Implementación de la inteligencia artificial en la gestión del cliente
| dc.audience | Comunidad Universidad de Medellín | spa |
| dc.contributor.advisor | Escobar Sierra, Manuela | |
| dc.contributor.author | González Vargas, Isabela | |
| dc.contributor.author | Ruiz Henao, Andrés Felipe | |
| dc.coverage.spatial | Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees | |
| dc.date | 2025-10-29 | |
| dc.date.accessioned | 2025-12-09T21:28:09Z | |
| dc.date.available | 2025-12-09T21:28:09Z | |
| dc.description | El acelerado desarrollo de las tecnologías de inteligencia artificial (IA) está transformando la manera en que las organizaciones interactúan con sus clientes. Según Davenport y Ronanki (2018), el 84% de las empresas considera que la IA les proporcionará una ventaja competitiva significativa. Sin embargo, su aplicación estratégica y sostenible en la gestión de relaciones con los clientes (CRM) permanece subestimada. Este estudio tiene como objetivo general determinar el impacto de la implementación de tecnologías de IA en la gestión de relaciones con clientes y estrategias de marketing empresarial. El marco teórico se fundamenta en los conceptos de automatización inteligente, toma de decisiones basada en datos y estrategias centradas en el cliente. La metodología incluye un análisis bibliométrico para mapear las tendencias más relevantes en la literatura científica sobre IA y CRM, seguido de un análisis de contenido de 197 artículos científicos. Para la búsqueda narrativa se profundizó en artículos de selección selectiva que muestran operaciones de procesamiento de lenguaje natural (PLN), segmentación latente de Dirichlet (LDA) y detección de emociones para extraer y evaluar las tendencias emergentes, patrones de implementación y resultados obtenidos. Las principales variables vinculadas a cinco categorías: automatización, personalización, predicción, comprensión emocional y mejoramiento de la experiencia del cliente, las cuales han sido claves en la mejora del potencial de revitalización de la gestión de clientes para permitir experiencias anticipadas, adaptativas y emocionalmente inteligentes. | spa |
| dc.description | The accelerated development of artificial intelligence (AI) technologies is transforming the way organizations interact with their customers. However, its strategic and sustainable application in customer relationship management (CRM) remains underexplored. This study aims to characterize how AI contributes to the evolution of customer management processes through an integrative approach. The theoretical framework is based on intelligent automation, data-driven decision-making, and customer-centric strategies. The methodology included a bibliometric analysis to map the most relevant trends in the scientific literature on AI and CRM, followed by text mining and content analysis of a corpus of 197 academic articles. For the in-depth narrative analysis, a representative sample of 25 key articles was selected, allowing for the identification of core topics and emotional clusters. Natural language processing (NLP), latent Dirichlet allocation (LDA), and emotion detection models were applied to extract and interpret thematic and affective patterns. The results validated five key categories: automation, personalization, prediction, emotional understanding, and ethical concerns. The study concludes that AI has the potential to revolutionize CRM by enabling anticipatory, adaptive, and emotionally intelligent customer experiences. Future research should focus on evaluating implementation models by sector and assessing the performance impact of AI-driven CRM strategies. | eng |
| dc.description.degreename | profesional en Mercadeo | spa |
| dc.format.extent | p. 1-53 | spa |
| dc.format.medium | Electrónico | spa |
| dc.format.mimetype | application/pdf | |
| dc.identifier.other | TG 0082 2025 | |
| dc.identifier.uri | http://hdl.handle.net/11407/9274 | |
| dc.language.iso | spa | |
| dc.language.iso | spa | |
| dc.publisher | Universidad de Medellín | spa |
| dc.publisher.faculty | Facultad de Ciencias Económicas y Administrativas | spa |
| dc.publisher.place | Medellín | spa |
| dc.publisher.program | Mercadeo | spa |
| dc.relation.citationendpage | 53 | |
| dc.relation.citationstartpage | 1 | |
