Sensitivity analysis of CRM indicators

Abstract

The research aims to explore the sensitivity of CRM indicators and to define specific conditions for their application according to customer historical information. The neural network analysis was applied for the research of classification task of distinguishing classes of potentially returning customers from those, who tend to leave the company. The research results revealed that application of traditional time and money-related variables have important advantage, as they are considered to make objective basis for judgement. The experimental research was performed by mining customer database of the travel agency. According to the experimental evaluation we explored that the classification model can not be uniformly applied throughout all the customer history during his lifecycle. The explored sensitivity of indicators suggests application of different neural networks fitting to the particular stages of the customer lifetime cycle. The neural network analysis results were summarized and the research insights presented, based on the dynamics of sensitivity of the customer indicators. © 2010 Springer-Verlag.