Analysis of the value added from improving contact-center response time in a customer service application

dc.contributorValencia Arboleda, Carlos Felipe
dc.contributorTabarez Pozos, Alejandra
dc.contributorMelendez Valencia, Kevin
dc.creatorCepeda Vega, Andrés Felipe
dc.date.accessioned2022-06-06T16:43:41Z
dc.date.available2022-06-06T16:43:41Z
dc.date.created2022-06-06T16:43:41Z
dc.date.issued2022-05-27
dc.identifierhttp://hdl.handle.net/1992/57724
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.description.abstractTargeted maximum likelihood estimation (TMLE) procedure is proposed to evaluate the causal impact of the waiting time and abandonment of a phone call in customer profits. Based on different spending behaviors and Customer Service (CS) information, this work finds the causal effect for the Average Treatment Effect (ATE) of the waiting time. This is done for customers with a single contact, two contacts and single contact with abandonment. The results are supported by showing the corresponding 95% confidence intervals as well as clearly stating all the assumptions involved and the limitations of the TMLE methodology. Additionally, the document illustrates the methodology for the identification of the effect by providing a comprehensive expla-nation of the use of Directed acyclic causal graphs.
dc.languageeng
dc.publisherUniversidad de los Andes
dc.publisherMaestría en Ingeniería Industrial
dc.publisherFacultad de Ingeniería
dc.publisherDepartamento de Ingeniería Industrial
dc.relationA. Mahvareh and B. Balcioglu, "Benefits of cross-training in a skill-based routing contactcenter with priority queues and impatient customers," IIE Transactions, vol. 41, no. 6, pp. 524-536, 2009.
dc.relationM. Saberi, O. K. Hussain and E. Chang, "Past, present and future of contact centers: a literature review," Business Process Management Journal, vol. 23, no. 3, pp. 574-597, 2017.
dc.relationM. Duggirala, N. Kambhatla, R. Polavarapu and G. Dinesh, "An Integrated Framework of Service Quality for Global Delivery of Contact Center Services," in SRII Global Conference, IEEE, San Jose, CA, 2011.
dc.relationH. Takeuchi, V. Subramaniam, T. Nasukawa, S. Roy and S. Balakrishnan, "A Conversation-Mining System for Gathering Insights to Improve Agent," in The 9th IEEE International Conference on E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, IEEE., Tokyo, 2007.
dc.relationM. Hernán and J. Robins, "A definition of causal effect," in Causal Inference: What If, Boca Ratón, Chapman & Hill/CRC, 2020, pp. 3-12.
dc.relationR. Donald, "Causal Inference Using Potential Outcomes," J. Amer. Statist. Assoc, vol. 100, no. 469, pp. 322-331, 2005.
dc.relationJ. Pearl , "Causal inference in statistics: An overview," Statistics Surveys, vol. 3, pp. 96-146, 2009.
dc.relationJ. Angrist, G. Imbens and D. Rubin, "Identification of Causal Effect Using Instrumental Variables," J. Am. Stat. Assoc., vol. 91, no. 434, pp. 444-455, 1996.
dc.relationM. Hernán and J. Robins, "Observational Studies," in Causal Inference: What If (2020), Boca Raton, Chapman & Hall/CRC, 2020, pp. 25-40.
dc.relationG. Imbens, "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," arXiv, vol. arXiv:1907.07271 [stat.ME], 2020.
dc.relationM. Hernán and J. Robins, Causal Inference: What If, Boca Raton: Chapman & Hall/CRC, 2020.
dc.relationS. Gruber and M. van der Laan, "tmle: An R Package for Targeted Maximum Likelihood Estimation," Journal of Statistical Software, vol. 51, no. 13, pp. 1-35, 2012.
dc.relationM. van der Laan, J. Coyle, N. Hejazi, I. Malenica, R. Phillips and A. Hubbard, ""Targeted Learning in R: Causal Data Science with the tlverse Software Ecosystem," 20 October 2021. [Online]. Available: https://tlverse.org/tlverse-handbook/index.html. [Accessed 28 November 2021].
