Temporal Modelling of Customer Behaviour

  • Ling Luo

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Ling Luo
    Pages 1-6
  3. Ling Luo
    Pages 7-14
  4. Ling Luo
    Pages 15-27
  5. Ling Luo
    Pages 119-122
  6. Back Matter
    Pages 123-123

About this book


This book describes advanced machine learning models – such as temporal collaborative filtering, stochastic models and Bayesian nonparametrics – for analysing customer behaviour. It shows how they are used to track changes in customer behaviour, monitor the evolution of customer groups, and detect various factors, such as seasonal effects and preference drifts, that may influence customers’ purchasing behaviour. In addition, the book presents four case studies conducted with data from a supermarket health program in which the customers were segmented and the impact of promotional activities on different segments was evaluated. The outcomes confirm that the models developed here can be used to effectively analyse dynamic behaviour and increase customer engagement. Importantly, the methods introduced here can also be used to analyse other types of behavioural data such as activities on social networks, and educational systems.


Customer Behaviour Analysis Tracking Customer Behaviour Temporal Aspects of Customer Behaviour Customer Segmentation Model (CSM) Fragmentation-coagulation (FC) Process Temporal Collaborative Filtering Temporal Preference Model Customer Response to Promotions Dynamic Model of Customer Behaviour Temporal Purchase Patterns Evolution of Customer Purchasing Latent Variable Models Gibbs Sampling Web-Based Supermarket Health Program Non-homogeneous Poisson Processes Bayesian Nonparametrics Mixture Modelling

Authors and affiliations

  • Ling Luo
    • 1
  1. 1.School of Computer ScienceThe University of SydneySydneyAustralia

Bibliographic information

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