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

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.

Exploring the use of gender-fair language by influencers

“…The literature has identified a gap in the research of more complex methodologies based on SN information (Nicolas et al. , 2018); therefore, this study employs Text Analysis by Data Mining Algorithms (Urrutia et al. , 2021) to develop the GFL Clustering BoW Methodology.…”

“…In addition, other authors have established that language is a variable that influences the attitudes, behavior and perceptions of society (De Lemus and Estevan-Reina, 2021;Patev et al, 2019;Heilman and Caleo, 2018;Sczesny et al, 2016;Koeser et al, 2015;Koeser and Sczesny, 2014). The literature has identified a gap in the research of more complex methodologies based on SN information (Nicolas et al, 2018); therefore, this study employs Text Analysis by Data Mining Algorithms (Urrutia et al, 2021) to develop the GFL Clustering BoW Methodology.…”