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Author: Tatiana L. Dyachenko
Publisher:
ISBN:
Category :
Languages : en
Pages : 130
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Book Description
The goal of better understanding consumers and making better marketing decisions drives development of new research methods and modification of existing ones. Both experimental consumer research and marketing modeling areas have been working toward this goal. However, there has been limited exchange of conceptual and methodological developments between the fields. We believe that integrating knowledge of contextual effects from psychology and consumer behavior with mathematical rigor of modeling would benefit the field of marketing research. It will allow researchers to uncover relationships that are difficult, if not impossible, to find with just one approach. We present two cases that demonstrate methodological and substantive benefits of this interaction. In the first essay, we bring psychological research literature into modeling consumer choices. In the second essay, we demonstrate how statistical methodology can be applied to mediation analysis commonly used in behavioral research. We show how adherence to the likelihood-based approach provides a common ground for this interaction. The results show that this merging of expertise has practical and methodological implications: the best performing proposed choice model in the first essay changes inferences about consumer preferences from what is concluded by traditional models; and conclusions about the presence of mediating mechanisms presented in the second essay could differ from results of the traditional methods.
Author: Tatiana L. Dyachenko
Publisher:
ISBN:
Category :
Languages : en
Pages : 130
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Book Description
The goal of better understanding consumers and making better marketing decisions drives development of new research methods and modification of existing ones. Both experimental consumer research and marketing modeling areas have been working toward this goal. However, there has been limited exchange of conceptual and methodological developments between the fields. We believe that integrating knowledge of contextual effects from psychology and consumer behavior with mathematical rigor of modeling would benefit the field of marketing research. It will allow researchers to uncover relationships that are difficult, if not impossible, to find with just one approach. We present two cases that demonstrate methodological and substantive benefits of this interaction. In the first essay, we bring psychological research literature into modeling consumer choices. In the second essay, we demonstrate how statistical methodology can be applied to mediation analysis commonly used in behavioral research. We show how adherence to the likelihood-based approach provides a common ground for this interaction. The results show that this merging of expertise has practical and methodological implications: the best performing proposed choice model in the first essay changes inferences about consumer preferences from what is concluded by traditional models; and conclusions about the presence of mediating mechanisms presented in the second essay could differ from results of the traditional methods.
Author: Asit K. Banerjee
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 137
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Gordon Foxall
Publisher: Routledge
ISBN: 1135238081
Category : Business & Economics
Languages : en
Pages : 370
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Book Description
Interpretive consumer research usually proceeds with a minimum of structure and preconceptions. This book presents a more structured approach than is usual, showing how a simple framework that embodies the rewards and costs associated with consumer choice can be used to interpret a wide range of consumer behaviours from everyday purchasing and saving, innovative choice, imitation, ‘green’ consumer behavior, to compulsive behaviors such as addictions (to shopping, to gambling, to alcohol and other drugs, etc). Foxall takes a qualitative approach to interpreting behavior, focusing on the epistemological problems that arise in such research and emphasizing the emotional as well as cognitive aspects of consumption. The author argues that consumer behaviour can be understood with the aid of a very simple model that proposes how the consequences of consumption impact consumers’ subsequent choices. The objective is to show that a basic model can be used to interpret consumer behaviour in general, not in isolation from the marketing influences that shape it, but as a course of human choice that is dynamically linked with managerial concerns.
Author: Peter E. Rossi
Publisher: John Wiley & Sons
ISBN: 0470863676
Category : Mathematics
Languages : en
Pages : 387
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Book Description
The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods. Written by the leading experts in the field, this unique book: Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models. Provides a self-contained introduction to Bayesian methods. Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems. Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies. Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.
Author: Julie Esther Novak
Publisher:
ISBN:
Category :
Languages : en
Pages : 168
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Book Description
Tailoring content to consumers has become a hallmark of marketing and digital media, particularly as it has become easier to identify customers across usage or purchase occasions. However, across a wide variety of contexts, companies find that customers do not consistently identify themselves, leaving a substantial fraction of anonymous visits. We develop a Bayesian hierarchical model that allows us to probabilistically assign anonymous sessions to users. These probabilistic assignments take into account a customer's demographic information, frequency of visitation, activities taken when visiting, and times of arrival. We present two studies, one with synthetic and one with real data, where we demonstrate improved performance over two popular practices (nearest-neighbor matching and deleting the anonymous visits) due to increased efficiency and reduced bias driven by the non-ignorability of which types of events are more likely to be anonymous. Using our proposed model, we avoid potential bias in understanding the effect of a firm's marketing on its customers, improve inference about the total number of customers in the dataset, and provide more precise targeted marketing to both previously observed and unobserved customers.
Author: Jeff Gill
Publisher: CRC Press
ISBN: 1420010824
Category : Mathematics
Languages : en
Pages : 696
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Book Description
The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorpora
Author: Ling Luo
Publisher: Springer
ISBN: 3030182894
Category : Technology & Engineering
Languages : en
Pages : 131
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Book Description
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.
Author: Andrew Gelman
Publisher: CRC Press
ISBN: 1439840954
Category : Mathematics
Languages : en
Pages : 677
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Book Description
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Author: Simon Jackman
Publisher: John Wiley & Sons
ISBN: 9780470686638
Category : Mathematics
Languages : en
Pages : 598
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Book Description
Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.
