Temporal Modelling of Customer Behaviour (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Springer Theses
Release date:	May 2019
First published:	2020
Authors:	Ling Luo
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	123
Edition:	1st ed. 2020
ISBN-13:	978-3-03-018288-5
Categories:	Books > Business & Economics > Business & management > Sales & marketing > Market research Books > Medicine > General issues > Public health & preventive medicine > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-018288-6
Barcode:	9783030182885

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