Bayesian Statistics and Marketing Rossi, Peter E; Allenby, Greg M; McCulloch, Rob
2012, 2012., 2005, 2012-05-14, 20051209, 20050101, c2005
eBook
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 Marketingdescribes 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 Marketingprovides 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.
Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are ...different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from
www.expedia.com
and
www.we8there.com
. Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis.
Data, as supplemental material, are available at
https://doi.org/10.1287/mksc.2016.0993
.
Consumers often try to achieve multiple goals when purchasing products and services, where choices are sought that maximize utility and other objectives such as to minimize regret. If the performance ...of choice outcomes are associated with high level of uncertainty at the time of purchase, consumers worry that the alternative of interest may turn out to be not optimal, an outcome they want to avoid when making a purchase. The authors propose a new regret function and explore its properties. Then, they propose a generalized framework of multiple goal pursuit and apply it to a utility maximization and regret minimization problem. Pareto-optimal sets are an outcome of multiple goal optimization problems where there are multiple alternatives that are nondominated. The proposed framework enables the authors to generalize a dual-goal problem as a constrained optimization problem where either utility is maximized subject to the constraint on regret or regret is minimized subject to the utility constraint. The proposed model fits the data better, provides improved predictions, and offers a tractable solution to a problem of utility maximization and regret minimization.
Consumers encounter costs and inconvenience in the purchase and consumption of goods. Time, effort, training, and expertise, also referred to as access costs, are examples of factors that constrain ...the ability of consumers to derive utility from offerings beyond the price of a good. Economies of scope exist when household production of these factors share commonalities among choice alternatives, making it more likely that some products would be purchased and consumed in conjunction with others. For example, consumers planning out-of-town travel might consider visiting multiple tourist attractions during their trip, and course offerings might appeal more to students who are already in the mindset of taking courses. We develop a model with economies of scope that captures a leveraging effect where the purchase of one good makes the purchase of other goods more likely. Our model offers an alternative explanation of complementary purchases that are driven by cost reductions rather than utility enhancements. The model is applied to four conjoint datasets where we find that economies of scope and access costs play an important role in consumer purchases and have high economic value relative to the role played by price.
This paper was accepted by Eric Anderson, marketing.
Supplemental Material:
The data files and online appendices are available at
https://doi.org/10.1287/mnsc.2023.4841
.
Abstract
Mediation analysis is used to study the relationship between stimulus and response in the presence of intermediate, generative variables. The traditional approach to the analysis utilizes ...the results of an aggregate regression model, which assumes that all respondents go through the same data-generating mechanism. We introduce a new approach that is able to uncover the heterogeneity in mediating mechanisms and provides more informative insights from mediation studies. The proposed approach provides individual-specific probabilities to mediate as well as a new measure of the degree of mediation as the prevalence of mediation in the sample. Covariates in the proposed model help describe the variation in the probability to mediate among respondents. The empirical examination of published studies demonstrates the presence of heterogeneity in mediating processes and supports the need for this new approach. We present evidence that the results of our more flexible heterogeneous mediation analysis do not necessarily agree with the traditional aggregate measures. We find that the conclusions from the aggregate analysis are neither sufficient nor necessary to claim mediation in the presence of heterogeneity. A web-based application allowing researchers to analyze the data with the proposed model in a user-friendly environment is developed.
User-generated content in the form of customer reviews, blogs, and tweets is an emerging and rich source of data for marketers. Topic models have been successfully applied to such data, demonstrating ...that empirical text analysis benefits greatly from a latent variable approach that summarizes high-level interactions among words. We propose a new topic model that allows for serial dependency of topics in text. That is, topics may carry over from word to word in a document, violating the bag-of-words assumption in traditional topic models. In the proposed model, topic carryover is informed by sentence conjunctions and punctuation. Typically, such observed information is eliminated prior to analyzing text data (i.e., preprocessing) because words such as "and" and "but" do not differentiate topics. We find that these elements of grammar contain information relevant to topic changes. We examine the performance of our models using multiple data sets and establish boundary conditions for when our model leads to improved inference about customer evaluations. Implications and opportunities for future research are discussed.
Demand Models With Random Partitions Smith, Adam N.; Allenby, Greg M.
Journal of the American Statistical Association,
01/2020, Letnik:
115, Številka:
529
Journal Article
Recenzirano
Odprti dostop
Many economic models of consumer demand require researchers to partition sets of products or attributes prior to the analysis. These models are common in applied problems when the product space is ...large or spans multiple categories. While the partition is traditionally fixed a priori, we let the partition be a model parameter and propose a Bayesian method for inference. The challenge is that demand systems are commonly multivariate models that are not conditionally conjugate with respect to partition indices, precluding the use of Gibbs sampling. We solve this problem by constructing a new location-scale partition distribution that can generate random-walk Metropolis-Hastings proposals and also serve as a prior. Our method is illustrated in the context of a store-level category demand model, where we find that allowing for partition uncertainty is important for preserving model flexibility, improving demand forecasts, and learning about the structure of demand.
Supplementary materials
for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Bayesian Statistics and Marketing Rossi, Peter E; Allenby, Greg M
Marketing science,
08/2003, Letnik:
22, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Bayesian methods have become widespread in marketing literature. We review the essence of the Bayesian approach and explain why it is particularly useful for marketing problems. While the appeal of ...the Bayesian approach has long been noted by researchers, recent developments in computational methods and expanded availability of detailed marketplace data has fueled the growth in application of Bayesian methods in marketing. We emphasize the modularity and flexibility of modern Bayesian approaches. The usefulness of Bayesian methods in situations in which there is limited information about a large number of units or where the information comes from different sources is noted. We include an extensive discussion of open issues and directions for future research.
The identification of optimal product and package designs is challenged when attributes and their levels interact. Firms recognize this by testing trial products and designs prior to launch, during ...which the effects of interactions are revealed. A difficulty in conducting analysis for product design is dealing with the high dimensionality of the design space and the selection of promising product configurations for testing. We propose an experimental criterion for efficiently testing product profiles with high demand potential in sequential experiments. The criterion is based on the expected improvement in market share of a design beyond the current best alternative. We also incorporate a stochastic search variable selection method to selectively estimate relevant interactions among the attributes. A validation experiment confirms that our proposed method leads to improved design concepts in a high-dimensional space compared with alternative methods.
This paper was accepted by Eric Anderson, marketing.
Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made ...available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers.
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