Day by day becomes more important for companies to own a database of loyal customers, because the continuous follow up and expanding business with them gives a bigger profitability that getting new customers. Several methodologies have been used for satisfaction measure and loyalty of customers, most of them are based on Behavioural Psychology from positivist approach and leverages description of manifest behaviors measured directly and whose main objective is establish the direct importance that each service driver has about loyalty, as in case of methodologies based on regression models. In this paper I illustrate an application of a Loyalty Model that looking for through an analysis of Structural Equation Modeling with latent variables, define the size of effect on the loyalty for phenomena like overall satisfaction with service, repurchase intention and recommendation. 

High volume of data is generated daily, especially on social networks. The usage of this data as a source in the study of the agent’s behavior in the stock market have been gaining interest, specifically in the machine learning field. Hence, in this article; a study about the predictive power of this kind of data over the future price variation direction of a stock is made, using the texts published in the StockTwits social network and machine learning techniques.