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Papers by Eric Bradlow

Research paper thumbnail of Bayesian Imputation for Anonymous Visits in CRM Data

Social Science Research Network, 2015

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Research paper thumbnail of Fusion Modeling

Springer eBooks, Dec 3, 2021

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Research paper thumbnail of An Integrated Model for Dynamic Brand Equity

Social Science Research Network, 2016

This paper presents a unified statistical model designed to measure brand equity as it changes ov... more This paper presents a unified statistical model designed to measure brand equity as it changes over time; and gauge the impact of increased brand equity on consumer's product choices. Our model extends traditional models of brand equity which posit that strong brands are simply "more preferred" after controlling for the marketing mix (i.e. an intercept) to a more general model that allows for (1) brand equity to evolve (modeled as a Bayesian DLM model); (2) perceived product attributes to vary with brand strength ― an "X-perception effect"; and (3) perceived coefficients for product attributes to be a function of equity ― a "beta effect". This extended model provides firms and researchers with a more comprehensive view of brand equity and how it manifests itself in consumers' product choices. We apply the proposed model to a panel dataset on purchases in the pretzel category, demonstrating that brand equity had a measurable effect on price sensitivity and product attribute perceptions. Using the model we optimize the timing of marketing and derive optimal weekly prices. Comparing those results to simpler benchmark models of brand equity, we find that managers can potentially lose profit by using the reduced-form model due to mispricing.

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Research paper thumbnail of Insead

Consumer behavior at the point of purchase is influenced by out-of-store memory-based factors (e.... more Consumer behavior at the point of purchase is influenced by out-of-store memory-based factors (e.g., brand awareness and brand image) and by in-store attention-based factors (e.g., package design, shelf position, and number of facings). In today’s cluttered retail environments, creating memory-based consumer pull is not enough; marketers must also create “visual lift ” for their brands—that is, incremental consideration caused by in-store visual attention. The problem is that it is currently impossible to precisely measure visual lift. Surveys can easily be conducted to compare pre-store intentions and post-store choices but they do not measure attention. They cannot therefore tell whether ineffective in-store marketing was due to a poor attention-getting ability—“unseen and hence unsold”—or to a poor visual lift—“seen yet still unsold”. Eye-tracking studies have shown that eye-movements to brands displayed on a supermarket shelf are valid measures of visual attention and are genera...

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Research paper thumbnail of ©2010 INFORMS Structural Estimation of the Effect of Out-of-Stocks

We develop a structural demand model that endogenously captures the effect of out-of-stocks on cu... more We develop a structural demand model that endogenously captures the effect of out-of-stocks on customerchoice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution pat-terns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate th...

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Research paper thumbnail of 1 The Role of Big Data and Predictive Analytics in Retailing Abstract

The paper examines the opportunities in and possibilities arising from Big Data in retailing, par... more The paper examines the opportunities in and possibilities arising from Big Data in retailing, particularly along five major data dimensions data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of Big Data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all...

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Research paper thumbnail of Binge Consumption of Online Content: A Boundedly Rational Model of Goal Progress and Knowledge Accumulation

Binge consumption of online content has emerged as a trending phenomenon among customers of onlin... more Binge consumption of online content has emerged as a trending phenomenon among customers of online streaming services, with various content providers spanning the spectrum from entertainment to education. Here, we focus on binging within an online education setting, using clickstream data from Coursera in which we observe individual-level lecture and quiz consumption patterns across multiple courses. We extend the literature by distinguishing between “temporal binging,” where individuals consume multiple pieces of content in a single sitting, and “content binging,” where individuals consume content from the same course in succession. We build a model that captures individual decisions about which course to consume, whether the content is a lecture or a quiz, and when to take breaks of different lengths. The parameters of our model can be mapped to specific theories in consumer psychology, which allows us to test for the mechanisms that drive binge consumption. There are three key fe...

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Research paper thumbnail of Refocusing loyalty programs in the era of big data: a societal lens paradigm

Marketing Letters, 2020

Big data and technological change have enabled loyalty programs to become more prevalent and comp... more Big data and technological change have enabled loyalty programs to become more prevalent and complex. How these developments influence society has been overlooked, both in academic research and in practice. We argue why this issue is important and propose a framework to refocus loyalty programs in the era of big data through a societal lens. We focus on three aspects of the societal lens—inequality, privacy, and sustainability. We discuss how loyalty programs in the big data era impact each of these societal factors, and then illustrate how, by adopting this societal lens paradigm, researchers and practitioners can generate insights and ideas that address the challenges and opportunities that arise from the interaction between loyalty programs and society. Our goal is to broaden the perspectives of researchers and managers so they can enhance loyalty programs to address evolving societal needs.

