scholarly journals Should Optimal Designers Worry About Consideration?

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Minhua Long ◽  
W. Ross Morrow
Keyword(s):  
Predictive Power ◽  
Predictive Accuracy ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Vehicle Body ◽  
Design Decisions ◽  
Consideration Set ◽  
Share Data ◽  
Real Purchasing ◽  
Consideration Set Formation

Consideration set formation using noncompensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via noncompensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to the data, and then optimize design decisions using the estimated models. Model predictive power and design profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to suboptimal design decisions when the population uses consideration behavior; convergence of compensatory models to noncompensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in noncompensatory screening is more valuable than heterogeneity in compensatory tradeoffs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply more profitable design decisions; different model forms can provide “descriptive” rather than “predictive” information that is useful for design.

scholarly journals Should Optimal Designers Worry About Consideration?

2014 ◽  
Author(s):  
Minhua Long ◽  
W. Ross Morrow
Keyword(s):  
Predictive Power ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Vehicle Body ◽  
Design Decisions ◽  
Consideration Set ◽  
Design Error ◽  
Trade Offs ◽  
Real Purchasing ◽  
Consideration Set Formation

Consideration set formation using non-compensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via non-compensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to this data, and then optimize design decisions using the estimated models. Model predictive power, design “error”, and profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply better design decisions; that is, different model forms can provide “descriptive” rather than “predictive” information that is useful for design.

scholarly journals What Do Consumers Consider Before They Choose? Identification from Asymmetric Demand Responses

2021 ◽  
Author(s):  
Jason Abaluck ◽  
Abi Adams-Prassl
Keyword(s):  
Lower Cost ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Consideration Sets ◽  
Consideration Set ◽  
Welfare Benefits ◽  
Continuous Choice ◽  
Opt Out ◽  
Prescription Drug Plans ◽  
Full Consideration

Abstract Consideration set models generalize discrete-choice models by relaxing the assumption that consumers consider all available options. Determining which options were considered has previously required either survey data or restrictions on how attributes impact consideration or utility. We provide an alternative route. In full-consideration models, choice probabilities satisfy a symmetry property analogous to Slutsky symmetry in continuous-choice models. This symmetry breaks down in consideration set models when changes in characteristics perturb consideration. We show that consideration probabilities are constructively identified from the resulting asymmetries. We validate our approach in a lab experiment where consideration sets are known and then apply our framework to study a “smart default” policy in Medicare Part D, wherein consumers are automatically reassigned to lower-cost prescription drug plans with the option of opting out. Full-consideration models imply such a policy will be ineffective because consumers will opt out to avoid switching costs. Allowing for inattention, we find that defaulting all consumers to lower-cost options produces negligible welfare benefits on average, but defaulting only consumers who would save at least $300 produces large benefits.

Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification

2014 ◽  
Vol 136 (12) ◽  
Author(s):  
C. Grace Haaf ◽  
Jeremy J. Michalek ◽  
W. Ross Morrow ◽  
Yimin Liu
Keyword(s):  
Market Share ◽  
Discrete Choice ◽  
Consumer Choice ◽  
Predictive Accuracy ◽  
Choice Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Model Fit ◽  
Model Specification ◽  
Vehicle Market

When design decisions are informed by consumer choice models, uncertainty in choice model predictions creates uncertainty for the designer. We investigate the variation and accuracy of market share predictions by characterizing fit and forecast accuracy of discrete choice models for the US light duty new vehicle market. Specifically, we estimate multinomial logit models for 9000 utility functions representative of a large literature in vehicle choice modeling using sales data for years 2004–2006. Each model predicts shares for the 2007 and 2010 markets, and we compare several quantitative measures of model fit and predictive accuracy. We find that (1) our accuracy measures are concordant: model specifications that perform well on one measure tend to also perform well on other measures for both fit and prediction. (2) Even the best discrete choice models exhibit substantial prediction error, stemming largely from limited model fit due to unobserved attributes. A naïve “static” model, assuming share for each vehicle design in the forecast year = share in the last available year, outperforms all 9000 attribute-based models when predicting the full market one year forward, but attribute-based models can predict better for four year forward forecasts or new vehicle designs. (3) Share predictions are sensitive to the presence of utility covariates but less sensitive to covariate form (e.g., miles per gallons versus gallons per mile), and nested and mixed logit specifications do not produce significantly more accurate forecasts. This suggests ambiguity in identifying a unique model form best for design. Furthermore, the models with best predictions do not necessarily have expected coefficient signs, and biased coefficients could misguide design efforts even when overall prediction accuracy for existing markets is maximized.

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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