Overview
- Nominated as an outstanding Ph.D. thesis by the University of Sydney, Australia
- Presents innovative machine learning techniques for modelling dynamic customer purchasing behaviour
- Reviews cutting-edge clustering techniques for temporal behavioural data
- Highlights applications in the assessment of web-based health programs and supermarket promotions
Part of the book series: Springer Theses (Springer Theses)
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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.
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Keywords
- 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
Table of contents (8 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Temporal Modelling of Customer Behaviour
Authors: Ling Luo
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-030-18289-2
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-18288-5Published: 08 May 2019
eBook ISBN: 978-3-030-18289-2Published: 27 April 2019
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XV, 123
Number of Illustrations: 4 b/w illustrations, 35 illustrations in colour
Topics: Computational Intelligence, Consumer Behavior, Machine Learning, Market Research/Competitive Intelligence, Health Promotion and Disease Prevention