(Department of Communications and Partnerships, University of Economics Ho Chi Minh City, Viet Nam)(2) * Ngan Thi Bich Lam
(Product Department, VSC Science Technology Communications Group Company Limited, Ho Chi Minh City, Viet Nam)(3) Trinh My Le
(Faculty of Information Technology, UEH Mekong, University of Economics Ho Chi Minh City, Viet Nam)(4) Duy Le Trinh
(Department of Student Affairs, University of Economics Ho Chi Minh City, Viet Nam)*corresponding author
AbstractBuilding on the premise that real-time marketing decisions require models that are both accurate and resource-efficient, our study proposes a context-aware, tabular pipeline using CatBoost with ordered target encoding and greedy feature combinations to learn from customer journey sequences. In feature engineering, we derive period-level aggregates such as touch counts and mean dwell times across daily, weekly, or monthly intervals, compute channel-usage entropy to measure diversity, and quantify recency relative to the most recent interaction, capturing intensity and diversity of user behavior. These sequences are further enriched with context signals such as dwell time, device type, temporal gaps, and channel entropy, along with demographic attributes, to provide a rich representation of customer behavior. We apply the method to the Netherlands Travel dataset (May 2015–Oct 2016), trimming each journey to the first ten and last twenty touchpoints, engineering row-level and period-level aggregates, and collapsing them to the purchase level. CatBoost, a gradient boosting algorithm, leverages ordered target encoding and greedy feature combinations to capture nonlinear interactions while remaining interpretable and CPU-friendly. Stratified five-fold cross-validation yields an AUC of 0.9479, and tuning the F0.5 = 0.869 score produces a threshold of 0.723 with precision 0.897, recall 0.773, and accuracy 0.895. These results demonstrate that structured tabular representations of customer journeys, combined with tree-based learning, can achieve strong predictive performance while maintaining interpretability and computational efficiency, offering a practical modeling framework for decision-support systems in travel marketing.
KeywordsCustomer journey; Purchase-decision prediction; Tabular sequences;Context-aware; CatBoost
|
DOIhttps://doi.org/10.26555/ijain.v12i3.2412 |
Article metricsAbstract views : 231 | PDF views : 1 |
Cite |
Full Text Download
|
References
[1] Y. Chen and C. Prentice, “Integrating Artificial Intelligence and Customer Experience,” Australas. Mark. J., vol. 33, no. 2, pp. 141–153, May 2025, doi: 10.1177/14413582241252904.
[2] “AI elevates customer service in travel and transportation | Roland Berger.” Accessed: Sep. 10, 2026. [Online]. Available: https://www.rolandberger.com/en/Insights/Publications/AI-elevates-customer-service-in-travel-and-transportation.html.
[3] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
[4] M. Ćirić, B. Predić, D. Stojanović, and I. Ćirić, “Single and Multiple Separate LSTM Neural Networks for Multiple Output Feature Purchase Prediction,” Electronics, vol. 12, no. 12, 2023, doi: 10.3390/electronics12122616.
[5] J. Zhang, “Deep Learning-Based Attribution Framework for Real-Time Budget Optimization in Cross-Channel Pharmaceutical Advertising: A Comparative Study of Traditional and Digital Channels,” in Proceedings of the 2025 International Conference on Software Engineering and Computer Applications, New York, NY, USA: ACM, Jun. 2025, pp. 248–254. doi: 10.1145/3747912.3747951.
[6] J. Li et al., “Text Is All You Need: Learning Language Representations for Sequential Recommendation,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, Aug. 2023, pp. 1258–1267. doi: 10.1145/3580305.3599519.
[7] Z. Cui, H. Wu, B. He, J. Cheng, and C. Ma, “Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive Learning,” in Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, New York, NY, USA: ACM, Oct. 2024, pp. 404–414. [Online]. Available: https://arxiv.org/html/2405.09369v5
[8] X. Zhou, R. Lumbantoruan, Y. Ren, L. Chen, X. Yang, and J. Shao, “Dynamic Bi-layer Graph Learning for Context-aware Sequential Recommendation,” ACM Trans. Recomm. Syst., vol. 2, no. 2, pp. 1–23, Jun. 2024, [Online]. Available: https://www.researchgate.net/publication/376379134
[9] N. L. S. d, E. Souza, M. T. Arante, L. F. Braghirolli, M. M. P. Mafia, and E. M. Frazzon, “Orchestrating a ship-from-store omnichannel operation using 4PL digital platform,” Int. J. Bus. Forecast. Mark. Intell., vol. 8, no. 1, p. 52, 2023, doi: 10.1504/IJBFMI.2023.127704.