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Research paper thumbnail of An Integrated Model for Dynamic Brand Equity

SSRN Electronic Journal, 2016

This paper presents a unified statistical model designed to measure brand equity as it changes ov... more This paper presents a unified statistical model designed to measure brand equity as it changes over time; and gauge the impact of increased brand equity on consumer's product choices. Our model extends traditional models of brand equity which posit that strong brands are simply "more preferred" after controlling for the marketing mix (i.e. an intercept) to a more general model that allows for (1) brand equity to evolve (modeled as a Bayesian DLM model); (2) perceived product attributes to vary with brand strength ― an "X-perception effect"; and (3) perceived coefficients for product attributes to be a function of equity ― a "beta effect". This extended model provides firms and researchers with a more comprehensive view of brand equity and how it manifests itself in consumers' product choices. We apply the proposed model to a panel dataset on purchases in the pretzel category, demonstrating that brand equity had a measurable effect on price sensitivity and product attribute perceptions. Using the model we optimize the timing of marketing and derive optimal weekly prices. Comparing those results to simpler benchmark models of brand equity, we find that managers can potentially lose profit by using the reduced-form model due to mispricing.

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Research paper thumbnail of Measuring Multi-Channel Advertising Response

SSRN Electronic Journal, 2016

Advances in data collection have made it increasingly easy to collect information on advertising ... more Advances in data collection have made it increasingly easy to collect information on advertising exposures.However, translating this seemingly rich data into measures of advertising response has proven difficult, largely due to concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We show how this problem can be addressed by studying a setting where a firm randomly held out customers from each campaign, creating a sequence of randomized field experiments that mitigates (many) potential endogeneity problems. Exploratory analysis of individual hold-out experiments shows positive effects for both email and catalog, however the estimated effect for any individual campaign is imprecise, due to the small size of the holdout.To pool data across campaigns we develop a hierarchical Bayesian model for advertising response, which allows us to account for individual differences in purchase propensity and marketing response. Building on the traditional ad-stock framework, we are able to estimate separate decay rates for each advertising medium, allowing us to predict channel-specific short- and long-term effects of advertising and use these predictions to inform marketing strategy. We find that catalogs have substantially longer-lasting impact on customer purchase than emails. We show how the model can be used to score and target individual customers based on their advertising responsiveness, and find that targeting the most responsive customers increases the predicted returns on advertising by about 70% versus traditional RFM-based targeting.

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Research paper thumbnail of Reflections on the Replication Corner: In Praise of Conceptual Replications

SSRN Electronic Journal, 2015

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Research paper thumbnail of A Cross-Cohort Changepoint Model for Customer-Base Analysis

SSRN Electronic Journal, 2012

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Research paper thumbnail of Measuring the Value of Point-of-Purchase Marketing with Commercial Eye-Tracking Data

SSRN Electronic Journal, 2006

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Research paper thumbnail of The interrelationships between brand and channel choice

Marketing Letters, 2014

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Research paper thumbnail of Latent Redemption Thresholds in Linear Loyalty Programs

SSRN Electronic Journal, 2012

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Research paper thumbnail of Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

Marketing Science, 2009

Many data sets, from different and seemingly unrelated marketing domains, all involve paths—recor... more Many data sets, from different and seemingly unrelated marketing domains, all involve paths—records of consumers' movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers' motivations and behaviors, path data sets will become more common and will play a more central role in marketing research. To guide future research in this area, we review the previous literature, propose a formal definition of a path (in a marketing context), and derive a unifying framework that allows us to classify different kinds of paths. We identify and discuss two primary dimensions (characteristics of the spatial configuration and the agent) as well as six underlying subdimensions. Based on this framework, we cover a range of important operational issues tha...

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Research paper thumbnail of Empirical models of manufacturer-retailer interaction: A review and agenda for future research

Marketing Letters, 2010

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Research paper thumbnail of Modeling the “Pseudodeductible” in Insurance Claims Decisions

Management Science, 2006

In many different managerial contexts, consumers “leave money on the table” by, for example, thei... more In many different managerial contexts, consumers “leave money on the table” by, for example, their failure to claim rebates, use available coupons, and so on. This project focuses on a related problem faced by homeowners who may be reluctant to file insurance claims despite the fact their losses are covered. We model this consumer decision by introducing the concept of the “pseudodeductible,” a latent threshold above the policy deductible that governs the homeowner’s claim behavior. In addition, we show how the observed number of claims can be modeled as the output of three stochastic processes that are separately, and in conjunction, managerially relevant: the rate at which losses occur, the size of each loss, and the choice of the individual to file or not file a claim. By allowing for the possibility of pseudodeductibles, one can sort out (and make accurate inferences about) these three processes. We test this model using a proprietary data set provided by State Farm, the largest...

