Temporal modelling of customer behaviour /

Temporal modelling of customer behaviour /

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Bibliographic Details
Author / Creator:	Luo, Ling, author.
Imprint:	Cham, Switzerland : Palgrave Macmillan, [2020] ©2020
Description:	1 online resource.
Language:	English
Series:	Springer theses: recognizing outstanding Ph.D. research Springer theses.
Subject:	Consumer behavior -- Mathematical models. Marketing -- Mathematical models. Consumer behavior -- Mathematical models. Marketing -- Mathematical models.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12601415

Hidden Bibliographic Details
ISBN:	9783030182892 3030182894 9783030182885
Notes:	Includes bibliographical references. Online resource; title from PDF title page (EBSCO, viewed April 30, 2019).
Summary:	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.

Table of Contents:

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. .