Statistical Local Models for Customer Satisfaction Data

Abstract

This paper considers statistical method for customer satisfaction data. Section 1 reports a short introduction. Section 2 describes our methodology to measure customer satisfaction: first we discuss the application of discrete graphical models, then we propose a theoretical approach to mixture different type of customer data following a dynamic reasoning approach. Section 3 presents the application based on a real set of information available on the website http://www.economia.unimi.it/projects/CSProject/. Finally, Section 4 shows the conclusions and further ideas of research.

Key Words:

• Customer satisfaction
• customer dataset
• dynamic models
• graphical models
• lifetime value

Additional information

Notes on contributors

Silvia Figini

Silvia Figini BS in Economics, University of Pavia University, 2001 PhD in Statistics, Bocconi University, dissertation on “Bayesian variable and model selection for Customer Lifetime Value”. At the University of Pavia she is research assistant for business statistics and data mining under the supervision of Prof. Paolo Giudici. Before joining the PhD program has worked for two years for Competence centre of data mining analysis and business intelligence in SAS Milan. Currently, she is also a member of the Italian Statistical Society. Research interests: Feature selection, Survival Analysis for lifetime models, Bayesian statistics, Computational Statistics.