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Flowchart of the methodology and key components

Graphical Abstract Figure

Flowchart of the methodology and key components

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Abstract

Network-based analyses have effectively understood customer preferences through interactions between customers and products, particularly for tailored product design. However, research applying this analysis to diverse customers with varied preferences is limited. This paper introduces a market-segmented network modeling approach, guided by customer preference, to explore heterogeneity in customers’ two-stage decision-making process: consideration-then-choice. In heterogeneous markets, customers with similar characteristics or purchasing similar products can exhibit different decision-making processes. Therefore, this method segments customers based on preferences rather than just characteristics, allowing for more accurate choice modeling. Using joint correspondence analysis, we identify associations between customer attributes and preferred products, characterizing market segments through clustering. We then build individual bipartite customer–product networks and apply the exponential random graph model to compare the product features influencing customer considerations and choices in various market segments. Using a US household vacuum cleaner survey, our method detected different customer preferences for the same product attribute at different decision-making stages. The market-segmentation model outperforms the non-segmented benchmark in prediction, highlighting its accuracy in predicting varied customer behaviors. This study underscores the vital role of preference-guided segmentation in product design, illustrating how understanding customer preferences at different decision stages can inform and refine design strategies, ensuring products align with diverse market needs.

References

1.
Hoyle
,
C.
,
Chen
,
W.
,
Ankenman
,
B.
, and
Wang
,
N.
,
2009
, “
Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design
,”
ASME J. Mech. Des.
,
131
(
7
), pp.
427
436
.
2.
Wassenaar
,
H. J.
, and
Chen
,
W.
,
2003
, “
An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling
,”
ASME J. Mech. Des.
,
125
(
3
), pp.
490
497
.
3.
Wang
,
M.
,
Chen
,
W.
,
Huang
,
Y.
,
Contractor
,
N. S.
, and
Fu
,
Y.
,
2016
, “
Modeling Customer Preferences Using Multidimensional Network Analysis in Engineering Design
,”
Des. Sci.
,
2
, p.
e11
.
4.
Sha
,
Z.
,
Wang
,
M.
,
Huang
,
Y.
,
Contractor
,
N.
,
Fu
,
Y.
, and
Chen
,
W.
,
2017
, “
Modeling Product Co-Consideration Relations: A Comparative Study of Two Network Models
,”
DS 87-6 Proceedings of the 21st International Conference on Engineering Design (ICED 17)
,
Vancouver, Canada
,
Aug. 21–25
.
5.
Cui
,
Y.
,
Ahmed
,
F.
,
Sha
,
Z.
,
Wang
,
L.
,
Fu
,
Y.
,
Contractor
,
N.
,
Chen
,
W.
, and
Suweis
,
S.
,
2022
, “
A Weighted Statistical Network Modeling Approach to Product Competition Analysis
,”
Complex
,
2022
, p.
9417869
.
6.
Train
,
K.
,
1986
,
Qualitative Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand
, Vol.
10
,
MIT Press
.
7.
Sha
,
Z.
,
Cui
,
Y.
,
Xiao
,
Y.
,
Stathopoulos
,
A.
,
Contractor
,
N.
,
Fu
,
Y.
, and
Chen
,
W.
,
2023
, “
