7th International Conference on Collaborative Innovation Networks (COINs17) – Extended Abstract

Data-Driven Vehicle Preference Modeling and Prediction: A Multidimensional NetworkAnalysis Approach
Mingxian Wang a, Zhenghui Sha b, Yun Huang c, J. Sophia Fu c, Yan Fu a, Noshir Contractor c,Wei Chen ca

Ford Motor Company; b University of Arkansas; c Northwestern UniversityIntroduction
The auto market in China has seen unprecedented expansion during the past decade and has
become highly complex with the evolving marketing structures and diverse consumer needs andtastes. To decide which vehicle segments and customer groups automakers should focus on andexploit the trends of “bigger market” and “repeat buyers,” a more granular understanding of theeffect of customers’ social interactions and preference heterogeneities is needed. Our study aims
to develop a data-driven multidimensional network modeling approach for analyzing andpredicting heterogeneous, complex customer preferences using buyer survey data collected inChina. The developed methodology can support product attribute prioritization andrevenue/volume forecast for future product planning and development.
Methodology
The core of the proposed research is to model customer-product interactions as a complex sociotechnical network. Specifically, we propose a Multidimensional Customer-Product Network(MCPN) framework to simultaneously model “customer” and “product” entities as well as multiple
types of relations such as consideration decision, purchase decision, product association, andsocial connection.
We start with the simplest unimodal network configuration where customers’ cross-shopping
behaviors and product similarities are analyzed to reveal the implied product competition, market
segmentation, and product positions. We then include multidimensional structures to integrate
customer preference decisions with product
Purchase decision
Feature association
feature similarities and model the dependence
Consideration decision
Preference associationof preference heterogeneity, product feature
Social interaction
association, and consideration and purchasedecisions. Finally, social influences on new
product adoptions are analyzed by introducing
customer-customer relations together withother product-product and customer-productrelations, as shown in Fig.1.
In this study, both descriptive network analyses
and analytical network models are performed.
In descriptive analyses, for example, ourresearch examines the impact of productfeatures using joint correspondence analysis(JCA) [1]. Taking customer-product relations
and similarity relations between products as anetwork, we incorporate multiple relations,attributes of customers, and features ofFig. 1. Modeling Customer-Product Relations as aComplex Socio-Technical Networked System1

7th International Conference on Collaborative Innovation Networks (COINs17) – Extended Abstract

products, to identify the principal factors invehicle purchase. The graphical output fromthe JCA (Fig. 2) remarkably simplifies thecomplex relationship structures betweendifferent sets of variables and generates asimple yet exhaustive description of theunderlying relationships. The results can beused as a guideline to better understand theproduct feature space and explore newpredictors of customer preferences.
In analytical network modeling, we employ
the Exponential Random Graph Models(ERGM) [2] as a unified statistical framework
Fig. 2. Joint Correspondence Analysis result ofVehicles and Perceived vehicle characteristics
for analyzing multiple relations in an MCPN
in support of engineering and businessdecisions. We find that the inferential network analysis with ERGMs enables the detailed modelingof both network structures and customer/product attributes in a rigorous statistical sense. Inaddition to ERGM, we also explored the capability of other network models. For example, themultiple regression quadratic assignment procedure (MRQAP) [3] is employed to predict productco-consideration relations as a function of various effect networks created by associations ofproduct attributes and customer demographics. With the developed MRQAP model, simulation
can be performed to forecast technological impacts on customers’ co-considerations and productcompetitions. In this study, we forecast the impacts of the adoption of two vehicle technologies –the fuel economy-boosting technology and the turbo engine technology. The case study provides
vehicle designers with insights into the change of market competitions brought by these newtechnological developments.
Summary and Discussion
The proposed Multidimensional Customer-Product Network (MCPN) framework has beensuccessfully applied to investigate products’ co-consideration relations [4-7], customers’consideration decisions [4] and choice decisions [8]. Through various network models, we havedemonstrated the use cases of MCPN, which enable the graphical exploration of the productfeature space and the forecast of technological impacts (e.g., turbo) on competitions amongindividual products and brand lines in a market. Our proposed MCPN framework for complex
customer-product systems also provides a platform, which enables rich opportunities of crossdisciplinary network research.
The proposed multidimensional network preference models have the following advantages: 1)
product associations can be modeled explicitly, 2) evaluation of social influence is achievable, 3)
nested decisions can be analyzed through network structural modeling, 4) interdependencies
among product/customer attributes can be handled, and 5) ERGM results can be easily integrated
into a vehicle design optimization problem.
Acknowledgement
The authors gratefully acknowledge financial support from Ford-Northwestern Alliance Projectand NSF (CMMI-1436658).
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7th International Conference on Collaborative Innovation Networks (COINs17) – Extended Abstract

References[1] Greenacre, M. & Blasius, J. “Multiple correspondence analysis and related methods”, CRC Press,
2006.
[2] Wang, P., Robins, G., Pattison, P. & Lazega, E. “Exponential random graph models for multilevelnetworks”. Social Networks.
[3] Krackhardt, D. “Predicting with networks: Nonparametric multiple regression analysis of dyadic data”.
Social networks, 10(4), 1988, 359-381.
[4] Wang, M., et al. “Modeling customer preferences using multidimensional network analysis in engineeringdesign”. Design Science, 2016. 2.
[5] Wang, M., et al. “A Network Approach for Understanding and Analyzing Product Co-consideration
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[6] Wang, M., et al. “Forecasting Technological Impacts on Customers’ Co-Consideration Behaviors: A
Data-Driven Network Analysis Approach”. ASME 2016 International Design Engineering TechnicalConferences and Computers and Information in Engineering Conference, Volume 2A: 42nd DesignAutomation Conference: V02AT03A040. doi:10.1115/DETC2016-60015.
[7] Sha, Z., et al. “Modeling Product Co-Consideration Relations: A Comparative Study of Two Network
Models.” in Submitted to the 21st International Conference on Engineering Design, ICED17. Vancouver,
Canada, Aug. 21-25, 2017.
[8] Fu, J., et al. “Modeling Customer Choice Preferences in Engineering Design Using Bipartite Network
Analysis.” in Submitted to the ASME 2017 International Design Engineering Technical Conferences andComputers and Information in Engineering Conference. Cleveland, OH, Aug. 6-9, 2017.

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