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Research paper thumbnail of Improving the crystal ball: harnessing consumer input to create retail prediction markets

Journal of Research in Interactive Marketing, 2010

PurposeRetail buyers' decisions result in billions of dollars of merchandise being purchased ... more PurposeRetail buyers' decisions result in billions of dollars of merchandise being purchased and offered for sale by retailers around the world. At present, retail buyers do not appear to be adequately harnessing consumer input to improve their forecasts. The purpose of this paper is to address this issue by introducing a new approach involving both retail buyers' consensus forecasts and those from a sample of “ordinary” consumers.Design/methodology/approachThe authors introduce a new approach to online forecasting that involves both retail buyers' consensus forecasts and those from a sample of “ordinary” consumers.FindingsThe results suggest an opportunity to create what are termed retail prediction markets that offer significant potential to improve the accuracy of buyers' forecasts.Originality/valueThe authors go beyond crowd sourcing technology and show how retail prediction markets may offer significant potential to improve the accuracy of retail buyers' for...

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Research paper thumbnail of A Learning-Based Model for Imputing Missing Levels in Partial Conjoint Profiles

Journal of Marketing Research, 2004

Respondents in a conjoint experiment sometimes are presented with successive partial product prof... more Respondents in a conjoint experiment sometimes are presented with successive partial product profiles. First, the authors model how respondents infer missing levels of product attributes in a partial conjoint profile by developing a learning-based imputation model that nests several extant models. The advantage of this approach over previous research is that it infers missing levels of an attribute not only from prior levels of the same attribute but also from prior levels of other attributes, especially ones that match the attribute levels of the current product profile. Second, the authors provide an empirical demonstration of their approach and test whether learning in conjoint studies occurs; to what extent; and in what manner it affects responses, partworths, and the relative importance of attributes. They show that the relative importance of attribute partworths can shift when subjects evaluate partial profiles, which suggests that consumers may construct rather than retrieve ...

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Research paper thumbnail of Bayesian Imputation for Anonymous Visits in CRM Data

Social Science Research Network, 2015

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Research paper thumbnail of Fusion Modeling

Springer eBooks, Dec 3, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Integrated Model for Dynamic Brand Equity

Social Science Research Network, 2016

This paper presents a unified statistical model designed to measure brand equity as it changes ov... more This paper presents a unified statistical model designed to measure brand equity as it changes over time; and gauge the impact of increased brand equity on consumer's product choices. Our model extends traditional models of brand equity which posit that strong brands are simply "more preferred" after controlling for the marketing mix (i.e. an intercept) to a more general model that allows for (1) brand equity to evolve (modeled as a Bayesian DLM model); (2) perceived product attributes to vary with brand strength ― an "X-perception effect"; and (3) perceived coefficients for product attributes to be a function of equity ― a "beta effect". This extended model provides firms and researchers with a more comprehensive view of brand equity and how it manifests itself in consumers' product choices. We apply the proposed model to a panel dataset on purchases in the pretzel category, demonstrating that brand equity had a measurable effect on price sensitivity and product attribute perceptions. Using the model we optimize the timing of marketing and derive optimal weekly prices. Comparing those results to simpler benchmark models of brand equity, we find that managers can potentially lose profit by using the reduced-form model due to mispricing.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Insead

Consumer behavior at the point of purchase is influenced by out-of-store memory-based factors (e.... more Consumer behavior at the point of purchase is influenced by out-of-store memory-based factors (e.g., brand awareness and brand image) and by in-store attention-based factors (e.g., package design, shelf position, and number of facings). In today’s cluttered retail environments, creating memory-based consumer pull is not enough; marketers must also create “visual lift ” for their brands—that is, incremental consideration caused by in-store visual attention. The problem is that it is currently impossible to precisely measure visual lift. Surveys can easily be conducted to compare pre-store intentions and post-store choices but they do not measure attention. They cannot therefore tell whether ineffective in-store marketing was due to a poor attention-getting ability—“unseen and hence unsold”—or to a poor visual lift—“seen yet still unsold”. Eye-tracking studies have shown that eye-movements to brands displayed on a supermarket shelf are valid measures of visual attention and are genera...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of ©2010 INFORMS Structural Estimation of the Effect of Out-of-Stocks

