Marketing Models Of Consumer Demand
DOWNLOAD
Download Marketing Models Of Consumer Demand PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Marketing Models Of Consumer Demand book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Marketing Models Of Consumer Demand
DOWNLOAD
Author : Pradeep Chintagunta
language : en
Publisher:
Release Date : 2010
Marketing Models Of Consumer Demand written by Pradeep Chintagunta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Consumer behavior categories.
Marketing Models Of Consumer Demand
DOWNLOAD
Author : Pradeep Chintagunta
language : en
Publisher:
Release Date : 2010
Marketing Models Of Consumer Demand written by Pradeep Chintagunta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Consumer behavior categories.
Discrete Choice Models Of Consumer Demand In Marketing
DOWNLOAD
Author : Pradeep K. Chintagunta
language : en
Publisher:
Release Date : 2011
Discrete Choice Models Of Consumer Demand In Marketing written by Pradeep K. Chintagunta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Consumer behavior categories.
This paper has three main objectives. The first objective is to articulate the main goals of demand analysis - forecasting, measurement and testing - and to highlight several considerations associated with these goals. Our second objective is describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility specifications of demand systems, and review extensions that have appeared in the Marketing literature. The third objective is to explore a few emerging directions in demand analysis including considering demand-side dynamics; combining purchase data with primary information; and using semiparametric and nonparametric approaches. We hope researchers new to this literature will take away a broader perspective on these models and see potential for new directions in future research.
Studies In Consumer Demand Econometric Methods Applied To Market Data
DOWNLOAD
Author : Jeffrey A. Dubin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Studies In Consumer Demand Econometric Methods Applied To Market Data written by Jeffrey A. Dubin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
Studies in Consumer Demand - Econometric Methods Applied to Market Data contains eight previously unpublished studies of consumer demand. Each study stands on its own as a complete econometric analysis of demand for a well-defined consumer product. The econometric methods range from simple regression techniques applied in the first four chapters, to the use of logit and multinomial logit models used in chapters 5 and 6, to the use of nested logit models in chapters 6 and 7, and finally to the discrete/continuous modeling methods used in chapter 8. Emphasis is on applications rather than econometric theory. In each case, enough detail is provided for the reader to understand the purpose of the analysis, the availability and suitability of data, and the econometric approach to measuring demand.
Consumer Driven Demand And Operations Management Models
DOWNLOAD
Author : Serguei Netessine
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-06-02
Consumer Driven Demand And Operations Management Models written by Serguei Netessine and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-02 with Business & Economics categories.
This important book is by top scholars in supply chain management, revenue management, and e-commerce, all of which are grounded in information technologies and consumer demand research. The book looks at new selling techniques designed to reach the consumer.
Consumer Demand In The United States
DOWNLOAD
Author : Lester D. Taylor
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-11-25
Consumer Demand In The United States written by Lester D. Taylor and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-25 with Business & Economics categories.
A classic treatise that defined the field of applied demand analysis, Consumer Demand in the United States: Prices, Income, and Consumption Behavior is now fully updated and expanded for a new generation. Consumption expenditures by households in the United States account for about 70% of America’s GDP. The primary focus in this book is on how households adjust these expenditures in response to changes in price and income. Econometric estimates of price and income elasticities are obtained for an exhaustive array of goods and services using data from surveys conducted by the Bureau of Labor Statistics and aggregate consumption expenditures from the National Income and Product Accounts, providing a better understanding of consumer demand. Practical models for forecasting future price and income elasticities are also demonstrated. Fully revised with over a dozen new chapters and appendices, the book revisits the original Houthakker-Taylor models while examining new material as well, such as the use of quantile regression and the stationarity of consumer preference. It also explores the emerging connection between neuroscience and consumer behavior, integrating the economic literature on demand theory with psychology literature. The most comprehensive treatment of the topic to date, this volume will be an essential resource for any researcher, student or professional economist working on consumer behavior or demand theory, as well as investors and policymakers concerned with the impact of economic fluctuations.
Microeconometric Models Of Consumer Demand
DOWNLOAD
Author : Jean-Pierre H. Dubé
language : en
Publisher:
Release Date : 2018
Microeconometric Models Of Consumer Demand written by Jean-Pierre H. Dubé and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
A long literature has developed econometric methods for estimating individual-consumer-level demand systems that accommodate corner solutions. The increasing access to transaction-level customer purchase histories across a wide array of markets and industries vastly expands the prospect for improved customer insight, more targeted marketing policies and individualized welfare analysis. A descriptive analysis of a broad, CPG database indicates that most consumer brand categories offer a wide variety of differentiated offerings for consumers. However, consumers typically purchase only a limited scope of the available variety, leading to a very high incidence of corner solutions which poses computational challenges for demand modeling. Historically, these computational challenges have limited the applicability of microeconometric models of demand in practice, except for the special case of pure discrete choice (e.g., logit and probit). Recent advances in computing power along with methods for numerical and simulation-based integration have been instrumental in facilitating the broader use of these models in practice. We survey herein the extant literature on the neoclassical derivation of microeconometric demand models that allow for corner solutions and differentiated products. We summarize the key developments in the literature, including the role of consumers' price expectations, and point towards opportunities for future research.
