{"product_id":"bayesian-statistics-and-marketing-isbn-9781394219117","title":"Bayesian Statistics and Marketing","description":"\u003cp\u003e\u003cb\u003eFine-tune your marketing research with this cutting-edge statistical toolkit\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBayesian Statistics and Marketing \u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eEconomists 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. \u003c\/p\u003e\u003cp\u003eReaders of the second edition of \u003ci\u003eBayesian Statistics and Marketing \u003c\/i\u003ewill also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDiscussion of Bayesian methods in text analysis and Machine Learning \u003c\/li\u003e\n\u003cli\u003eUpdates throughout reflecting the latest research and applications \u003c\/li\u003e\n\u003cli\u003eDiscussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here \u003c\/li\u003e\n\u003cli\u003eExtensive case studies throughout to link theory and practice\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBayesian Statistics and Marketing\u003c\/i\u003e is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner. \u003c\/p\u003e\u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 A Basic Paradigm for Marketing Problems 2\u003c\/p\u003e \u003cp\u003e1.2 A Simple Example 3\u003c\/p\u003e \u003cp\u003e1.3 Benefits and Costs of the Bayesian Approach 5\u003c\/p\u003e \u003cp\u003e1.4 An Overview of Methodological Material and Case Studies 6\u003c\/p\u003e \u003cp\u003e1.5 Approximate Bayes Methods and This Book 7\u003c\/p\u003e \u003cp\u003e1.6 Computing and This Book 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Bayesian Essentials 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Essential Concepts from Distribution Theory 11\u003c\/p\u003e \u003cp\u003e2.2 The Goal of Inference and Bayes Theorem 15\u003c\/p\u003e \u003cp\u003e2.3 Conditioning and the Likelihood Principle 16\u003c\/p\u003e \u003cp\u003e2.4 Prediction and Bayes 17\u003c\/p\u003e \u003cp\u003e2.5 Summarizing the Posterior 17\u003c\/p\u003e \u003cp\u003e2.6 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators 18\u003c\/p\u003e \u003cp\u003e2.7 Identification and Bayesian Inference 20\u003c\/p\u003e \u003cp\u003e2.8 Conjugacy, Sufficiency, and Exponential Families 21\u003c\/p\u003e \u003cp\u003e2.9 Regression and Multivariate Analysis Examples 23\u003c\/p\u003e \u003cp\u003e2.10 Integration and Asymptotic Methods 37\u003c\/p\u003e \u003cp\u003e2.11 Importance Sampling 38\u003c\/p\u003e \u003cp\u003e2.12 Simulation Primer for Bayesian Problems 42\u003c\/p\u003e \u003cp\u003e2.13 Simulation from Posterior of Multivariate Regression Model 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 MCMC Methods 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 MCMC Methods 50\u003c\/p\u003e \u003cp\u003e3.2 A Simple Example: Bivariate Normal Gibbs Sampler 52\u003c\/p\u003e \u003cp\u003e3.3 Some Markov Chain Theory 57\u003c\/p\u003e \u003cp\u003e3.4 Gibbs Sampler 63\u003c\/p\u003e \u003cp\u003e3.5 Gibbs Sampler for the SUR Regression Model 64\u003c\/p\u003e \u003cp\u003e3.6 Conditional Distributions and Directed Graphs 66\u003c\/p\u003e \u003cp\u003e3.7 Hierarchical Linear Models 69\u003c\/p\u003e \u003cp\u003e3.8 Data Augmentation and a Probit Example 74\u003c\/p\u003e \u003cp\u003e3.9 Mixtures of Normals 78\u003c\/p\u003e \u003cp\u003e3.10 Metropolis Algorithms 85\u003c\/p\u003e \u003cp\u003e3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model 92\u003c\/p\u003e \u003cp\u003e3.12 Hybrid MCMC Methods 95\u003c\/p\u003e \u003cp\u003e3.13 Diagnostics 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Unit-Level Models and Discrete Demand 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Latent Variable Models 104\u003c\/p\u003e \u003cp\u003e4.2 Multinomial Probit Model 106\u003c\/p\u003e \u003cp\u003e4.3 Multivariate Probit Model 116\u003c\/p\u003e \u003cp\u003e4.4 Demand Theory and Models Involving Discrete Choice 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Hierarchical Models for Heterogeneous Units 