0
Papers: Choice-based preference modeling and design

A Data-Driven Network Analysis Approach to Predicting Customer Choice Sets for Choice Modeling in Engineering Design

[+] Author and Article Information
Mingxian Wang

Department of Mechanical Engineering,
Northwestern University,
Evanston, IL 60208
e-mail: mingxianwang2016@u.northwestern.edu

Wei Chen

Wilson-Cook Professor in Engineering Design
Department of Mechanical Engineering,
Northwestern University,
Evanston, IL 60208
e-mail: weichen@northwestern.edu

Studies show that the number of vehicles a customer seriously considers is often in the range of 3-6. [14,15]

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 9, 2014; final manuscript received March 10, 2015; published online May 19, 2015. Assoc. Editor: Bernard Yannou.

J. Mech. Des 137(7), 071410 (Jul 01, 2015) (11 pages) Paper No: MD-14-1553; doi: 10.1115/1.4030160 History: Received September 09, 2014; Revised March 10, 2015; Online May 19, 2015

In this paper, we propose a data-driven network analysis based approach to predict individual choice sets for customer choice modeling in engineering design. We apply data analytics to mine existing data of customer choice sets, which is then used to predict choice sets for individual customers in a new choice modeling scenario where choice set information is lacking. Product association network is constructed to identify product communities based on existing data of customer choice sets, where links between products reflect the proximity or similarity of two products in customers' perceptual space. To account for customer heterogeneity, customers are classified into clusters (segments) based on their profile attributes and for each cluster the product consideration frequency is computed. For predicting choice sets in a new choice modeling scenario, a probabilistic sampling approach is proposed to integrate product associations, customer segments, and the link strengths in the product association network. In case studies, we first implement the approach using an example with simulated choice set data. The quality of predicted choice sets is examined by assessing the estimation bias of the developed choice model. We then demonstrate the proposed approach using actual survey data of vehicle choice, illustrating the benefits of improving a choice model through choice set prediction and the potential of using such choice models to support engineering design decisions. This research also highlights the benefits and potentials of using network techniques for understanding customer preferences in product design.

