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Author: Saisandeep Reddy Satyavolu
Publisher:
ISBN:
Category :
Languages : en
Pages : 126
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Book Description
The proliferation of available data in marketing has placed an emphasis on the applicability of extant marketing models to big data. To tackle this problem, methods from machine learning have been increasingly applied by the marketing community. This line of research is a subset of research in marketing that is becoming interdisciplinary. A number of marketing researchers have successfuly adopted methods from other seemingly unrelated fields in their research. In that vein, this thesis examines the applicability of Bayesian Nonparametric methods (from the field of machine learning) to marketing. The first chapter of this thesis provides a very brief survey of marketing research papers that have enhanced pure marketing models using methods from machine learning. The second chapter describes the Dirichlet Process, a key component of Bayesian Nonparametric analysis and provides two synthetic data applications. Going forward, we study the applicability of Bayesian Nonparametric methods to model Heterogeneity across multiple markets. Bayesian Nonparametric methods have been used in marketing and economics literature to model heterogeneity in discrete choice models, but past applications have only been limited to data from a single market. So as to compare heterogeneity in consumer preferences across multiple markets, we use the Hierarchical Dirichlet Process (HDP) which lets multiple "groups" of data "share statistical strength". Heterogeneity across multiple markets is modeled using the HDP in two different contexts (B2C and B2B) in this thesis. Our work shows that the HDP provides a convenient "middle ground" to other extreme modeling options, which are (1) ignore heterogeneity of preferences across markets and (2) model each market separately. Another aspect of the HDP is the ease with which it can be incorporated into models of discrete choice. The models developed and estimated in this thesis are also helpful for the marketing manager. In the B2C application, the results of the model provide the manager with a practical way of tailoring targeting activities towards consumers with varying preferences. Finally, in the B2B application, we find that based on the Stage of the selling process, some marketing activities play a larger role than others in converting sales leads into clients. These results provide a data driven basis for the manager to appropriately allocate marketing dollars to activities based on the selling process.
Author: Saisandeep Reddy Satyavolu
Publisher:
ISBN:
Category :
Languages : en
Pages : 126
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Book Description
The proliferation of available data in marketing has placed an emphasis on the applicability of extant marketing models to big data. To tackle this problem, methods from machine learning have been increasingly applied by the marketing community. This line of research is a subset of research in marketing that is becoming interdisciplinary. A number of marketing researchers have successfuly adopted methods from other seemingly unrelated fields in their research. In that vein, this thesis examines the applicability of Bayesian Nonparametric methods (from the field of machine learning) to marketing. The first chapter of this thesis provides a very brief survey of marketing research papers that have enhanced pure marketing models using methods from machine learning. The second chapter describes the Dirichlet Process, a key component of Bayesian Nonparametric analysis and provides two synthetic data applications. Going forward, we study the applicability of Bayesian Nonparametric methods to model Heterogeneity across multiple markets. Bayesian Nonparametric methods have been used in marketing and economics literature to model heterogeneity in discrete choice models, but past applications have only been limited to data from a single market. So as to compare heterogeneity in consumer preferences across multiple markets, we use the Hierarchical Dirichlet Process (HDP) which lets multiple "groups" of data "share statistical strength". Heterogeneity across multiple markets is modeled using the HDP in two different contexts (B2C and B2B) in this thesis. Our work shows that the HDP provides a convenient "middle ground" to other extreme modeling options, which are (1) ignore heterogeneity of preferences across markets and (2) model each market separately. Another aspect of the HDP is the ease with which it can be incorporated into models of discrete choice. The models developed and estimated in this thesis are also helpful for the marketing manager. In the B2C application, the results of the model provide the manager with a practical way of tailoring targeting activities towards consumers with varying preferences. Finally, in the B2B application, we find that based on the Stage of the selling process, some marketing activities play a larger role than others in converting sales leads into clients. These results provide a data driven basis for the manager to appropriately allocate marketing dollars to activities based on the selling process.