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Heterogeneity and Priors 130\u003c\/p\u003e \u003cp\u003e5.2 Hierarchical Models 132\u003c\/p\u003e \u003cp\u003e5.3 Inference for Hierarchical Models 134\u003c\/p\u003e \u003cp\u003e5.4 A Hierarchical Multinomial Logit Example 137\u003c\/p\u003e \u003cp\u003e5.5 Using Mixtures of Normals 143\u003c\/p\u003e \u003cp\u003e5.6 Further Elaborations of the Normal Model of Heterogeneity 152\u003c\/p\u003e \u003cp\u003e5.7 Diagnostic Checks of the First Stage Prior 155\u003c\/p\u003e \u003cp\u003e5.8 Findings and Influence on Marketing Practice 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Model Choice and Decision Theory 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Model Selection 160\u003c\/p\u003e \u003cp\u003e6.2 Bayes Factors in the Conjugate Setting 162\u003c\/p\u003e \u003cp\u003e6.3 Asymptotic Methods for Computing Bayes Factors 163\u003c\/p\u003e \u003cp\u003e6.4 Computing Bayes Factors Using Importance Sampling 165\u003c\/p\u003e \u003cp\u003e6.5 Bayes Factors Using MCMC Draws from the Posterior 166\u003c\/p\u003e \u003cp\u003e6.6 Bridge Sampling Methods 169\u003c\/p\u003e \u003cp\u003e6.7 Posterior Model Probabilities with Unidentified Parameters 170\u003c\/p\u003e \u003cp\u003e6.8 Chib’s Method 171\u003c\/p\u003e \u003cp\u003e6.9 An Example of Bayes Factor Computation: Diagonal MNP models 172\u003c\/p\u003e \u003cp\u003e6.10 Marketing Decisions and Bayesian Decision Theory 178\u003c\/p\u003e \u003cp\u003e6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Simultaneity 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 A Bayesian Approach to Instrumental Variables 186\u003c\/p\u003e \u003cp\u003e7.2 Structural Models and Endogeneity\/Simultaneity 195\u003c\/p\u003e \u003cp\u003e7.3 Non-Random Marketing Mix Variables 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Bayesian Perspective on Machine Learning 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 207\u003c\/p\u003e \u003cp\u003e8.2 Regularization 209\u003c\/p\u003e \u003cp\u003e8.3 Bagging 212\u003c\/p\u003e \u003cp\u003e8.4 Boosting 216\u003c\/p\u003e \u003cp\u003e8.5 Deep Learning 217\u003c\/p\u003e \u003cp\u003e8.6 Applications 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Bayesian Analysis for Text Data 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 227\u003c\/p\u003e \u003cp\u003e9.2 Consumer Demand 228\u003c\/p\u003e \u003cp\u003e9.3 Integrated Models 236\u003c\/p\u003e \u003cp\u003e9.4 Discussion 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Case Study 1: Analysis of Choice-Based Conjoint Data Using A Hierarchical Logit Model 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Choice-Based Conjoint 255\u003c\/p\u003e \u003cp\u003e10.2 A Random Coefficient Logit 258\u003c\/p\u003e \u003cp\u003e10.3 Sign Constraints and Priors 258\u003c\/p\u003e \u003cp\u003e10.4 The Camera Data 262\u003c\/p\u003e \u003cp\u003e10.5 Running the Model 266\u003c\/p\u003e \u003cp\u003e10.6 Describing the Draws of Respondent Partworths 268\u003c\/p\u003e \u003cp\u003e10.7 Predictive Posteriors 270\u003c\/p\u003e \u003cp\u003e10.8 Comparison of Stan and Sawtooth Software to bayesm Routines 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Case Study 2: WTP and Equilibrium Analysis with Conjoint Demand 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Demand for Product Features 278\u003c\/p\u003e \u003cp\u003e11.2 Conjoint Surveys and Demand Estimation 282\u003c\/p\u003e \u003cp\u003e11.3 WTP Properly Defined 287\u003c\/p\u003e \u003cp\u003e11.4 Nash Equilibrium Prices -- Computation and Assumptions 294\u003c\/p\u003e \u003cp\u003e11.5 Camera Example 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Case Study 3: Scale Usage Heterogeneity 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Background 307\u003c\/p\u003e \u003cp\u003e12.2 Model 310\u003c\/p\u003e \u003cp\u003e12.3 Priors and MCMC Algorithm 314\u003c\/p\u003e \u003cp\u003e12.4 Data 316\u003c\/p\u003e \u003cp\u003e12.5 Discussion 320\u003c\/p\u003e \u003cp\u003e12.6 R Implementation 