Bayesian Network Applications to Customer Surveys and InfoQ

Abstract

Modelling relationships between variables has been a major challenge for statisticians in a wide range of application areas. In conducting customer satisfaction surveys, one main objective, is to identify the drivers to overall satisfaction (or dissatisfaction) in order to initiate proactive actions for containing problems and/or improving customer satisfaction. Bayesian Networks (BN) combine graphical analysis with Bayesian analysis to represent relations linking measured and target variables. Such graphical maps are used for diagnostic and predictive analytics. This paper is about the use of BN in the analysis of customer survey data. We propose an approach to sensitivity analysis for identifying the drivers of overall satisfaction. We also address the problem of selection of robust networks. Moreover, we show how such an analysis generates high information quality (InfoQ) and can be effectively combined with an integrated analysis considering various models.