A Network-Based Discrete Choice Model for Decision-Based Design
,”
Des. Sci.
,
9
, p.
e7
.
8.
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
,”
Market. Lett.
,
2
(
3
), pp.
181
197
.
9.
Hauser
,
J. R.
,
Ding
,
M.
, and
Gaskin
,
S. P.
,
2009
, “
Non-Compensatory (and Compensatory) Models of Consideration-Set Decisions
,”
Proceedings of the Sawtooth Software Conference
,
Provo, UT
,
May
.
10.
Gaskin
,
S.
,
Evgeniou
,
T.
,
Bailiff
,
D.
, and
Hauser
,
J.
,
2007
, “
Two-Stage Models: Identifying Non-Compensatory Heuristics for the Consideration Set Then Adaptive Polyhedral Methods Within the Consideration Set
,”
Proceedings of the Sawtooth Software Conference
,
Santa Rosa, CA
,
Oct. 17–19
, pp.
67
83
.
11.
Fu
,
J. S.
,
Sha
,
Z.
,
Huang
,
Y.
,
Wang
,
M.
,
Fu
,
Y.
, and
Chen
,
W.
,
2017
, “
Two-Stage Modeling of Customer Choice Preferences in Engineering Design Using Bipartite Network Analysis
,” p.
V02AT03A039
.
12.
Beane
,
T.
, and
Ennis
,
D.
,
1987
, “
Market Segmentation: A Review
,”
Eur. J. Mark.
,
21
(
5
), pp.
20
42
.
13.
Goyat
,
S.
,
2011
, “
The Basis of Market Segmentation: A Critical Review of Literature
,”
Eur. J. Bus. Manage.
,
3
(
9
), pp.
45
54
. https://api.semanticscholar.org/CorpusID:55906805
14.
Lin
,
C.
,
2002
, “
Segmenting Customer Brand Preference: Demographic Or Psychographic
,”
J. Prod. Brand Manage.
,
11
(
4
), pp.
249
268
.
15.
Susilo
,
W. H.
,
2016
, “
An Impact of Behavioral Segmentation to Increase Consumer Loyalty: Empirical Study in Higher Education of Postgraduate Institutions at Jakarta
,”
Proc. Soc. Behav. Sci.
,
229
, pp.
183
195
.
16.
Peltier
,
J. W.
, and
Schribrowsky
,
J. A.
,
1997
, “
The Use of Need-Based Segmentation for Developing Segment-Specific Direct Marketing Strategies
,”
J. Direct Market.
,
11
(
4
), pp.
53
62
.
17.
Greenacre
,
M.
, and
Blasius
,
J.
,
2006
,
Multiple Correspondence Analysis and Related Methods
,
CRC Press, New York
.
18.
Hunter
,
D. R.
,
Handcock
,
M. S.
,
Butts
,
C. T.
,
Goodreau
,
S. M.
, and
Morris
,
M.
,
2008
, “
ERGM: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks
,”
J. Stat. Softw.
,
24
(
3
), pp.
1
29
.
19.
Xiao
,
Y.
,
Cui
,
Y.
,
Raut
,
N.
,
Januar
,
J.
,
Koskinen
,
J.
,
Contractor
,
N.
,
Chen
,
W.
, and
Sha
,
Z.
,
2024
, “
Survey Data on Customer Two-Stage Decision-Making Process in Household Vacuum Cleaner Market
,”
Data Brief
,
54
, p.
110353
.
20.
Jin
,
J.
,
Liu
,
Y.
,
Ji
,
P.
, and
Kwong
,
C. K.
,
2018
, “
Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product Design
,”
ASME J. Comput. Inf. Sci. Eng.
,
19
(
1
), p.
010801
.
21.
Tuarob
,
S.
, and
Tucker
,
C. S.
,
2015
, “
Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data
,”
ASME J. Comput. Inf. Sci. Eng.
,
15
(
3
), p.
031003
.
22.
Afshari
,
H.
,
Peng
,
Q.
, and
Gu
,
P.
,
2016
, “
Design Optimization for Sustainable Products Under Users’ Preference Changes
,”
ASME J. Comput. Inf. Sci. Eng.
,
16
(
4
), p.
041001
.
23.
Jiang
,
W.
,
Zhao
,
W.
,
Du
,
L.
,
Zhang
,
K.
, and
Yu
,
M.
,
2022
, “
Product Perceptual Similarity Evaluation: From Attributive Error to Human Knowledge Hierarchy
,”
ASME J. Comput. Inf. Sci. Eng.
,
23
(
2
), p.
021002
.
24.
Siddharth
,
L.
,
Blessing
,
L.
, and
Luo
,
J.
,
2022
, “
Natural Language Processing In-and-For Design Research
,”
Des. Sci.
,
8
, p.
e21
.
25.
Wang
,
M.
,
Chen