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Case Study 4: Volumetric Conjoint 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 323\u003c\/p\u003e \u003cp\u003e13.2 Model Development 324\u003c\/p\u003e \u003cp\u003e13.3 Estimation 329\u003c\/p\u003e \u003cp\u003e13.4 Empirical Analysis 331\u003c\/p\u003e \u003cp\u003e13.5 Discussion 339\u003c\/p\u003e \u003cp\u003e13.6 Using the Code 342\u003c\/p\u003e \u003cp\u003e13.7 Concluding Remarks 342\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Case Study 5: Approximate Bayes and Personalized Pricing 343\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Heterogeneity and Heterogeneous Treatment Effects 343\u003c\/p\u003e \u003cp\u003e14.2 The Framework 344\u003c\/p\u003e \u003cp\u003e14.3 Context and Data 345\u003c\/p\u003e \u003cp\u003e14.4 Does the Bayesian Bootstrap Work? 346\u003c\/p\u003e \u003cp\u003e14.5 A Bayesian Bootstrap Procedure for the HTE Logit 349\u003c\/p\u003e \u003cp\u003e14.6 Personalized Pricing 351\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A An Introduction to R and bayesm 357\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Setting up the R Environment and bayesm 357\u003c\/p\u003e \u003cp\u003eA.2 The R Language 360\u003c\/p\u003e \u003cp\u003eA.3 Using bayesm 379\u003c\/p\u003e \u003cp\u003eA.4 Obtaining Help on bayesm 379\u003c\/p\u003e \u003cp\u003eA.5 Tips on Using MCMC Methods 381\u003c\/p\u003e \u003cp\u003eA.6 Extending and Adapting Our Code 381\u003c\/p\u003e \u003cp\u003eReferences 383\u003c\/p\u003e \u003cp\u003eIndex 389\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePeter Rossi\u003c\/b\u003e is James Collins Distinguished University Professor of Marketing, Economics, and Statistics at the Anderson School of Management, UCLA, USA. He is the author of the popular R package, bayesm, and he has researched and published extensively on pricing and promotion, target marketing, and other related subjects. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eGreg Allenby\u003c\/b\u003e is Helen C. Kurtz Professor of Marketing as well as Professor of Statistics at the Fisher College of Business, Ohio State University, USA. He is a Fellow of the Informs Society for Marketing Science and the American Statistical Association, and he has published widely on the development and application of quantitative methods in marketing. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSanjog Misra\u003c\/b\u003e is Charles H. Kellstadt Professor of Marketing in the Booth School of Business, University of Chicago, USA. He has served as the co-editor of numerous high-impact journals, including Quantiative Marketing and Economics, and his research focuses on the use of machine learning and deep learning to study consumer and firm decisions.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eFine-tune your marketing research with this cutting-edge statistical toolkit\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBayesian Statistics and Marketing \u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eEconomists 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. \u003c\/p\u003e\u003cp\u003eReaders of the second edition of \u003ci\u003eBayesian Statistics and Marketing \u003c\/i\u003ewill also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDiscussion of Bayesian methods in text analysis and Machine Learning \u003c\/li\u003e\n\u003cli\u003eUpdates throughout reflecting the latest research and applications \u003c\/li\u003e\n\u003cli\u003eDiscussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here \u003c\/li\u003e\n\u003cli\u003eExtensive case studies throughout to link theory and practice\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBayesian Statistics and Marketing\u003c\/i\u003e is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988794458341,"sku":"NP9781394219117","price":110.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394219117.jpg?v=1761781614","url":"https:\/\/k12savings.com\/products\/bayesian-statistics-and-marketing-isbn-9781394219117","provider":"K12savings","version":"1.0","type":"link"}