[10] J. N. Sheth, V. Jain, and A. Ambika, “The growing importance of customer-centric support services for improving customer experience,” J. Bus. Res., vol. 164, no. September, p. 113943, Sep. 2023, doi: 10.1016/j.jbusres.2023.113943.
[11] M. Xu, F. Liu, and W. Xu, “A Survey on Sequential Recommendation,” in 2019 6th International Conference on Information Science and Control Engineering (ICISCE), IEEE, Dec. 2019, pp. 106–111. doi: 10.1109/ICISCE48695.2019.00031.
[12] C. Wu, F. Wu, T. Qi, C. Li, and Y. Huang, “Is News Recommendation a Sequential Recommendation Task?,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA: ACM, Jul. 2022, pp. 2382–2386. [Online]. Available: https://arxiv.org/pdf/2108.08984
[13] F. Sun et al., “BERT4Rec,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, New York, NY, USA: ACM, Nov. 2019, pp. 1441–1450. [Online]. Available: https://arxiv.org/pdf/1904.06690.
[14] L. Theodorakopoulos and A. Theodoropoulou, “Leveraging Big Data Analytics for Understanding Consumer Behavior in Digital Marketing: A Systematic Review,” Hum. Behav. Emerg. Technol., vol. 2024, no. 1, p. 3641502, Jan. 2024, doi: 10.1155/2024/3641502.
[15] P. B. Pires, B. M. Perestrelo, and J. D. Santos, “Unpacking Customer Experience in Online Shopping: Effects on Satisfaction and Loyalty,” J. Theor. Appl. Electron. Commer. Res., vol. 20, no. 3, p. 245, Sep. 2025, doi: 10.3390/jtaer20030245.
[16] T. S. G. Furquim, C. P. da Veiga, C. R. P. da Veiga, and W. V. da Silva, “The Different Phases of the Omnichannel Consumer Buying Journey: A Systematic Literature Review and Future Research Directions,” J. Theor. Appl. Electron. Commer. Res., vol. 18, no. 1, pp. 79–104, Dec. 2022, doi: 10.3390/jtaer18010005.
[17] L. Amalia, Sertiningsih, F. Hayatuki, S. Muliani, and A. M. Junaidi, “Advantages and Disadvantages of Utilizing Markov Chains in a Digital Marketing Strategy to Predict E-Commerce Sales,” Sci. Technol. Sci. Soc., vol. 1, no. 3, pp. 43–61, Dec. 2024, doi: 10.59324/stss.2024.1(3).03.
[18] L. D. Hollebeek, S. Urbonavicius, V. Sigurdsson, R. Arvola, and M. K. Clark, “Customer Journey Value: A Conceptual Framework,” J. Creat. Value, vol. 9, no. 1, pp. 8–26, May 2023, doi: 10.1177/23949643231157155.
[19] X. Xia et al., “TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, Aug. 2023, pp. 5249–5259. doi: 10.1145/3580305.3599918.
[20] A. Muralidhar and Y. Lakkanna, “From Clicks to Conversions: Analysis of Traffic Sources in E-Commerce,” J. Media Manag., vol. 6, no. 12, p. 1, Dec. 2024, doi: 10.47363/JMM/2024(6)182.
[21] L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data?,” in Advances in Neural Information Processing Systems 35, San Diego, California, USA: Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2022, pp. 507–520. doi: 10.52202/068431-0037.
[22] V. Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci, “Deep Neural Networks and Tabular Data: A Survey,” IEEE Trans. Neural Networks Learn. Syst., vol. 35, no. 6, pp. 7499–7519, Jun. 2024, doi: 10.1109/TNNLS.2022.3229161.
[23] R. Timmer, “Deep Learning versus Gradient Boosted Decision Trees: A Comparison on Noise Robustness,” Erasmus Univ. Rotterdam Erasmus Sch. Econ., no. July, 2023, [Online]. Available: https://thesis.eur.nl/pub/67870/Bachelor-Thesis-Ruben-Timmer.pdf.
