Author: Robert Nathanael Donnelly
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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.
Author: Sandeep R. Chandukala
Publisher: Now Publishers Inc
ISBN: 1601981643
Category : Business & Economics
Languages : en
Pages : 100
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Book Description
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.
Author: Madhav Kumar (Scientist in business management)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This dissertation contains three essays on designing scalable models and policy learning methods for online marketplaces. The underlying theme across all chapters is the development of data-driven practical solutions that help improve business operations and customer experiences in e-commerce. The first chapter offers a new perspective on creating promotional bundles in cross-category retail. A scalable approach is designed that efficiently leverages historical purchases and consideration sets to learn heuristics for complementarity and substitutability using machine learning-based embeddings. Subsequently, thousands of candidate bundles are created based on these heuristics and their effectiveness is tested using a field experiment. Offline policy learning is applied to the experimental data to optimize the retailer's bundle design policy. The optimized policy is robust across product categories, generalizes well to the retailer's entire assortment, and provides an expected improvement of 35% in revenue over the baseline policy. The second chapter investigates the impact of algorithmic pricing on consumer behavior. The adoption of algorithmic pricing by an online retailer led to considerably higher price volatility. Analysis of detailed clickstream data, complemented with lab experiments, suggests that consumers become more price sensitive when exposed to frequently changing prices caused by algorithms. Furthermore, it shows that a key mechanism driving this behavior is price salience. This finding is economically consequential because even if implementing algorithmic pricing is profitable, it triggers unintended side effects that modify consumer behavior in ways that undermine those gains. The third chapter augments choice models and recommendation systems with consumer consideration sets. Recommendations systems are commonly used in online marketplaces to suggest relevant items (products in case of e-commerce, content in case of social media, and music/movies in case of entertainment platforms) to users. In the case of online retail, these systems typically use historical purchases to learn consumer preferences and then predict what consumers are likely to buy next. The suggested method enhances the learning of consumer preferences by flexibly incorporating consumers' historical consideration sets along with purchases with a sequential deep learning model. The search augmented recommendation system better captures consumers' latent preferences, more accurately predicts future actions, and substantially outperforms strong baselines. Finally, we show that these gains are distributed across the entire spectrum of consumers and not concentrated among a small subset of high usage consumers.
Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399
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Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Author: Jeffrey A. Dubin
Publisher: Springer Science & Business Media
ISBN: 1461556651
Category : Business & Economics
Languages : en
Pages : 306
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Book Description
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.
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Business & Economics
Languages : en
Pages : 271
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Book Description
What is Consumer Choice The theory of consumer choice is the branch of microeconomics that relates preferences to consumption expenditures and to consumer demand curves. It analyzes how consumers maximize the desirability of their consumption, by maximizing utility subject to a consumer budget constraint.Factors influencing consumers' evaluation of the utility of goods include: income level, cultural factors, product information and physio-psychological factors. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Consumer choice Chapter 2: Utility Chapter 3: Indifference curve Chapter 4: Budget constraint Chapter 5: Substitute good Chapter 6: Marginal rate of substitution Chapter 7: Income-consumption curve Chapter 8: Substitution effect Chapter 9: Law of demand Chapter 10: Utility maximization problem Chapter 11: Marshallian demand function Chapter 12: Revealed preference Chapter 13: Hicksian demand function Chapter 14: Corner solution Chapter 15: Relative price Chapter 16: Local nonsatiation Chapter 17: Quasilinear utility Chapter 18: Homothetic preferences Chapter 19: Preference (economics) Chapter 20: Robinson Crusoe economy Chapter 21: Linear utility (II) Answering the public top questions about consumer choice. (III) Real world examples for the usage of consumer choice in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Consumer Choice.
