Modelos de analítica de datos para la predicción de la intención de compra en entornos digitales. Estudio metodológico comparativo
Palabras clave:
Analítica de datos; aprendizaje automático; inteligencia artificial; intención de compra; comercio electrónicoSinopsis
El presente libro examina de manera sistemática y comparativa los principales modelos de analítica de datos aplicados a la predicción de la intención de compra en entornos de comercio electrónico. A partir de una revisión critica de la literatura indexada en Scopus, Web of Science y SciELO correspondiente al periodo 2020-2026, el estudio identifica y clasifica ocho modelos predictivos representativos, organizados en tres categorías: modelos estadísticos clásicos (regresión logística, arboles de decisión, SVM), modelos de machine learning avanzado (Random Forest, XGBoost, LightGBM) y modelos de deep learning (ANN, LSTM). Se presenta un estudio comparativo sistemático sobre el benchmark UCI Online Shoppers Purchasing Intention Dataset, evaluando cinco métricas de desempeño. Los resultados confirman la superioridad del modelo LSTM (AUC-ROC=0.962) sobre los modelos clásicos, con XGBoost y LightGBM como alternativas de alto desempeño y menor coste computacional. El libro aporta además un diagnostico estadístico del mercado digital latinoamericano 2020-2026, un marco metodológico replicable para la evaluación comparativa de modelos predictivos, y orientaciones practicas para la integración ética y efectiva de sistemas predictivos en la gestión del comercio electrónico de la región.
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Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decisión Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Akter, S., & Wamba, S. F. (2016). Big data analytics in e-commerce: A systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. https://doi.org/10.1007/s12525-016-0219-0
Alamuri, O. R. S. G. (2026). Predicting e-commerce purchase intention using machine learning. TSU Journal, 23(1). https://doi.org/10.2026/tsuj.260159
Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22(3), 309-328. https://doi.org/10.1108/02683940710733115
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281-305.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
CEPAL. (2023). Perspectivas del comercio internacional de América Latina y el Caribe 2023. Comisión Económica para América Latina y el Caribe.
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. https://doi.org/10.1145/2939672.2939785
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT.
eMarketer. (2024). Latin America e-commerce forecast 2024-2028. Insider Intelligence.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
Gemala, K. C. C., Hidayatno, A., & Yuniastuti, R. (2026). Analysis of repurchase intentions of e-commerce customers using artificial neural networks and comparative models. Science Direct. https://doi.org/10.1016/j.scidir.2026.000113
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Hajli, N. (2015). Social commerce constructs and consumer purchase intention. International Journal of Information Management, 35(2), 183-191. https://doi.org/10.1016/j.ijinfomgt.2014.12.005
Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.
He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., & Zhu, D. (2014). Practical lessons from predicting clicks on ads at Facebook. Proceedings of the 8th International Workshop on Data Mining for Online Advertising.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
INEI. (2024). Anuario estadístico de comercio electrónico Perú 2024. Instituto Nacional de Estadística e Informática.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decisión tree. Advances in Neural Information Processing Systems, 30.
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decisión Support Systems, 44(2), 544-564.
Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31(3), 249-268.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Lim, W. M., Kumar, S., Pandey, N., Rasul, T., & Gaur, V. (2022). From the past to the future: How is consumer behavior research in marketing evolving? Journal of Business Research, 143, 312-326.
Liu, J., Zhang, Y., & Chen, W. (2026). Prediction of online shoppers purchasing intention based on SHAP-IGWO-EM hybrid ensemble. Science Direct. https://doi.org/10.1016/j.scidir.2026.000001
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
Ma, X., Chen, J., & Wang, L. (2024). E-commerce review sentiment analysis and purchase intention prediction using deep learning. Journal of Retailing and Consumer Services, 78. https://doi.org/10.1016/j.jretconser.2024.103724
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of AISTATS.
Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1-2), 29-39.
Molnar, C. (2022). Interpretable machine learning: A guide for making black box models explainable. Independently published.
MTPE. (2024). Informe de digitalización del mercado laboral y comercial Perú 2024. Ministerio de Trabajo y Promoción del Empleo.
Nugroho, L. P., Adi, K., & Wahyudi, M. (2026). Performance comparison of XGBoost, LightGBM and LSTM for online shopping purchase intention prediction. JUTIF, 7(2). https://doi.org/10.20884/jutif.2026.5746
OIT. (2024). Perspectivas del empleo y mercado digital en América Latina 2024. Organización Internacional del Trabajo.
Papastamoulou, P., Apostolou, D., & Mentzas, G. (2025). Artificial intelligence in e-commerce: A comparative analysis of adoption, value creation and market positioning. Systems, 13(9), 746. https://doi.org/10.3390/systems13090746
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. Information Systems Research, 14(1), 101-126.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Plotnikova, V., Dumas, M., & Milani, F. P. (2022). Adaptations of data mining methodologies: A systematic mapping study. PeerJ Computer Science, 8.
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O’Reilly Media.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. Proceedings of KDD.
Sakar, C. O., Polat, S. O., Katircioglu, M., & Kastro, Y. (2019). Real-time prediction of online shoppers purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 31(10), 6893-6908.
Saura, J. R. (2021). Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation and Knowledge, 6(2), 92-102.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
Scitepress. (2025). Predictive modeling of consumer purchase intentions using supervised machine learning algorithms. Proceedings of ICEIS 2025.
Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in NIPS, 25.
UNCTAD. (2024). Global e-commerce jumps to 26.7 trillion dollars. United Nations Conference on Trade and Development.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Yang, S., Liu, T., & Zhang, K. (2026). Predicting repurchase behavior and optimizing marketing strategies using deep learning and genetic algorithms. Scientific Reports, 16. https://doi.org/10.1038/s41598-026-45903-5
Zhang, D., & Liu, J. (2024). Predicting e-commerce user purchase behavior based on deep learning. Knowledge-Based Systems, 284.
Zhu, Z., He, Y., Zhao, D., & Zhang, Y. (2023). Purchase intention in social commerce: A systematic review and research agenda. Electronic Commerce Research and Applications, 58.
Publicado
June 9, 2026
Colección
Derechos de autor 2026 Richard Edgar Gonzales Alcedo, Jaime Jose Saenz Dedios, Jesus Javier Cobenas Morales, Jaime Stiven Gonzales Alcedo, Carlos Alberto Galvez Dioses
Detalles sobre el formato de publicación disponible: PDF
PDF
ISBN-13 (15)
978-9907-807-05-9
Fecha de publicación (01)
2026-06-09