[24] G. Zabërgja, A. Kadra, C. M. M. Frey, and J. Grabocka, “Tabular Data: Is Deep Learning all you need?,” vol. 2, pp. 1–55, Oct. 2025, [Online]. Available: http://arxiv.org/abs/2402.03970
[25] M. Badouch and M. Boutaounte, “TAHRF: enhancing personalized tourism recommendations with dynamic adaptation,” IAES Int. J. Artif. Intell., vol. 15, no. 1, pp. 374–382, Feb. 2026, doi: 10.11591/ijai.v15.i1.pp374-382.
[26] W. Zheng, J. Li, H. Pan, and X. Fan, “EMO: Elastic oversampling with boundary skeletons from minimal enclosing balls,” Neurocomputing, vol. 672, no. April, p. 132818, Apr. 2026, doi: 10.1016/j.neucom.2026.132818.
[27] A. Tanaka, K. Sato, and Y. Nakamura, “Real-Time Modeling of Customer Journey in Multichannel Platforms,” J. Electron. Commer. Manag., vol. 1, no. 1, pp. 65–84, 2025, [Online]. Available: https://www.joecm.ir/article_222685_87a12dd210117584dd7642463cc6dece.pdf
[28] R. Du, D. He, Y. Song, Z. Ding, S. Chan, and X. Li, “Privacy-Preserving Short-Term Travel Demand Forecasting Based on Federated Learning,” IEEE Trans. Veh. Technol., vol. 74, no. 7, pp. 11436–11449, Jul. 2025, doi: 10.1109/TVT.2025.3547823.
[29] S. Chen et al., “TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data,” vol. 1, pp. 1–13, 2024, [Online]. Available: https://arxiv.org/abs/2411.11148v1
[30] A. V. Petrov and C. Macdonald, “Generative Sequential Recommendation with GPTRec,” vol. 1, pp. 1–10, 2023, [Online]. Available: http://arxiv.org/abs/2306.11114
[31] R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need,” Inf. Fusion, vol. 81, pp. 84–90, May 2022, doi: 10.1016/j.inffus.2021.11.011.
[32] I. Mapfumo and T. Shongwe, “Performance Evaluation of Machine Learning and Deep Learning Models for Credit Risk Prediction,” J. Risk Financ. Manag., vol. 19, no. 3, p. 210, Mar. 2026, doi: 10.3390/jrfm19030210.
[33] K. F. Balbal and D. Birant, “RFM-Net: A Convolutional Neural Network for Customer Segment Classification,” Appl. Sci., vol. 16, no. 5, p. 2223, Feb. 2026, doi: 10.3390/app16052223.
[34] M. Flórez, E. Carrillo, F. Mendes, and J. Carreño, “A Context-Aware Tourism Recommender System Using a Hybrid Method Combining Deep Learning and Ontology-Based Knowledge,” J. Theor. Appl. Electron. Commer. Res., vol. 20, no. 3, p. 194, Aug. 2025, doi: 10.3390/jtaer20030194.
[35] R. A. Ruslan and N. Arbaiy, “Handling Imbalanced Datasets in Machine Learning: Challenges, Approaches, and Best Practices,” J. Appl. Sci. Technol. Comput., vol. 1, no. 2, pp. 1–17, Nov. 2024, doi: 10.30880/jastec.2024.01.02.003.
[36] M. Altalhan, A. Algarni, and M. Turki-Hadj Alouane, “Imbalanced Data Problem in Machine Learning: A Review,” IEEE Access, vol. 13, pp. 13686–13699, 2025, doi: 10.1109/ACCESS.2025.3531662.
[37] G. Li, Y. Wu, Y. Bai, and W. Zhang, “ReMAHA–CatBoost: Addressing Imbalanced Data in Traffic Accident Prediction Tasks,” Appl. Sci., vol. 13, no. 24, p. 13123, Dec. 2023, doi: 10.3390/app132413123.
[38] H. Ribeiro, B. Barbosa, A. C. Moreira, and R. Rodrigues, “Customer Experience, Loyalty, and Churn in Bundled Telecommunications Services,” Sage Open, vol. 14, no. 2, p. 21582440241245190, Apr. 2024, doi: 10.1177/21582440241245191.