Author: James Michael Brand
Publisher:
ISBN:
Category :
Languages : en
Pages : 210
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Book Description
This dissertation addresses a number of questions which relate to a growing body of evidence for the rise of markups among large firms, and concentration in many industries, in the United States over time. Although existing evidence suggests that markups have been on the rise, there are a number of competing explanations for these trends which have differing policy implications. In this dissertation I argue that, in the retail food industry, the most likely cause of rising markups over time is the growing prevalence of “niche” goods which more closely match consumers’ idiosyncratic needs, and estimate a number of structural models of consumer demand in order to study the evidence for this hypothesis. The first chapter discusses the existing evidence that markups have been on the rise in the United States and introduces the theoretical framework and empirical evidence motivating the study of markups in the retail food industry. In this chapter I show that the number of goods carried by retail food stores in my data has grown significantly between 2006 and 2017, and that the characteristics of the offered goods have changed over time. Using a simple theoretical model, I argue that firms’ incentives to offer “niche” goods, which I define herein, grow as they become able to stock a larger portfolio of goods. I also introduce the data set which is used in all three chapters of this dissertation and briefly discuss the way in which I select the categories which are the focus of my empirical work in Chapters 2 and 3. The second chapter introduces three empirical models of consumer choice in nine retail food categories. The first is a traditional mixed logit model in the spirit of Berry, Levinsohn and Pakes (1995), which has been applied in many studies in industrial organization. The second is an approximation of this model, developed by Salanie and Wolak (2019), which allows consumer preferences to differ flexibly in every three-digit ZIP code in my data. The third model is a constant elasticity model of demand, which makes different assumptions than do mixed logit models and serves in part as a robustness check against their potentially strong assumptions. I estimate each of these models separately in nine large categories of products in 2006 and 2017 and demonstrate that each model implies that consumers have become significantly less price sensitive over time. A simple pricing rule demonstrates that the firms in my data may have been able to sustain larger markups over time solely due to the observed changes in the price sensitivity of consumers, absent any changes in firm pricing behavior. In the third and final chapter, I estimate two additional models of demand in order to determine the structural reasons that consumers have become less price sensitive. First, I demonstrate that an assumption restricting changes in unobserved product quality over time allows us to distinguish between the effects of rising horizontal differentiation and changes in the direct disutility of price due to, for example, changing consumer demographics and wealth. In a second model, I introduce a new scaling parameter which allows me to measure differences in consumers’ price sensitivity for newer and older goods (as measured by how long a good has been sold in a store in my sample) separately. Together, these models provide evidence that horizontal differentiation has increased significantly over time and that consumers are particularly insensitive to changes in the prices of newer goods. I take these findings as evidence that niche products do play a significant role in explaining the results of Chapter 2, as predicted by the model introduced in Chapter 1
Author: Jeongwen Chiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 158
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Author: Patrick Bajari
Publisher:
ISBN:
Category : Consumers' preferences
Languages : en
Pages : 80
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Book Description
We study the identification and estimation of preferences in hedonic discrete choice models of demand for differentiated products. In the hedonic discrete choice model, products are represented as a finite dimensional bundle of characteristics, and consumers maximize utility subject to a budget constraint. Our hedonic model also incorporates product characteristics that are observed by consumers but not by the economist. We demonstrate that, unlike the case where all product characteristics are observed, it is not in general possible to uniquely recover consumer preferences from data on a consumer's choices. However, we provide several sets of assumptions under which preferences can be recovered uniquely, that we think may be satisfied in many applications. Our identification and estimation strategy is a two stage approach in the spirit of Rosen (1974). In the first stage, we show under some weak conditions that price data can be used to nonparametrically recover the unobserved product characteristics and the hedonic pricing function. In the second stage, we show under some weak conditions that if the product space is continuous and the functional form of utility is known, then there exists an inversion between a consumer's choices and her preference parameters. If the product space is discrete, we propose a Gibbs sampling algorithm to simulate the population distribution of consumers' taste coefficients.
Author: Pradeep K. Chintagunta
Publisher:
ISBN:
Category : Consumer behavior
Languages : en
Pages : 40
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Book Description
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.
