng có thể quan tâm và tương tác, từ đó ảnh hưởng đến quyết định mua hàng và cải thiện chiến lược điều hướng nội dung cũng như chiến lược marketing trên website của doanh nghiệp.
Abstract
Research on utilizing recommendation systems to train models for predicting touchpoints in the customer journey and purchasing decisions on websites. The study involves customer segmentation and identifying target customer groups using the K-means algorithm. Subsequently, Collaborative Filtering with Low Rank Matrix Factorization is applied alongside training a Neural Network model to predict the frequency of touchpoints in the customer shopping journey. Based on the predicted touchpoint frequency data, models such as Logistic Regression, Decision Tree, Random Forest, KNN, and XGBoost are employed to predict customer purchasing decisions. This research provides valuable insights for applications that predict customer touchpoints of interest and interaction, ultimately influencing purchasing decisions and enhancing content navigation strategies as well as marketing strategies on business websites.
Từ khóa
Dự đoán điểm chạm; Hành trình mua sắm; Hệ thống đề xuất; Website
Chi tiết bài viết
Lĩnh vực kinh tế (JEL Codes)
C38 - Classification Methods • Cluster Analysis • Principal Components • Factor Models - Multiple or Simultaneous Equation Models • Multiple Variables, C45 - Neural Networks and Related Topics - Econometric and Statistical Methods: Special Topics, C88 - Other Computer Software - Data Collection and Data Estimation Methodology • Computer Programs, D12 - Consumer Economics: Empirical Analysis - Household Behavior and Family Economics, M31 - Marketing - Marketing and Advertising
Tài liệu tham khảo
Bernard, G. & Andritsos, P. (2017). A process mining based model for customer journey mapping. Forum and doctoral consortium papers presented at the 29th International Conference on Advanced Information Systems Engineering (CAiSE 2017), CEUR Workshop Proceedings, 49-56.
Bernard, G. & Andritsos, P. (2018). CJM-AB: Abstracting customer journey maps using process mining. Information Systems in the Big Data Era: CAiSE Forum 2018, Tallinn, Estonia, June 11-15, 2018, Proceedings 30, Springer, 49-56. https://doi.org/10.1007/978-3-319-92901-9_5
Breiman, L. (2002). Manual on setting up, using, and understanding random forests v3.1. Statistics Department University of California Berkeley, CA, USA, 1, 3-42.
Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984). Classification and regression trees. Wadsworth. Inc, Belmont, CA.
Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. https://doi.org/10.1145/2939672.2939785
Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297. https://doi.org/10.1007/%2FBF00994018
Cox, D.R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society Series B: Statistical Methodology, 20, 215-232. https://doi.org/10.1111/j.2517-6161.1958.tb00292.x
Dam, N.A.K., Le Dinh, T. & Menvielle, W. (2021). Towards a conceptual framework for customer intelligence in the era of big data. International Journal of Intelligent Information Technologies (IJIIT), 17, 64-80. https://doi.org/10.4018/IJIIT.289968
Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27, 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Fix, E. & Hodges, J.L. (1951). Discriminatory analysis, nonparametric discrimination. https://doi.org/10.2307/1403797
Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 1189-1232. https://doi.org/10.1214/aos/1013203451
Habets, S. (2020). Predicting a customer’s next touch point from customer journey data. Eindhoven University of Technology. https://doi.org/10.1007/978-3-031-43433-4_12
Halvorsrud, R., Kvale, K. & Følstad, A. (2016). Improving service quality through customer journey analysis. Journal of Service Theory and Practice, 26, 840-867. https://doi.org/10.1108/JSTP-05-2015-0111
Hernandez, S., Alvarez, P., Fabra, J. & Ezpeleta, J. (2017). Analysis of users’ behavior in structured e-commerce websites. IEEE Access. https://doi.org/11941-11958. 10.1109/ACCESS.2017.2707600
Hu, Y., Koren, Y. & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. 2008 Eighth IEEE International Conference on Data Mining, IEEE, 263-272. https://doi.org/10.1109/ICDM.2008.22
Kabir, S., Mudur, S.P. & Shiri, N. (2012). Capturing browsing interests of users into web usage profiles. Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence.
Lemon, K.N. & Verhoef, P.C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80, 69-96. https://doi.org/10.1509/jm.15.0420
Leoni, M., Maggi, F. M. & van der Aalst, W. M. P. (2015). An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data. Information Systems, 47(C), 258–277. https://doi.org/10.1016/j.is.2013.12.005
Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 281-297. http://projecteuclid.org/euclid.bsmsp/1200512992
Mangiaracina, R., Brugnoli, G. & Perego, A. (2009). The ecommerce customer journey: A model to assess and compare the user experience of the ecommerce websites. Journal of Internet Banking and Commerce, 14, 1-11.
Mcculloch, W.S. & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115-133. https://doi.org/10.1007/BF02478259
Melville, P. & Sindhwani, V. (2010). Recommender systems. Encyclopedia of Machine Learning, 1, 829-838. https://doi.org/10.1007/978-0-387-30164-8_705
Pacuk, A., Sankowski, P., Węgrzycki, K., Witkowski, A. & Wygocki, P. (2016). RecSys Challenge 2016: Job recommendations based on preselection of offers and gradient boosting. Proceedings of the Recommender Systems Challenge.
Ricci, F., Rokach, L. & Shapira, B. (2010). Introduction to recommender systems handbook. Recommender Systems Handbook. Springer. https://doi.org/10.1007/978-0-387-85820-3_1
Rozinat, A. & Van Der Aalst, W.M. (2008). Conformance checking of processes based on monitoring real behavior. Information Systems, 33, 64-95. https://doi.org/10.1016/j.is.2007.07.001
Schonenberg, H., Weber, B., Van Dongen, B. & Van Der Aalst, W. (2008). Supporting flexible processes through recommendations based on history. Business Process Management: 6th International Conference, BPM 2008, Milan, Italy, September 2-4, 2008. Proceedings 6, Springer, 51-66. https://doi.org/10.1007/978-3-540-85758-7_7
Singh, H. B. & Singh, H. K. (2010). Web Data Mining research: A survey. In 2010 IEEE International Conference on Computational Intelligence and Computing Research, 1–10. https://doi.org/10.1109/ICCIC.2010.5705856
Sharkawy, A.-N. (2020). Principle of neural network and its main types. Journal of Advances in Applied & Computational Mathematics, 7, 8-19. https://doi.org/10.15377/2409-5761.2020.07.2
Skulimowski, A. & Kacprzyk, J. (1997). Knowledge, information and creativity support systems: Recent trends, advances and solutions. KICSS2013-8th International Conference on Knowledge, Information, and Creativity Support Systems, Springer. https://doi.org/10.1007/978-3-319-19090-7
Terragni, A. & Hassani, M. (2018). Analyzing customer journey with process mining: From discovery to recommendations. 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), IEEE, 224-229. https://doi.org/10.1109/FiCloud.2018.00040
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17, 168-192. https://doi.org/10.1016/j.aci.2018.08.003
Wiethölter, J., et al. (2023). Exploring customer journey mining and RPA: Prediction of customers’ next touchpoint. September 2023. https://doi.org/10.1007/978-3-031-43433-4_12
Wolny, J. & Charoensuksai, N. (2014). Mapping customer journeys in multichannel decision-making. Journal of Direct, Data and Digital Marketing Practice, 15, 317-326. https://doi.org/10.1057/dddmp.2014.24
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