Papers: Choice-based preference modeling and design

Should Optimal Designers Worry About Consideration?

[+] Author and Article Information
Minhua Long

Mechanical Engineering,
Iowa State University,
Ames, IA 50014
e-mail: mhlong@iastate.edu

W. Ross Morrow

Analytics Scientist
Ford Research and Innovation Center Palo Alto,
Palo Alto, CA 94304
e-mail: wmorro13@ford.com

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 18, 2014; final manuscript received March 19, 2015; published online May 19, 2015. Assoc. Editor: Wei Chen.

J. Mech. Des 137(7), 071411 (Jul 01, 2015) (9 pages) Paper No: MD-14-1245; doi: 10.1115/1.4030178 History: Received April 18, 2014; Revised March 19, 2015; Online May 19, 2015

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.

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Grahic Jump Location
Fig. 1

KLD of predicted choice probability distribution from true behavior. Solid lines represent the range of observed values over 20 separate data sets while the dashed line represents the average value.

Grahic Jump Location
Fig. 2

Percent of ideal profits obtained by designs and prices under true behavior recovery when choosing designs and prices with estimated models. Note the log10 scale y-axis focuses on differences from 100%. Solid lines represent the range of observed values over 20 separate data sets while the dashed line represents the average value.

Grahic Jump Location
Fig. 3

Percent of ideal profits obtained by designs chosen using estimated models, but optimizing prices for these designs with knowledge of the true choice behavior. Note the log10 scale y-axis focuses on differences from 100%. Solid lines represent the range of observed values over 20 separate data sets while the dashed line represents the average value.

Grahic Jump Location
Fig. 4

Profit error versus KLD for each model, averaged over trials. Completely recovery of the ideal profit yields 0% error while zero profit yields 100% error.




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