| dc.relation.references | Abbasimehr, H., & Shabani, M. (2020). Forecasting of Customer Behavior Using Time Series Analysis. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 45). https://doi.org/10.1007/978-3-030-37309-2_15 | |
| dc.relation.references | ACAN. (2017). LA INDUSTRIA 4.0. | |
| dc.relation.references | Adachi, T., Endo, M., & Ohashi, K. (2020). Regret over the delay in childbearing decision negatively associates with life satisfaction among Japanese women and men seeking fertility treatment: A cross-sectional study. BMC Public Health, 20(1). https://doi.org/10.1186/s12889-020-09025-5 | |
| dc.relation.references | Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0191-6 | |
| dc.relation.references | Alladi, R. (2024). How AI can transform Customer Relationship Management. In International Journal of Management (Vol. 14). http://www.ijmra.us,http://www.ijmra.us | |
| dc.relation.references | Almalis, I., Kouloumpris, E., & Vlahavas, I. (2022). Sector-level sentiment analysis with deep learning. Knowledge-Based Systems, 258. https://doi.org/10.1016/j.knosys.2022.109954 | |
| dc.relation.references | Álvarez Munarriz, L. (1994). Fundamentos de inteligencia artificial. Universidad de Murcia. https://books.google.es/books?hl=es&lr=&id=UfccXvwzIOUC&oi=fnd&pg=PA19&dq=que+es+la+inteligencia+artificial&ots=z13cZgRSIl&sig=EfNAYfd_hhfmgIyAhMRoyBc1uQw#v=one page&q&f=false | |
| dc.relation.references | Aparecido Claudio, A. (2024). DECODIFICANDO VIESES SOCIAIS: A INTERMEDIAÇÃO DECISIVA DA INTELIGÊNCIA ARTIFICIAL E SUA PRÓPRIA TENDÊNCIA AOS VIESES. https://doi.org/10.1590/SciELOPreprints.7939 | |
| dc.relation.references | Banitaan, S., Salem, S., Jin, W., & Aljarah, I. (2010a). A formal study of classification techniques on entity discovery and their application to opinion mining. International Conference on Information and Knowledge Management, Proceedings, 29–35. https://doi.org/10.1145/1871985.1871992 | |
| dc.relation.references | Banitaan, S., Salem, S., Jin, W., & Aljarah, I. (2010b). A formal study of classification techniques on entity discovery and their application to opinion mining. International Conference on Information and Knowledge Management, Proceedings, 29–35. https://doi.org/10.1145/1871985.1871992 | |
| dc.relation.references | Başarslan, M. S., & Kayaalp, F. (2024). Sentiment analysis using a deep ensemble learning model. Multimedia Tools and Applications, 83(14), 42207–42231. https://doi.org/10.1007/s11042-023-17278-6 | |
| dc.relation.references | Benhamou, S. (2022). La transformación del trabajo y el empleo en la era de la inteligencia artificial: análisis, ejemplos e interrogantes. www.cepal.org/apps | |
| dc.relation.references | Berry, K., & Singh, A. (2024). AI-driven service marketing: Transforming customer experience and operational efficiency. In AI Innovations in Service and Tourism Marketing. https://doi.org/10.4018/979-8-3693-7909-7.ch003 | |
| dc.relation.references | Bidve, V., Shafi, P. M., Sarasu, P., Pavate, A., Shaikh, A., Borde, S., Singh, V. B. P., & Raut, R. (2024). Use of explainable AI to interpret the results of NLP models for sentimental analysis. Indonesian Journal of Electrical Engineering and Computer Science, 35(1), 511–519. https://doi.org/10.11591/ijeecs.v35.i1.pp511-519 | |
| dc.relation.references | Blei, D. M., Ng, A. Y., & Edu, J. B. (2003). Latent Dirichlet Allocation Michael I. Jordan. In Journal of Machine Learning Research (Vol. 3). | |
| dc.relation.references | Borghi, M., & Mariani, M. M. (2021). Service robots in online reviews: Online robotic discourse. Annals of Tourism Research, 87. https://doi.org/10.1016/j.annals.2020.103036 | |
| dc.relation.references | Cañón Solano, A. V., Cardona Arboleda, L. D., Coral García, C. C., & Carmona Domínguez, C. D. (2023). Benefits of artificial intelligence in companies. Management (Montevideo), 1. https://doi.org/10.62486/agma202317 | |
| dc.relation.references | Carichon, F., Ngouma, C., Liu, B., & Caporossi, G. (2024). Objective and neutral summarization of customer reviews. Expert Systems with Applications, 255. https://doi.org/10.1016/j.eswa.2024.124449 | |
| dc.relation.references | Conceição, L., Rodrigues, V., Meira, J., Marreiros, G., & Novais, P. (2022). Supporting Argumentation Dialogues in Group Decision Support Systems: An Approach Based on Dynamic Clustering. Applied Sciences (Switzerland), 12(21). https://doi.org/10.3390/app122110893 | |