Author: Yuhao Fan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
I explore the application of Bayesian statistical modelling, and in particular Bayesian nonparametric methods in marketing research. I apply Bayesian nonparametric methods in both chapters of my dissertation to model two types of customer dynamics.In the first chapter, I investigate the impact of implementing a free cancellation program on customer behavior and firm profits in a hostel booking setting. While many firms have recently introduced free cancellation programs, the impact of such programs on customer behavior and firm profits remains unclear. I investigate this question empirically, using data from a hostel booking platform that recently introduced a free cancellation program. To understand the program's impact on a myriad of aspects of customer behavior, including booking timing, spend amount, and propensity to cancel, while also accounting for latent attrition and customer heterogeneity, I build a hierarchical, Bayesian nonparametric model of behavior, leveraging Gaussian process change points to capture the effect of the free cancellation program on booking dynamics, and a Dirichlet process mixture specification for customer heterogeneity. These nonparametric components of the model allow us to make minimal assumptions about important aspects of booking behavior, while uncovering rich insights about the time-varying impact of the program, and the heterogeneity of customers. Our results suggest that the free cancellation program led customers to book more frequently, book earlier, spend more, and cancel more of their trips. Crucially, the increase in bookings generally outweighed the increase in cancellations in long term, resulting in an increase in average customer lifetime value.In the second chapter, I apply Bayesian nonparametric methods, in particular, Multi-output Gaussian Process, to model the cross-category dynamics of customers' preference parameters in brand choice models. I show that the proposed model allows us to transfer information about customers' preference parameters within and across categories, and that modelling the cross-category dynamics of customers' preference parameters improves model fit and prediction accuracy. Moreover, leveraging information across categories gives us more reliable estimates of price elasticities. Together, these two chapters illustrate the power of Bayesian methods to gain deep insights into dynamic marketing problems.
Author: Peter Rossi
Publisher: Princeton University Press
ISBN: 1400850304
Category : Business & Economics
Languages : en
Pages : 219
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Book Description
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Author: Peter E. Rossi
Publisher:
ISBN:
Category :
Languages : en
Pages : 214
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Book Description
I review and develop Bayesian non-parametric and semi-parametric methods based on finite and infinite mixtures of normals. Applications include regression, IV methods, and random coefficient models.
Author: J.K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387226540
Category : Mathematics
Languages : en
Pages : 311
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Book Description
This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Author: Dipak D. Dey
Publisher: Springer Science & Business Media
ISBN: 1461217326
Category : Mathematics
Languages : en
Pages : 376
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Book Description
A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Author: Brian Matthew Hartman
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Global marketing managers are keenly interested in being able to predict the sales of their new products. Understanding how a product is adopted over time allows the managers to optimally allocate their resources. With the world becoming ever more global, there are strong and complex interactions between the countries in the world. My work explores how to describe the relationship between those countries and determines the best way to leverage that information to improve the sales predictions. In Chapter II, I describe how diffusion speed has changed over time. The most recent major study on this topic, by Christophe Van den Bulte, investigated new product di ffusions in the United States. Van den Bulte notes that a similar study is needed in the international context, especially in developing countries. Additionally, his model contains the implicit assumption that the diffusion speed parameter is constant throughout the life of a product. I model the time component as a nonparametric function, allowing the speed parameter the flexibility to change over time. I find that early in the product's life, the speed parameter is higher than expected. Additionally, as the Internet has grown in popularity, the speed parameter has increased. In Chapter III, I examine whether the interactions can be described through a reference hierarchy in addition to the cross-country word-of-mouth eff ects already in the literature. I also expand the word-of-mouth e ffect by relating the magnitude of the e ffect to the distance between the two countries. The current literature only applies that e ffect equally to the n closest countries (forming a neighbor set). This also leads to an analysis of how to best measure the distance between two countries. I compare four possible distance measures: distance between the population centroids, trade ow, tourism ow, and cultural similarity. Including the reference hierarchy improves the predictions by 30 percent over the current best model. Finally, in Chapter IV, I look more closely at the Bass Diffusion Model. It is prominently used in the marketing literature and is the base of my analysis in Chapter III. All of the current formulations include the implicit assumption that all the regression parameters are equal for each country. One dollar increase in GDP should have more of an eff ect in a poor country than in a rich country. A Dirichlet process prior enables me to cluster the countries by their regression coefficients. Incorporating the distance measures can improve the predictions by 35 percent in some cases.
Author: Peter E. Rossi
Publisher: John Wiley & Sons
ISBN: 1394219113
Category : Business & Economics
Languages : en
Pages : 405
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Book Description
Fine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.
Author: Herbert K. H. Lee
Publisher: SIAM
ISBN: 9780898718423
Category : Mathematics
Languages : en
Pages : 106
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Book Description
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.
Author: Peter Rossi
Publisher: Princeton University Press
ISBN: 0691145326
Category : Business & Economics
Languages : en
Pages : 218
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Book Description
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
