Affiliations: [a] Facultad de Ciencias de la Ingeniería, Universidad Católica del Maule, Talca, Chile
| [b] Escuela de Ingeniería Comercial, Facultad de Economía y Negocios, Universidad Santo Tomás, Chile
Correspondence:
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Corresponding author. Dra. Carolina Nicolas, Escuela de Ingeniería Comercial, Facultad de Economía y Negocios, Universidad Santo Tomás, Chile. E-mail: [email protected].
Abstract: Companies 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.
Keywords: Management CRM system, data mining on customer, forgotten effects theory, methodology effects of the causal, food industry, Chile