,
W.
,
Huang
,
Y.
,
Contractor
,
N. S.
, and
Fu
,
Y.
,
2015
, “
A Multidimensional Network Approach for Modeling Customer-Product Relations in Engineering Design
,”
Proceedings of the ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Boston, MA
,
Aug. 2–5
.
26.
Urberg
,
K. A.
,
1992
, “
Locus of Peer Influence: Social Crowd and Best Friend
,”
J. Youth Adolesc.
,
21
(
4
), pp.
439
450
.
27.
Bi
,
Y.
,
Qiu
,
Y.
,
Sha
,
Z.
,
Wang
,
M.
,
Fu
,
Y.
,
Contractor
,
N.
, and
Chen
,
W.
,
2021
, “
Modeling Multi-year Customers’ Considerations and Choices in China’s Auto Market Using Two-Stage Bipartite Network Analysis
,”
Netw. Spatial Econ.
,
21
, pp.
365
385
.
28.
Robins
,
G.
,
Pattison
,
P.
,
Kalish
,
Y.
, and
Lusher
,
D.
,
2007
, “
An Introduction to Exponential Random Graph (p*) Models for Social Networks
,”
Soc. Netw.
,
29
(
2
), pp.
173
191
.
29.
Wang
,
P.
,
2012
, “Exponential Random Graph Model Extensions: Models for Multiple Networks and Bipartite Networks,”
Exponential Random Graph Model Extensions: Models for Multiple Networks and Bipartite Networks
,
Cambridge University Press
, pp.
115
129
.
30.
Hoffman
,
D. L.
, and
Franke
,
G. R.
,
1986
, “
Correspondence Analysis: Graphical Representation of Categorical Data in Marketing Research
,”
J. Mark. Res.
,
23
(
3
), pp.
213
227
.
31.
Beldona
,
S.
,
Morrison
,
A. M.
, and
O’Leary
,
J.
,
2005
, “
Online Shopping Motivations and Pleasure Travel Products: A Correspondence Analysis
,”
Tourism Manage.
,
26
(
4
), pp.
561
570
.
32.
de Nooy
,
W.
,
2003
, “
Fields and Networks: Correspondence Analysis and Social Network Analysis in the Framework of Field Theory
,”
Poetics
,
31
(
5
), pp.
305
327
.
33.
Wang
,
M.
,
Huang
,
Y.
,
Contractor
,
N.
,
Fu
,
Y.
, and
Chen
,
W.
,
2016
, “
A Network Approach for Understanding and Analyzing Product Co-Consideration Relations in Engineering Design
,”
DS 84: Proceedings of the DESIGN 2016 14th International Design Conference
,
Cavtat, Dubrovnik, Croatia
,
May 16–19
.
34.
Newman
,
M. E. J.
, and
Girvan
,
M.
,
2004
, “
Finding and Evaluating Community Structure in Networks
,”
Phys. Rev. E
,
69
(
2
), p.
026113
.
35.
Ketchen
,
D. J.
, and
Shook
,
C. L.
,
1996
, “
The Application of Cluster Analysis in Strategic Management Research: An Analysis and Critique
,”
Strat. Manage. J.
,
17
(
6
), pp.
441
458
.
36.
Reichardt
,
J.
, and
Bornholdt
,
S.
,
2006
, “
Statistical Mechanics of Community Detection
,”
Phys. Rev. E
,
74
(
1
), p.
016110
.
37.
Huang
,
Z.
,
1997
, “
Clustering Large Data Sets With Mixed Numeric and Categorical Values
,”
Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference
,
Singapore
,
Feb. 23–24
, pp.
21
34
.
38.
Koskinen
,
J.
,
Robins
,
G.
, and
Pattison
,
P.
,
2010
, “
Analysing Exponential Random Graph (P-Star) Models With Missing Data Using Bayesian Data Augmentation
,”
Stat. Methodol.
,
7
(
3
), pp.
366
384
.
39.
Wang
,
C.
,
Butts
,
C. T.
,
Hipp
,
J. R.
,
Jose
,
R.
, and
Lakon
,
C. M.
,
2016
, “
Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method With Application to Add Health
,”
Soc. Netw.
,
45
, pp.
89
98
.
40.
Cui
,
Y.
,
2023
, “
Multi-stage Customer Preferences Modeling Using Data-Driven Network Analysis
,” Ph.D. thesis,
Northwestern University
,
Evanston, IL
.
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