Studies In Consumer Demand Econometric Methods Applied To Market Data
DOWNLOAD
Author : Jeffrey A Dubin
language : en
Publisher:
Release Date : 1998-07-31
Studies In Consumer Demand Econometric Methods Applied To Market Data written by Jeffrey A Dubin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-07-31 with categories.
Scalable Models Of Consumer Demand With Large Choice Sets
DOWNLOAD
Author : Robert Nathanael Donnelly
language : en
Publisher:
Release Date : 2019
Scalable Models Of Consumer Demand With Large Choice Sets written by Robert Nathanael Donnelly and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
This dissertation consists of three essays related to the analysis of heterogeneity in consumer preferences based on individual level data on historical choices. In particular, they are connected by their application of modern Bayesian approaches to model consumers who differ both in their preferences for observed characteristics as well as their preferences for characteristics that are unobserved by the econometrician, but can instead be inferred from the correlations in choice behavior across different subsets of the population of consumers. The three chapters of this dissertation are also connected by their focus on scalability (both in computation and statistical efficiency) to large choice sets. Large choice sets are all around us, and the rise of E-commerce is leading to even larger sets of products that consumers can choose between. The average grocery store has tens of thousands of unique SKUs. The South Bay region around Stanford University has thousands of restaurants to choose between when you decide to go out for lunch. Large web retailers like Amazon sell hundreds of millions of distinct items. Individual level data on choices in situations like these present both opportunities and challenges. While these data sources are often large and rich in information, it is almost always the case that the number of choice occasions that we observe for any single individual is very small relative to the number of possible items they could have chosen between. Some types of products are easily described as a bundle of characteristics that consumers have preferences over, for example cars (horsepower, number of doors, leather seats) or digital cameras (resolution, zoom, flash), however for many other product categories it is more difficult to find a ''feature representation'' of products that accurately captures the heterogeneity in preferences across consumers. What are the characteristics that differ between Coke and Pepsi that lead to such strong disagreements over which is best. My work builds on recently developed approaches from machine learning for estimating models with large numbers of latent variables. This allows us to infer latent ''characteristics'' of products that are not directly observed by the econometrician, but can be inferred based on similarities in choice patterns across a large set of consumers. This allows us to model consumer preferences with heterogeneity in preferences for both observed and unobserved product characteristics. The first chapter of this dissertation is a paper written together with Susan Athey, David Blei, Francisco Ruiz, and Tobias Schmidt which analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each restaurant has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant opens or closes and compare our predictions to the actual realized outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location. The second chapter is a paper written together with Susan Athey, David Blei, and Francisco Ruiz applies a similar approach in the context of supermarket scanner data. This paper demonstrates a method for estimating consumer preferences among discrete choices, where the consumer makes choices from many different categories. The consumer's utility is additive in the different categories, and her preferences about product attributes as well as her price sensitivity vary across products. Her preferences are correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes, a more realistic functional form for price sensitivity, and products going out of stock. We incorporate the information about the product hierarchy, so that consumers are assumed to select at most one alternative within a category. We evaluate the performance of the model using held-out data from weeks with price changes. We show that our model improves over traditional modeling approaches that consider each category in isolation, when we evaluate the ability of the model to predict responsiveness to price changes (using held-out data from a large number of price changes that occurred in our sample). We show that one source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts. The third chapter of this dissertation proposes a novel estimator for learning heterogeneous consumer preferences based on both browsing and purchase data from online retailers with large product assortments. This work was done in collaboration with Ilya Morozov. Despite increasing availability data on the product pages consumers browse prior to making a purchase, the existing marketing literature provides little guidance on how retailers can use it to make better marketing decisions. In this paper, we propose an empirical framework that allows to efficiently extract information from consumers' search histories and use it to design personalized product recommendations. Our framework is based on the standard consideration set model from the marketing literature. To extract information from the unstructured search data, we augment the model with rich consumer heterogeneity and include several unobserved product characteristics. We then propose a way to estimate this model's parameters using a latent factorization approach from the computer science literature. The proposed framework can be seen as combining a structural approach to modeling consumer consideration from marketing with nonparametric estimation methods commonly used in the computer science. We are in discussion with a large online retailer to gain access to data and to run an AB test to experimentally validate the effects of improved rankings and recommendations of products.
Choice Models In Marketing
DOWNLOAD
Author : Sandeep R. Chandukala
language : en
Publisher: Now Publishers Inc
Release Date : 2008
Choice Models In Marketing written by Sandeep R. Chandukala and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Choice Models in Marketing examines recent developments in the modeling of choice for marketing and reviews a large stream of research currently being developed by both quantitative and qualitative researches in marketing. Choice in marketing differs from other domains in that the choice context is typically very complex, and researchers' desire knowledge of the variables that ultimately lead to demand in marketplace. The marketing choice context is characterized by many choice alternatives. The aim of Choice Models in Marketing is to lay out the foundations of choice models and discuss recent advances. The authors focus on aspects of choice that can be quantitatively modeled and consider models related to a process of constrained utility maximization. By reviewing the basics of choice modeling and pointing to new developments, Choice Models in Marketing provides a platform for future research.