Optimizing statistical forecasting models in real time with bayesian component using´big data´ in marketing

Abstract

Companies possess a large amount of digital information that needs to be registered and available for enquiry on real time. That is why, it is essential the creation of a solid structure in a Big Data System that enables a company to generate a reliable data matrix of relational variables. In this article, it is intended to emphasize the relevance of the generation of such a solid structure in order to deal with historic register (hard data that derive from billing and logistics areas) and subjective information (marketing strategies) to allow the recording and enquiry of data of a company in real time. This type of system will facilitate the creation of set of schemes to store and represent nonredundant information to identify recurrent patterns for strengthening processes of estimation and simulation of product demand through statistical analysis of qualitative and quantitative variables. The Dynamic Bayesian Model techniques, which update through Dynamic tables, are suitable tools to achieve such objectives. They use Bayesian adjustment of arithmetic mean with exponential smoothing method based on product demand to generate indicators of either encouraging or inhibitory effects which function as “input” for any Multivariate Statistical Forecast Models.