| dc.relation.references | Dang, C. N., Moreno-García, M. N., & de la Prieta, F. (2021). An approach to integrating sentiment analysis into recommender systems. Sensors, 21(16). https://doi.org/10.3390/s21165666 | |
| dc.relation.references | Das, R., Hossain, M. F., Ahmed, T., Devanath, A., Akter, S., & Sattar, A. (2022). Classification of Product Review Sentiment by NLP and Machine Learning. 2022 2nd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022. https://doi.org/10.1109/ICAECT54875.2022.9808003 | |
| dc.relation.references | del Carmen Sosa Sierra, M. (2007). Inteligencia artificial en la gestión financiera empresarial. | |
| dc.relation.references | Devi, P., & Bansal, K. L. (2024). Data science in healthcare: techniques, challenges and opportunities. Health and Technology, 14(4), 623–634. https://doi.org/10.1007/s12553-024- 00861-8 | |
| dc.relation.references | dos Santos, S. S. S., & Carvalho, C. E. (2023). The use of digital data analytics in the performance of advertising campaigns: the effect of absorptive capacity. Revista Brasileira de Gestao de Negocios, 25(3), 333–352. https://doi.org/10.7819/rbgn.v25i3.4230 | |
| dc.relation.references | Dudhia, D. J., Dave, S. R., & Yagnik, S. (2020). Self attentive product recommender - A hybrid approach with machine learning and neural network. 2020 International Conference for Emerging Technology, INCET 2020. https://doi.org/10.1109/INCET49848.2020.9154034 | |
| dc.relation.references | Feldman, R. (2013). Techniques and applications for sentiment analysis: The main applications and challenges of one of the hottest research areas in computer science. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274 | |
| dc.relation.references | Gallego, K. (2023). El Poder del Marketing Emocional en tus Estrategias Comerciales: Conectando con tus Clientes en un Nivel Profundo. Innova Maketing Solutions. https://innova- ms.com/el-poder-del-marketing-emocional-en-tus-estrategias-comerciales-conectando-con-tus- clientes-en-un-nivel-profundo/ | |
| dc.relation.references | García-Pablos, A., Cuadros, M., & Rigau, G. (2017). W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis. http://arxiv.org/abs/1705.07687 | |
| dc.relation.references | Gayhardt, L., Li, B., & Lu, P. (2024). Componente Asignación de Dirichlet latente. https://learn.microsoft.com/es-es/azure/machine-learning/component-reference/latent-dirichlet- allocation?view=azureml-api-2#technical-notes | |
| dc.relation.references | Gontero, S., & Menéndez, E. (2021). MACRO MACRODATOS DATOS ( BIG DATA BIG DATA ) Y MERCADO Y MERCADO LABORAL LABORAL Identificación de habilidades a través de vacantes de empleo en línea. www.cepal.org/apps | |
| dc.relation.references | Gonzalez Avella, J. C. (2017, July 18). Uso del Análisis Discriminante Lineal (LDA) para la exploración de datos: Paso a paso. Apsl. https://apsl.tech/es/blog/using-linear- discriminant-analysis-lda-data-explore-step-step/ | |
| dc.relation.references | Gross, B. (1992). La inteligencia artificial y su aplicación en la enseñanza. Comunicación, Lenguaje y Educación, 4(13), 73–80. https://doi.org/10.1080/02147033.1992.10821001 | |
| dc.relation.references | Haddad, O., Fkih, F., & Omri, M. N. (2024). An intelligent sentiment prediction approach in social networks based on batch and streaming big data analytics using deep learning. Social Network Analysis and Mining, 14(1), 150. https://doi.org/10.1007/s13278-024-01304-y | |
| dc.relation.references | Henostroza Diaz, D. G., & Marquez Yauri, H. Y. (2025). Marketing 4.0 y 5.0: Impacto de la transformación digital y la inteligencia artificial en la personalización del consumidor. Arandu UTIC, 12(1), 2526–2551. https://doi.org/10.69639/arandu.v12i1.756 | |
| dc.relation.references | Huang, M.-H., & Rust, R. T. (n.d.). A strategic framework for artificial intelligence in marketing. https://doi.org/10.1007/s11747-020-00749-9/Published | |
| dc.relation.references | Kamath, P. B., Geetha, M., Acharya, D. U., Nandi, R., & Urolagin, S. (2024). Impact of Effective Word Vectors on Deep Learning Based Subjective Classification of Online Reviews. Journal of Machine and Computing, 4(3), 736–747. https://doi.org/10.53759/7669/jmc202404069 | |