[39] A. A. Alhussan, M. Metwally, and S. K. Towfek, “Predicting CO2 Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm,” Mathematics, vol. 13, no. 9, p. 1481, Apr. 2025, doi: 10.3390/math13091481.
[40] K. N. Lemon and P. C. Verhoef, “Understanding Customer Experience Throughout the Customer Journey,” J. Mark., vol. 80, no. 6, pp. 69–96, Nov. 2016, doi: 10.1509/jm.15.0420.
[41] G. Bernard and P. Andritsos, “A process mining based model for customer journey mapping,” CEUR Workshop Proc., vol. 1848, pp. 49–56, 2017, [Online]. Available: https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper7.pdf.
[42] R. S. Dixit, S. L. Choudhary, and N. Govil, “Analyzing the impact of artificial intelligence on the online purchase decision-making process through the lens of the UTAUT 2 model,” Discov. Comput., vol. 28, no. 1, p. 88, May 2025, doi: 10.1007/s10791-025-09575-5.
[43] L. Reinkemeyer, Process Mining in Action. Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-40172-6.
[44] L. Zhang and L. Jiang, “Game-theoretic SHAP-driven interpretable forecasting of air cargo demand using Bayesian-optimized random forests,” Front. Phys., vol. 13, Oct. 2025, doi: 10.3389/fphy.2025.1705687.
[45] M. Lee, I. Choi, and W.-C. Kim, “Predicting Mobile Payment Behavior Through Explainable Machine Learning and Application Usage Analysis,” J. Theor. Appl. Electron. Commer. Res., vol. 20, no. 2, p. 117, May 2025, doi: 10.3390/jtaer20020117.
[46] A. Prashanthan, “An Integrated Framework for Optimizing Customer Retention Budget using Clustering, Classification, and Mathematical Optimization,” J. Comput. Theor. Appl., vol. 3, no. 1, pp. 45–63, Jul. 2025, doi: 10.62411/jcta.13194.
[47] M. Teixeira, J. M. Oliveira, and P. Ramos, “Enhancing Hierarchical Sales Forecasting with Promotional Data: A Comparative Study Using ARIMA and Deep Neural Networks,” Mach. Learn. Knowl. Extr., vol. 6, no. 4, pp. 2659–2687, Nov. 2024, doi: 10.3390/make6040128.
[48] E. Neufeld, “Cross-Device and Cross-Channel Identity Measurement Issues and Guidelines,” J. Advert. Res., vol. 57, no. 1, pp. 109–118, Mar. 2017, doi: 10.2501/JAR-2017-008.
[49] F. M. Talaat, A. Aljadani, B. Alharthi, M. A. Farsi, M. Badawy, and M. Elhosseini, “A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing,” Mathematics, vol. 11, no. 18, p. 3930, Sep. 2023, doi: 10.3390/math11183930.
[50] Q. Ai, X. Wang, and M. Bendersky, “Metric-agnostic Ranking Optimization,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, in SIGIR ’23. New York, NY, USA: ACM, Jul. 2023, pp. 2669–2680. doi: 10.1145/3539618.3591935.
[51] N. S. Dahake, P. Suresh Dahake, and A. Gajghate, “Beyond the Hype: A Critical Evaluation of Predictive Analytics Accuracy in High-Stakes Business Decisions,” in 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI), IEEE, Nov. 2024, pp. 1–6. doi: 10.1109/IDICAIEI61867.2024.10842857.
[52] G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” Adv. Neural Inf. Process. Syst., vol. 2017-Decem, no. Nips, pp. 3147–3155, 2017, [Online]. Available: https://proceedings.neurips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf.
[53] O. Chapelle, E. Manavoglu, and R. Rosales, “Simple and Scalable Response Prediction for Display Advertising,” ACM Trans. Intell. Syst. Technol., vol. 5, no. 4, pp. 1–34, Jan. 2015, doi: 10.1145/2532128.
[54] Hall of Franklin Institute, “Monthly weather review,” J. Franklin Inst., vol. 138, no. 6, pp. 1–8, Dec. 1894, doi: 10.1016/0016-0032(94)90228-3.
[55] E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach,” Biometrics, vol. 44, no. 3, p. 837, Sep. 1988, doi: 10.2307/2531595.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0