Temporal Modelling of Customer Behaviour

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.

Sommario

Introduction.- Datasets .- Literature Review .- Tracking Purchase Behaviour Change.- Discovering Purchase Behaviour Patterns .-  Evaluating Impact of the Web-based Health Program .- Tracking the Evolution of Customer Segmentations .- Conclusions and Future Work. 

Riassunto

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.

Dettagli sul prodotto

• Autori: Ling Luo
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 01.01.2019
• EAN: 9783030182885
• ISBN: 978-3-0-3018288-5
• Pagine: 123
• Dimensioni: 162 mm x 239 mm x 14 mm
• Peso: 364 g
• Illustrazioni: XV, 123 p. 39 illus., 35 illus. in color.
• Serie: Springer Theses
• Categorie: Scienze naturali, medicina, informatica, tecnica > Tecnica > Tematiche generali, enciclopedie; Marketing, Marktforschung, B, Arbeits-, Wirtschafts- und Organisationspsychologie, machine learning, Maschinelles Lernen, Public Health und Präventivmedizin, Market research, engineering, health promotion, Market Research/Competitive Intelligence, Public health & preventive medicine, Sales & marketing, Consumer behavior, Computational Intelligence, Health Promotion and Disease Prevention, Motivation research (Marketing), Public health and preventive medicine, Market Research and Competitive Intelligence, Mixture Modelling, Bayesian Nonparametrics