Applying data mining on customer relationship management system to discover forgotten effects

dc.creatorUrrutia-Sepúlveda, Angélica
dc.creatorRojo, Fabiola
dc.creatorNicolas Alarcón, Carolina
dc.creatorAhumada, Roberto
dc.date2021-12-15T13:40:56Z
dc.date2021-12-15T13:40:56Z
dc.date2021
dc.date.accessioned2022-10-18T12:13:44Z
dc.date.available2022-10-18T12:13:44Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/3605
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4443801
dc.descriptionCompanies need to know customer preferences for decision-making. For this reason, the companies take into account the Customer Relationship Management (CRM). These information systems have the objective to give support and allow the management of customer data. Nevertheless, it is possible to forget causal relationships that are not always explicit, obvious, or observables. The aim of this study on new methodologies for finding causal relationships. This research used a data analysis methodology of a CRM. The traditional analysis method is the Theory of Forgotten Effects (TFE), which is considered in this work. The new approach proposed in this article is to use Data Mining Algorithms (DMA) like Association Rules (AR) to discover causal relationships. This study analyzed 5,000 users’ comments and opinions about a Chilean foods industry company. The results show that the DMA used in this work obtains the same values as the TFE. Consequently, DMA can be used to identify non-obvious comments about products and services.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceJournal of Intelligent & Fuzzy Systems, 40(2), 1783-1794
dc.subjectManagement CRM system
dc.subjectData mining on customer
dc.subjectFood industry
dc.subjectChile
dc.titleApplying data mining on customer relationship management system to discover forgotten effects
dc.typeArtículos de revistas


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