| dc.relation.references | Karthick, S., Victor, R. J., Manikandan, S., & Goswami, B. (2018). Professional chat application based on natural language processing. 2018 IEEE International Conference on Current Trends in Advanced Computing, ICCTAC 2018, 1–4. https://doi.org/10.1109/ICCTAC.2018.8370395 | |
| dc.relation.references | Lina, P., Vera, M., Asesor, O., Liseth, K., & Ortega, A. (2023). Adopción de Tecnologías de Inteligencia Artificial: un estudio para las empresas en Colombia. | |
| dc.relation.references | Liu, X.-Y., Zhang, K.-Q., Fiumara, G., Meo, P. D., & Ficara, A. (2024). Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis. Applied Sciences (Switzerland), 14(15). https://doi.org/10.3390/app14156802 | |
| dc.relation.references | Mansouri, A., Affendey, L. S., & Mamat, A. (2008). Named Entity Recognition Approaches. In IJCSNS International Journal of Computer Science and Network Security (Vol. 8, Issue 2). | |
| dc.relation.references | Maynard, D., Bontcheva, K., & Augenstein, I. (2017). Natural Language Processing for the Semantic Web. In Synthesis Lectures on the Semantic Web: Theory and Technology (Vol. 6, Issue 2). https://doi.org/10.2200/S00741ED1V01Y201611WBE015 | |
| dc.relation.references | McKinsey & Company. (2023). El estado de la IA en 2023: El año clave de la IA generativa. | |
| dc.relation.references | Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/J.ASEJ.2014.04.011 | |
| dc.relation.references | Meindl, B., & Mendonça, J. (2021). Mapping Industry 4.0 Technologies: From Cyber- Physical Systems to Artificial Intelligence. | |
| dc.relation.references | Ming, Y., Liu, X., Shen, G., Gao, D., & Wang, Y. (2023). A conditional random field framework for language process in product review mining. Multimedia Tools and Applications, 82(1), 803–817. https://doi.org/10.1007/s11042-022-13303-2 | |
| dc.relation.references | Moodley, K. (2024). Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities. South African Medical Journal, 114(1), 16–20. https://doi.org/10.7196/SAMJ.2024.v114i2.1631 | |
| dc.relation.references | Murel Ph.D, J., & Kavlakoglu, E. (2024). ¿Qué es la asignación latente de Dirichlet? IBM. https://www.ibm.com/es-es/topics/latent-dirichlet-allocation | |
| dc.relation.references | Mustak, M., Hallikainen, H., Laukkanen, T., Plé, L., Hollebeek, L. D., & Aleem, M. (2024). Using machine learning to develop customer insights from user-generated content. Journal of Retailing and Consumer Services, 81, 104034. https://doi.org/10.1016/j.jretconser.2024.104034 | |
| dc.relation.references | Musto, C., Martina, A. F. M., Iovine, A., Narducci, F., de Gemmis, M., & Semeraro, G. (2024). Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domain. Journal of Intelligent Information Systems, 62(2), 575– 599. https://doi.org/10.1007/s10844-023-00835-8 | |
| dc.relation.references | Omar, M., Germán, V., & Dima, C. (2022). Desarrollo de una herramienta de aprendizaje automático (machine learning) para establecer relaciones entre ocupaciones y programas de capacitación en el Uruguay. www.cepal.org/apps | |
| dc.relation.references | Ortiz Morales, M. D., Joyanes Aguilar, L., & Giraldo Marín, L. M. (2015). Los desafíos del marketing en la era del big data. E-Ciencias de La Información, 6(1), 1. https://doi.org/10.15517/eci.v6i1.19005 | |
| dc.relation.references | Pauli, P. A. (2019). Análisis de sentimiento. | |
| dc.relation.references | Puebla, J. G. (2018). Big data and new geographies: The digital footprint of human activity. Documents d’Analisi Geografica, 64(2), 195–217. https://doi.org/10.5565/rev/dag.526 | |
| dc.relation.references | Ramos Torres, C. M., & Duque Holguín, M. (2023). IMPACTO DEL MARKETING EMOCIONAL EN LA TOMA DE DECISIONES DEL CONSUMIDOR UNA REVISIÓN SISTEMATICA DE LA LITERATURA. | |
| dc.relation.references | Ravi, K., Ravi, V., & Prasad, P. S. R. K. (2017). Fuzzy formal concept analysis based opinion mining for CRM in financial services. Applied Soft Computing Journal, 60, 786–807. https://doi.org/10.1016/j.asoc.2017.05.028 | |
| dc.relation.references | Redacción. (2023, October 1). La Ciencia de las Emociones en el Marketing y la Publicidad y su Impacto en la Mente del Consumidor. https://www.puromarketing.com/44/212718/ciencia-emociones-marketing-publicidad-impacto- mente-consumidor | |
