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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.

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Figures

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Fig. 1

Graphical illustration of the major steps

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Fig. 2

Illustration of vehicle association network and product communities

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Fig. 3

Illustration of the choice set prediction approach

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Fig. 4

Bias quantification of estimated choice models under two noise scenarios

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Fig. 5

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

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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

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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.

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