A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport

Subrata Chakraborty,David Lassen,Lin Ma,Kerrie Mengersen,Colin Fidge

doi:10.1186/s40165-016-0021-2

Abstract

We formalise and present an innovative general approach for developing complex system models from survey data by applying Bayesian Networks. The challenges and approaches to converting survey data into usable probability forms are explained and a general approach for integrating expert knowledge (judgements) into Bayesian complex system models is presented. The structural complexities of the Bayesian complex system modelling process, based on various decision contexts, are also explained along with a solution. A novel application of Bayesian complex system models as a management tool for decision making is demonstrated using a railway transport case study. Customer satisfaction, which is a Key Performance Indicator in public transport management, is modelled using data from customer surveys conducted by Queensland Rail, Australia.

Highlights

The success of a business is reflected in its established Key Performance Indicators (KPIs)
In this article we show how Bayesian Networks (BNs) can be developed and used as an effective management tool for KPI analysis where the KPIs represent the overall performances of various factors with objective and subjective performance measures
Case study As a concrete illustration of the new approach, here we describe a large-scale case study in which a Bayesian Network model was developed from survey data collected by a major public transport utility

Summary

Introduction

The success of a business is reflected in its established Key Performance Indicators (KPIs). There are a wealth of methods for analysing customer satisfaction surveys These include summary statistical evaluations, factor analysis and its variants including customer satisfaction indices (Fornell et al 1996; Kristensen et al 2000; Johnson et al 2001), linear regression and its variants (Ting and Chen 2002; Chatterjee and Hadi 2008), nonparametric non-linear approaches such as classification and regression trees (Death and Fabricius 2000), latent factor approaches such as structural equation models (Hackl and Westlund 2000), multi-criteria approaches (Siskos et al 1998), and so on. We follow this with a discussion of the case study and an exposition of the corresponding analyses

Background

Conclusion