FIGURES IN THIS ARTICLE
<>
Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Chen, W., Hoyle, C., and Wassenaar, H. J., 2013, Decision-Based Design: Integrating Consumer Preferences Into Engineering Design, Springer, London [CrossRef].
Kumar, D., Chen, W., and Simpson, T. W., 2009, “A Market-Driven Approach to Product Family Design,” Int. J. Prod. Res., 47(1), pp. 71–104. [CrossRef]
Hoyle, C., Chen, W., Wang, N., and Koppelman, F. S., 2010, “Integrated Bayesian Hierarchical Choice Modeling to Capture Heterogeneous Consumer Preferences in Engineering Design,” ASME J. Mech. Des., 132(12), p. 121010. [CrossRef]
He, L., Chen, W., Hoyle, C., and Yannou, B., 2012, “Choice Modeling for Usage Context-Based Design,” ASME J. Mech. Des., 134(3), p. 031007. [CrossRef]
He, L., Wang, M., Chen, W., and Conzelmann, G., 2014, “Incorporating Social Impact on New Product Adoption in Choice Modeling: A Case Study in Green Vehicles,” Transp. Res. Part D: Transp. Environ., 32(2014), pp. 421–434. [CrossRef]
Kim, H. M., Kumar, D. K., Chen, W., and Papalambros, P. Y., 2006, “Target Exploration for Disconnected Feasible Regions in Enterprise-Driven Multilevel Product Design,” AIAA J., 44(1), pp. 67–77. [CrossRef]
Michalek, J. J., Feinberg, F. M., and Papalambros, P. Y., 2005, “Linking Marketing and Engineering Product Design Decisions Via Analytical Target Cascading*,” J. Prod. Innovation Manage., 22(1), pp. 42–62. [CrossRef]
Shiau, C.-S. N., and Michalek, J. J., 2009, “Should Designers Worry About Market Systems?,” ASME J. Mech. Des., 131(1), p. 011011. [CrossRef]
Morrow, W. R., Long, M., and MacDonald, E. F., 2014, “Market-System Design Optimization With Consider-Then-Choose Models,” ASME J. Mech. Des., 136(3), p. 031003. [CrossRef]
Resende, C. B., Heckmann, C. G., and Michalek, J. J., 2012, “Robust Design for Profit Maximization With Aversion to Downside Risk From Parametric Uncertainty in Consumer Choice Models,” ASME J. Mech. Des., 134(10), p. 100901. [CrossRef]
Train, K. E., 2009, Discrete Choice Methods With Simulation, Cambridge University. [CrossRef]
Ben-Akiva, M. E., and Lerman, S. R., 1985, Discrete Choice Analysis: Theory and Application to Travel Demand, MIT, Cambridge.
U.S. Department of Transportation, F. H. A., 2009, “National Household Travel Survey,” http://nhts.ornl.gov
Hauser, J. R., and Wernerfelt, B., 1990, “An Evaluation Cost Model of Consideration Sets,” J. Consum. Res., 16(4), pp. 393–408. [CrossRef]
Hauser, J. R., Ding, M., and Gaskin, S. P., 2009, “Non-Compensatory (and Compensatory) Models of Consideration-Set Decisions,” Sawtooth Software Conference, Sequim.
Nerella, S., and Bhat, C. R., 2004, “Numerical Analysis of Effect of Sampling of Alternatives in Discrete Choice Models,” Transp. Res. Rec.: J. Transp. Res. Board, 1894(1), pp. 11–19. [CrossRef]
McFadden, D., 1978, Modelling the Choice of Residential Location, Institute of Transportation Studies, University of California, Berkeley, CA.
Peters, T., Adamowicz, W. L., and Boxall, P. C., 1995, “Influence of Choice Set Considerations in Modeling the Benefits From Improved Water Quality,” Water Resour. Res., 31(7), pp. 1781–1787. [CrossRef]
Williams, H., and Ortúzar, J. D., 1982, “Behavioural Theories of Dispersion and the Mis-Specification of Travel Demand Models,” Transp. Res. Part B: Methodol., 16(3), pp. 167–219. [CrossRef]
Shocker, A. D., Ben-Akiva, M., Boccara, B., and Nedungadi, P., 1991, “Consideration Set Influences on Consumer Decision-Making and Choice: Issues, Models, and Suggestions,” Mark. Lett., 2(3), pp. 181–197. [CrossRef]
Parsons, G. R., and Kealy, M. J., 1992, “Randomly Drawn Opportunity Sets in a Random Utility Model of Lake Recreation,” Land Econ., 68(1), pp. 93–106. [CrossRef]
Anderson, J. R., and Bower, G. H., 1974, “A Propositional Theory of Recognition Memory,” Memory Cognit., 2(3), pp. 406–412. [CrossRef]
Henderson, G. R., Iacobucci, D., and Calder, B. J., 1998, “Brand Diagnostics: Mapping Branding Effects Using Consumer Associative Networks,” Eur. J. Oper. Res., 111(2), pp. 306–327. [CrossRef]
Pandey, G., Chawla, S., Poon, S., Arunasalam, B., and Davis, J. G., 2009, “Association Rules Network: Definition and Applications,” Stat. Anal. Data Min., 1(4), pp. 260–279. [CrossRef]
Silverstein, C., Brin, S., and Motwani, R., 1998, “Beyond Market Baskets: Generalizing Association Rules to Dependence Rules,” Data Min. Knowl. Discovery, 2(1), pp. 39–68. [CrossRef]
Swait, J., and Ben-Akiva, M., 1987, “Incorporating Random Constraints in Discrete Models of Choice Set Generation,” Transp. Res. Part B: Methodol., 21(2), pp. 91–102. [CrossRef]
Hauser, J. R., 2014, “Consideration-Set Heuristics,” J. Bus. Res., 67(8), pp. 1688–1699. [CrossRef]
Ben-Akiva, M., and Boccara, B., 1995, “Discrete Choice Models With Latent Choice Sets,” Int. J. Res. Mark., 12(1), pp. 9–24. [CrossRef]
