Revisión literaria sobre la propensión de compra de los individuos mediante la analítica predictiva en el sector Retail de consumo

Abstract

The document in question being of a literary nature; it compiles and exposes the procedures to fulfill the objective of this article which is to examine the effectiveness of the models of purchase propensity through predictive analytics, that is to say, that through the different methods mentioned here it is possible to evidence the robustness that these tools of the Big Data have to analyze in detail the factors that affect the preferences at the time of buying. For this reason, the document is made with a descriptive type methodology supported by a literature review that begins with the most frequent techniques and concepts of predictive analytics oriented to making good decisions. Following this, the most relevant cases of companies that have implemented this system are presented, and finally, the most precise and effective methods that predictive analytics can use to increase consumption in the retail sector are suggested. Consequently, it is specified that the most viable treatment corresponds to the Recurrent Neural Networks, the decision trees, and the Business Intelligence through Machine and supervised Deep Learning.