dc.relationP. Zivich, zEpid, 2018.
dc.relationM. Schuler and S. Rose, "Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies," American Journal of Epidemiology, vol. 185, no. 1, pp. 65-73, 2017.
dc.relationM. A. Luque-Fernandez, M. Schomaker, B. Rachet and M. Schnitzer, "Targeted maximum likelihood estimation for a binary treatment: A tutorial," Statistics in Medicine , vol. 37, no. 16, pp. 2530-2436, 2018.
dc.relationS. Lendle, B. Fireman and M. van der Laan, "Targeted maximum likelihood estimation in safety analysis," Journal of Clinical Epidemiology, vol. 66, no. 8, pp. 591-598, 2013.
dc.relationS. Gruber and M. van der Laan, "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data," Biometrics , vol. 69, no. 1, pp. 254-262, 2013.
dc.relationM. Pang, T. Schuster, K. Filion, M. Eberg and R. Platt, "Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research," Epidemiology, vol. 25, no. 4, pp. 570-577, 2016.
dc.relationM. van der Laan , Z. Wang and L. van der Laan, "Higher Order Targeted Maximum Likelihood Estimation," arXiv:2101.06290, 2021.
dc.relationM. Hernán and J. Robins, "Graphical representation of causal effects," in Causal Inference: What If, Boca Ratón, Chapman & Hill/CRC, 2020, pp. 69-82.
dc.relationM. Petersen and M. van der Laan, "Causal Models and Learning from Data," Epidemiology, vol. 24, no. 3, pp. 418-426, 2014.
dc.relationP. P. Howards , E. F. Schisterman and P. J. Heagerty, "Potential Confounding by Exposure History and Prior Outcomes: An Example from Perinatal Epidemiology," Epidemiology , vol. 18, no. 5, pp. 544-551, 2007.
dc.relationP. Vineis and D. Kriebel, "Causal models in epidemiology: past inheritance and genetic future," Environmental Health, vol. 5, no. 21, 2006.
dc.relationPearl Judea, "Causal Inference," in Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, 2010.
dc.relationD. B. Rubin, "Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment," Journal of the American Statistical Association , vol. 75, no. 371, pp. 591-593, 1980.
dc.relationM. Hernán and J. Robins, "Confounding," in Causal Inference: What If, Boca Ratón, Chapman & Hall/CRC, 2020, pp. 83-98.
dc.relationJ. Textor, B. van der Zander, M. K. Gilthorpe, M. Liskiewicz and G. T. Ellison, "Robust causal inference using directed acyclic graphs: the R package 'dagitty'," International Journal of Epidemiology, vol. 45, no. 6, pp. 1887-1894, 2016.
dc.relationM. J. van der Laan and D. Rubin, "Targeted Maximum Likelihood Learning," University of California, Berkeley, 2006.
dc.relationM. J. van der Laan, E. C. Polley and A. E. Hubbard, "Super learner," Stat Appl Genet Mol Biol, vol. 6, no. 25, 2007.
dc.relationF. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher and M. Perrot, "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, no. 85, pp. 2825-2830, 2011.
dc.relationG. James, D. Witten, T. Hastie and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, New York, NY: Springer, 2021.
dc.relationN. Erickson, J. Mueller, A. Shirkov, H. Zhang, P. Larroy, M. Li and A. Smola, "AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data," arXiv:2003.06505v1 [stat.ML], 2020.
dc.relationO. J. Dunn, "Multiple Comparisons Among Means," Journal of the American Statistical Association, vol. 56, no. 293, p. 52-64, 1961.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rightshttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleAnalysis of the value added from improving contact-center response time in a customer service application
dc.typeTrabajo de grado - Maestría


Este ítem pertenece a la siguiente institución