TY - GEN
T1 - Data-driven customer segmentation based on online review analysis and customer network construction
AU - Park, Seyoung
AU - Kim, Harrison M.
N1 - Publisher Copyright:
Copyright © 2021 by ASME.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85119956109&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119956109&partnerID=8YFLogxK
U2 - 10.1115/DETC2021-70036
DO - 10.1115/DETC2021-70036
M3 - Conference contribution
AN - SCOPUS:85119956109
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 47th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - 47th Design Automation Conference, DAC 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021
Y2 - 17 August 2021 through 19 August 2021
ER -