| dc.relation.references | Rodríguez, H. (2023, September 25). Así interpreta la inteligencia artificial nuestros estados de ánimo. Natgeo. https://www.nationalgeographic.com.es/ciencia/asi-interpreta-la- inteligencia-artificial-nuestros-estados-de-animo-_16304 | |
| dc.relation.references | Russo, C., Ramón, H., Alonso, N., Cicerchia, B., Esnaola, L., & Tessore, J. P. (2016). Tratamiento Masivo de Datos Utilizando Técnicas de Machine Learning. | |
| dc.relation.references | Salesforce. (2024, May 20). New Salesforce Report: AI is Marketers’ Top Priority – And Biggest Headache. | |
| dc.relation.references | Samha, A. K., Li, Y., & Zhang, J. (2015). Aspect-based opinion mining from product reviews using conditional random fields. Conferences in Research and Practice in Information Technology Series, 168, 119–128. | |
| dc.relation.references | Sherif, N. H., Raad Ali, R., Fahidhil, E., Haroon, N. H., Hussam, R., & Ibrahem, M. (2023). Integrating NLP in the Business Decision Support System to Promote Customer Loyalty. 1st International Conference on Emerging Research in Computational Science, ICERCS 2023 - Proceedings. https://doi.org/10.1109/ICERCS57948.2023.10434114 | |
| dc.relation.references | Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019 | |
| dc.relation.references | Timimi, H., Baaddi, M., & Bennouna, A. (2025a). Impact of artificial intelligence on the personalization of the customer experience: A systematic literature review. Multidisciplinary Reviews, 8(7). https://doi.org/10.31893/multirev.2025224 | |
| dc.relation.references | Timimi, H., Baaddi, M., & Bennouna, A. (2025b). Impact of artificial intelligence on the personalization of the customer experience: A systematic literature review. Multidisciplinary Reviews, 8(7). https://doi.org/10.31893/multirev.2025224 | |
| dc.relation.references | Tsytsarau, M., & Palpanas, T. (2012). Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24(3), 478–514. https://doi.org/10.1007/S10618-011-0238-6 | |
| dc.relation.references | Vargas Pérez, C., & Peñalosa Figueroa, J. L. (2019). BIG DATA Aplicaciones en las Empresas, la Justicia y la Docencia. | |
| dc.relation.references | Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1 | |
| dc.relation.references | Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119 | |
| dc.relation.references | Zhang, S., Lu, G., & Shuang, K. (2019). Opinion Knowledge Injection Network for Aspect Extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 11954 LNCS. https://doi.org/10.1007/978-3-030-36711-4_56 | |
| dc.relation.references | Zhao, Q., Zhao, W., Guo, X., Zhang, K., & Yu, M. (2022). A dynamic customer requirement mining method for continuous product improvement. Autonomous Intelligent Systems, 2(1). https://doi.org/10.1007/s43684-022-00032-4 | |
| dc.relation.references | Zhou, Z.-H. (2021). Maching learning. Springer. https://books.google.com.co/books?id=ctM- EAAAQBAJ&printsec=frontcover&hl=es&source=gbs_ge_summary_r&cad=0#v=onepage&q& f=false | |
| dc.rights.accessrights | info:eurepo/semantics/openAccess | |
| dc.rights.creativecommons | Attribution-NonCommercial-ShareAlike 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0 | * |
| dc.subject | Inteligencia Artificial (IA) | spa |
| dc.subject | Aprendizaje automático | spa |
| dc.subject | Macrodatos | spa |
| dc.subject | Procesamiento del Lenguaje Natural (NLP) | spa |
| dc.subject | Analítica de Datos | spa |
| dc.subject | Automatización | spa |
| dc.subject | Machine learning | eng |
| dc.subject | Big Data | eng |
| dc.subject.lemb | Aprendizaje automático (Inteligencia artificial) | |
| dc.subject.lemb | Automatización | |
| dc.subject.lemb | Big data | |
| dc.subject.lemb | Inteligencia artificial | |
| dc.subject.lemb | Lingüística computacional | |
| dc.subject.lemb | Mercadeo - Procesamiento de datos | |
| dc.subject.lemb | Planificación empresarial | |
| dc.subject.lemb | Planificación estratégica | |
| dc.subject.lemb | Relaciones con los clientes | |
| dc.title | Implementación de la inteligencia artificial en la gestión del cliente | |
| dc.type | info:eu-repo/semantics/bachelorThesis | |
| dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | |
| dc.type.driver | info:eu-repo/semantics/bachelorThesis | |
| dc.type.hasversion | publishedVersion | |
| dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
| dc.type.local | Trabajo de Grado - Pregrado | spa |
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