Recently, many studies on product design have utilized online 
data for customer analysis. However, most of them treat 
online customers as a group of people with the same preferences 
while customer segmentation is a key strategy in conventional 
market analysis. To supplement this gap, this paper proposes 
a new methodology for online customer segmentation. First, 
customer attributes are extracted from online customer reviews. 
Then, a customer network is constructed based on the extracted 
attributes. Finally, the network is partitioned by modularity clustering 
and the resulting clusters are analyzed by topic frequency. 
The methodology is implemented to a smartphone review data. 
The result shows that online customers have different preferences 
as offline customers do, and they can be divided into separate 
groups with different tendencies for product features. This can 
help product designers to draw segment-based design implications 
from online data.&nbsp;In future works, each segment will be further analyzed to 
get implications for product design. There have been many studies on online customer analysis. Some 
of the previous methods can be used to analyze each segment and 
provide customized design implications. The impact of customer 
segmentation will be discussed by comparing the obtained design 
implications with and without customer segmentation.

Data-Driven Customer Segmentation Based On Online Review Analysis and Customer Network Construction

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

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