We develop a structural demand model that endogenously captures the effect of out-of-stocks on cu... more We develop a structural demand model that endogenously captures the effect of out-of-stocks on customerchoice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution pat-terns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate th...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of 1 The Role of Big Data and Predictive Analytics in Retailing Abstract

The paper examines the opportunities in and possibilities arising from Big Data in retailing, par... more The paper examines the opportunities in and possibilities arising from Big Data in retailing, particularly along five major data dimensions data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of Big Data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Binge Consumption of Online Content: A Boundedly Rational Model of Goal Progress and Knowledge Accumulation

Binge consumption of online content has emerged as a trending phenomenon among customers of onlin... more Binge consumption of online content has emerged as a trending phenomenon among customers of online streaming services, with various content providers spanning the spectrum from entertainment to education. Here, we focus on binging within an online education setting, using clickstream data from Coursera in which we observe individual-level lecture and quiz consumption patterns across multiple courses. We extend the literature by distinguishing between “temporal binging,” where individuals consume multiple pieces of content in a single sitting, and “content binging,” where individuals consume content from the same course in succession. We build a model that captures individual decisions about which course to consume, whether the content is a lecture or a quiz, and when to take breaks of different lengths. The parameters of our model can be mapped to specific theories in consumer psychology, which allows us to test for the mechanisms that drive binge consumption. There are three key fe...

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Research paper thumbnail of Refocusing loyalty programs in the era of big data: a societal lens paradigm

Marketing Letters, 2020

Big data and technological change have enabled loyalty programs to become more prevalent and comp... more Big data and technological change have enabled loyalty programs to become more prevalent and complex. How these developments influence society has been overlooked, both in academic research and in practice. We argue why this issue is important and propose a framework to refocus loyalty programs in the era of big data through a societal lens. We focus on three aspects of the societal lens—inequality, privacy, and sustainability. We discuss how loyalty programs in the big data era impact each of these societal factors, and then illustrate how, by adopting this societal lens paradigm, researchers and practitioners can generate insights and ideas that address the challenges and opportunities that arise from the interaction between loyalty programs and society. Our goal is to broaden the perspectives of researchers and managers so they can enhance loyalty programs to address evolving societal needs.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Integrated Model for Dynamic Brand Equity

SSRN Electronic Journal, 2016

This paper presents a unified statistical model designed to measure brand equity as it changes ov... more This paper presents a unified statistical model designed to measure brand equity as it changes over time; and gauge the impact of increased brand equity on consumer's product choices. Our model extends traditional models of brand equity which posit that strong brands are simply "more preferred" after controlling for the marketing mix (i.e. an intercept) to a more general model that allows for (1) brand equity to evolve (modeled as a Bayesian DLM model); (2) perceived product attributes to vary with brand strength ― an "X-perception effect"; and (3) perceived coefficients for product attributes to be a function of equity ― a "beta effect". This extended model provides firms and researchers with a more comprehensive view of brand equity and how it manifests itself in consumers' product choices. We apply the proposed model to a panel dataset on purchases in the pretzel category, demonstrating that brand equity had a measurable effect on price sensitivity and product attribute perceptions. Using the model we optimize the timing of marketing and derive optimal weekly prices. Comparing those results to simpler benchmark models of brand equity, we find that managers can potentially lose profit by using the reduced-form model due to mispricing.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Measuring Multi-Channel Advertising Response

SSRN Electronic Journal, 2016

Advances in data collection have made it increasingly easy to collect information on advertising ... more Advances in data collection have made it increasingly easy to collect information on advertising exposures.However, translating this seemingly rich data into measures of advertising response has proven difficult, largely due to concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We show how this problem can be addressed by studying a setting where a firm randomly held out customers from each campaign, creating a sequence of randomized field experiments that mitigates (many) potential endogeneity problems. Exploratory analysis of individual hold-out experiments shows positive effects for both email and catalog, however the estimated effect for any individual campaign is imprecise, due to the small size of the holdout.To pool data across campaigns we develop a hierarchical Bayesian model for advertising response, which allows us to account for individual differences in purchase propensity and marketing response. Building on the traditional ad-stock framework, we are able to estimate separate decay rates for each advertising medium, allowing us to predict channel-specific short- and long-term effects of advertising and use these predictions to inform marketing strategy. We find that catalogs have substantially longer-lasting impact on customer purchase than emails. We show how the model can be used to score and target individual customers based on their advertising responsiveness, and find that targeting the most responsive customers increases the predicted returns on advertising by about 70% versus traditional RFM-based targeting.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Reflections on the Replication Corner: In Praise of Conceptual Replications

