Stochastic Models of Buying Behavior


Book Description

Approaches to stochastic modeling; Estimating and testing stochastic models; Brand-choice models; Zero-order models; Two state markov models; Linear learning models for brand choice; A probability diffusion model; Application of the probability diffusion model; Purchase incidence models; Models for purchase timing and market penetration; A stochastic model for monitoring new product adoption; Parameter estimations and some emperical results for STEAM; Extension to STEAM.










Stochastic Modeling of Consumer Purchase Behavior


Book Description

This paper develops alternative brand purchase models. These models are based on distinct assumptions about the product class purchasing process over a fixed time-period. In each case, the brand choice process conditioned on a product purchase being made is assumed to be heterogeneous zero order. New analytical closed-form results are derived. These results include various market statistics such as the brand penetration, the mean and variance of the brand purchase distribution and the aggregate brand purchase distribution itself. These theoretical expressions are based on the assumption of independence between brand choice probability and mean product purchase rate across the population.




Temporal Modelling of Customer Behaviour


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.




Models of Buyer Behavior


Book Description

This edited book, discusses thorough and wide-ranging theories and models associated with differing aspects of buyer behavior from a team of marketing experts. Combines conceptual and theoretical basics of marketing discipline. Part 1 focuses on Armstrong's views on the ideological and practical strategy of conducting research to substantiate concepts and a network of concepts that comprises a theory. Part 2 centers on the encompassing models of buyer behavior. Part 3 assimilates the extensive models of innovative behavior and adoption process. Part 4 consists of papers which provide models of consumer classification and market segmentation. Part 5 includes a theoretical analysis of the changes which are likely to emerge in buyer behavior theory and research.This Classic Book was originally published in 1974 by Harper and Row.Dr. Jagdish (Jag) N. Sheth is the Charles H. Kellstadt Professor of Marketing in the Goizueta Business School at Emory University. Prior positions, include the University of Southern California; the University of Illinois; the faculty of Columbia University; and, the Massachusetts Institute of Technology. Dr. Sheth is well known for his scholarly contributions in consumer behavior, relationship marketing, competitive strategy and geopolitical analysis.







Building Models for Marketing Decisions


Book Description

This book is about marketing models and the process of model building. Our primary focus is on models that can be used by managers to support marketing decisions. It has long been known that simple models usually outperform judgments in predicting outcomes in a wide variety of contexts. For example, models of judgments tend to provide better forecasts of the outcomes than the judgments themselves (because the model eliminates the noise in judgments). And since judgments never fully reflect the complexities of the many forces that influence outcomes, it is easy to see why models of actual outcomes should be very attractive to (marketing) decision makers. Thus, appropriately constructed models can provide insights about structural relations between marketing variables. Since models explicate the relations, both the process of model building and the model that ultimately results can improve the quality of marketing decisions. Managers often use rules of thumb for decisions. For example, a brand manager will have defined a specific set of alternative brands as the competitive set within a product category. Usually this set is based on perceived similarities in brand characteristics, advertising messages, etc. If a new marketing initiative occurs for one of the other brands, the brand manager will have a strong inclination to react. The reaction is partly based on the manager's desire to maintain some competitive parity in the mar keting variables.




Models of Buyer Behavior, Chapter 14


Book Description