Andrews, R. L., and Srinivasan, T., 1995, “Studying Consideration Effects in Empirical Choice Models Using Scanner Panel Data,” J. Mark. Res., 32(1), pp. 30–41. [CrossRef]
Swait, J., 2001, “A Non-Compensatory Choice Model Incorporating Attribute Cutoffs,” Transp. Res. Part B: Methodol., 35(10), pp. 903–928. [CrossRef]
Cantillo, V., and Ortúzar, J. D. D., 2005, “A Semi-Compensatory Discrete Choice Model With Explicit Attribute Thresholds of Perception,” Transp. Res. Part B: Methodol., 39(7), pp. 641–657. [CrossRef]
Dieckmann, A., Dippold, K., and Dietrich, H., 2009, “Compensatory Versus Noncompensatory Models for Predicting Consumer Preferences,” Judgment Decis. Making, 4(3), pp. 200–213.
Martínez, F., Aguila, F., and Hurtubia, R., 2009, “The Constrained Multinomial Logit: A Semi-Compensatory Choice Model,” Transp. Res. Part B: Methodol., 43(3), pp. 365–377. [CrossRef]
Silva-Risso, J., and Ionova, I., 2008, “Practice Prize Winner-A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions,” Mark. Sci., 27(4), pp. 545–566. [CrossRef]
Wasserman, S., and Faust, K., 1994, Social Network Analysis: Methods and Applications, Cambridge University, Cambridge. [CrossRef]
Sosa, M., Mihm, J., and Browning, T., 2011, “Degree Distribution and Quality in Complex Engineered Systems,” ASME J. Mech. Des., 133(10), p. 101008. [CrossRef]
Sosa, M. E., Eppinger, S. D., and Rowles, C. M., 2007, “A Network Approach to Define Modularity of Components in Complex Products,” ASME J. Mech. Des., 129(11), pp. 1118–1129. [CrossRef]
Netzer, O., Feldman, R., Goldenberg, J., and Fresko, M., 2012, “Mine Your Own Business: Market-Structure Surveillance Through Text Mining,” Mark. Sci., 31(3), pp. 521–543. [CrossRef]
Raeder, T., and Chawla, N. V., 2011, “Market Basket Analysis With Networks,” Social Network Anal. Min., 1(2), pp. 97–113. [CrossRef]
Moe, W. W., 2006, “An Empirical Two-Stage Choice Model With Varying Decision Rules Applied to Internet Clickstream Data,” J. Mark. Res., 43(4), pp. 680–692. [CrossRef]
Newman, M. E., and Girvan, M., 2004, “Finding and Evaluating Community Structure in Networks,” Phys. Rev. E, 69(2), p. 026113. [CrossRef]
Weinstein, A., 1994, Market Segmentation: Using Demographics, Psychographics and Other Niche Marketing Techniques to Predict and Model Customer Behavior, Probus Publishing Company, Chicago.
Eshghi, A., Haughton, D., Legrand, P., Skaletsky, M., and Woolford, S., 2011, “Identifying Groups: A Comparison of Methodologies,” J. Data Sci., 9, pp. 271–291.
Lemp, J. D., and Kockelman, K. M., 2012, “Strategic Sampling for Large Choice Sets in Estimation and Application,” Transp. Res. Part A: Policy Pract., 46(3), pp. 602–613. [CrossRef]
Hyndman, R. J., and Koehler, A. B., 2006, “Another Look at Measures of Forecast Accuracy,” Int. J. Forecasting, 22(4), pp. 679–688. [CrossRef]
Fruchterman, T. M. J., and Reingold, E. M., 1991, “Graph Drawing by Force-Directed Placement,” Software: Pract. Exp., 21(11), pp. 1129–1164. [CrossRef]
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B., 2007, “Measurement and Analysis of Online Social Networks,” The 7thACM SIGCOMM Conference on Internet Measurement, ACM, New York, pp. 29–42. [CrossRef]
Hartigan, J. A., and Wong, M. A., 1979, “Algorithm AS 136: A k-Means Clustering Algorithm,” Appl. Stat., 28(1), pp. 100–108. [CrossRef]
Krackhardt, D., 1988, “Predicting With Networks: Nonparametric Multiple Regression Analysis of Dyadic Data,” Social Networks, 10(4), pp. 359–381. [CrossRef]
Bass, F. M., 1974, “The Theory of Stochastic Preference and Brand Switching,” J. Mark. Res., pp. 1–20. [CrossRef]
Clauset, A., Newman, M. E., and Moore, C., 2004, “Finding Community Structure in Very Large Networks,” Phys. Rev. E, 70(6), p. 066111. [CrossRef]
Csardi, G., and Nepusz, T., 2005, The Igraph Software Package for Complex Network Research, InterJournal, Complex Systems, http://igraph.org

Figures

Grahic Jump Location
Fig. 1

Graphical illustration of the major steps

Grahic Jump Location
Fig. 2

Illustration of vehicle association network and product communities

Grahic Jump Location
Fig. 3

Illustration of the choice set prediction approach

Grahic Jump Location
Fig. 4

Bias quantification of estimated choice models under two noise scenarios

Grahic Jump Location
Fig. 5

Frequency distribution of network statistics: (a) link strength and (b) node degree

Grahic Jump Location
Fig. 6

Vehicle association network with colored communities and structural properties: (a) network derived using training choice sets and (b) network derived using predicted choice sets

Grahic Jump Location
Fig. 7

Examples of customer heterogeneity in choice set preference. CF and CP for customers purchased the same Hyundai Entourage but belong to different clusters.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In