SSRN Electronic Journal, 2015

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Cross-Cohort Changepoint Model for Customer-Base Analysis

SSRN Electronic Journal, 2012

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Measuring the Value of Point-of-Purchase Marketing with Commercial Eye-Tracking Data

SSRN Electronic Journal, 2006

Bookmarks Related papers MentionsView impact

Research paper thumbnail of The interrelationships between brand and channel choice

Marketing Letters, 2014

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Latent Redemption Thresholds in Linear Loyalty Programs

SSRN Electronic Journal, 2012

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

Marketing Science, 2009

Many data sets, from different and seemingly unrelated marketing domains, all involve paths—recor... more Many data sets, from different and seemingly unrelated marketing domains, all involve paths—records of consumers' movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers' motivations and behaviors, path data sets will become more common and will play a more central role in marketing research. To guide future research in this area, we review the previous literature, propose a formal definition of a path (in a marketing context), and derive a unifying framework that allows us to classify different kinds of paths. We identify and discuss two primary dimensions (characteristics of the spatial configuration and the agent) as well as six underlying subdimensions. Based on this framework, we cover a range of important operational issues tha...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Empirical models of manufacturer-retailer interaction: A review and agenda for future research

Marketing Letters, 2010

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Modeling the “Pseudodeductible” in Insurance Claims Decisions

Management Science, 2006

In many different managerial contexts, consumers “leave money on the table” by, for example, thei... more In many different managerial contexts, consumers “leave money on the table” by, for example, their failure to claim rebates, use available coupons, and so on. This project focuses on a related problem faced by homeowners who may be reluctant to file insurance claims despite the fact their losses are covered. We model this consumer decision by introducing the concept of the “pseudodeductible,” a latent threshold above the policy deductible that governs the homeowner’s claim behavior. In addition, we show how the observed number of claims can be modeled as the output of three stochastic processes that are separately, and in conjunction, managerially relevant: the rate at which losses occur, the size of each loss, and the choice of the individual to file or not file a claim. By allowing for the possibility of pseudodeductibles, one can sort out (and make accurate inferences about) these three processes. We test this model using a proprietary data set provided by State Farm, the largest...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Improving the crystal ball: harnessing consumer input to create retail prediction markets

Journal of Research in Interactive Marketing, 2010

PurposeRetail buyers' decisions result in billions of dollars of merchandise being purchased ... more PurposeRetail buyers' decisions result in billions of dollars of merchandise being purchased and offered for sale by retailers around the world. At present, retail buyers do not appear to be adequately harnessing consumer input to improve their forecasts. The purpose of this paper is to address this issue by introducing a new approach involving both retail buyers' consensus forecasts and those from a sample of “ordinary” consumers.Design/methodology/approachThe authors introduce a new approach to online forecasting that involves both retail buyers' consensus forecasts and those from a sample of “ordinary” consumers.FindingsThe results suggest an opportunity to create what are termed retail prediction markets that offer significant potential to improve the accuracy of buyers' forecasts.Originality/valueThe authors go beyond crowd sourcing technology and show how retail prediction markets may offer significant potential to improve the accuracy of retail buyers' for...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Learning-Based Model for Imputing Missing Levels in Partial Conjoint Profiles

Journal of Marketing Research, 2004

Respondents in a conjoint experiment sometimes are presented with successive partial product prof... more Respondents in a conjoint experiment sometimes are presented with successive partial product profiles. First, the authors model how respondents infer missing levels of product attributes in a partial conjoint profile by developing a learning-based imputation model that nests several extant models. The advantage of this approach over previous research is that it infers missing levels of an attribute not only from prior levels of the same attribute but also from prior levels of other attributes, especially ones that match the attribute levels of the current product profile. Second, the authors provide an empirical demonstration of their approach and test whether learning in conjoint studies occurs; to what extent; and in what manner it affects responses, partworths, and the relative importance of attributes. They show that the relative importance of attribute partworths can shift when subjects evaluate partial profiles, which suggests that consumers may construct rather than retrieve ...

Bookmarks Related papers MentionsView impact