Temporal Modelling of Customer Behaviour
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Beschreibung
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.
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.
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
Autor*in
Ling Luo
Themen in »Temporal Modelling of Customer Behaviour«
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