TY - JOUR
T1 - Exploring Time-Based Characteristics of the E-Car Market for Effective Market Segmentation
AU - Tripathi, Shailesh
AU - Bachmann, Nadine
AU - Brunner, Manuel
AU - Jodlbauer, Herbert
N1 - Publisher Copyright:
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
PY - 2024
Y1 - 2024
N2 - In recent years, the electric car (e-car) market has seen noticeable growth attributed to technological advancements and new research offering multiple innovation possibilities for businesses, which should effectively bring new technologies to market, create added value for customers, and capture value for manufacturers. Leveraging data-driven methods and analytics within the e-car market is instrumental in guiding decision-making processes and facilitating the development of new value propositions and services. This study aims to provide insights into the evolution of e-car features, identify potential market segments, and support data-driven decision-making in business and marketing research. Our analysis focuses on e-car data from 2010-2023, utilizing data-driven techniques such as principal component analysis, clustering, trend analysis, and enrichment analysis. The trend analysis considers the series start year of e-car models and their corresponding summarized features as principal components and examines changes over time. Hierarchical cluster analysis then allows us to identify distinct segments in the e-car market, while enrichment analysis with respect to the series start year and brand helps us understand the latest innovations in different segments. Our analysis reveals a potential trend in the e-car market, suggesting a shift towards medium- and medium-large-sized cars that offer improved range, speed, and lower energy consumption. Additionally, the identified six clusters as the latest segments, which, in conjunction with the trend analysis, present opportunities for optimizing product development and identifying new market spaces. Managers can utilize the findings of this study to explore future market opportunities.
AB - In recent years, the electric car (e-car) market has seen noticeable growth attributed to technological advancements and new research offering multiple innovation possibilities for businesses, which should effectively bring new technologies to market, create added value for customers, and capture value for manufacturers. Leveraging data-driven methods and analytics within the e-car market is instrumental in guiding decision-making processes and facilitating the development of new value propositions and services. This study aims to provide insights into the evolution of e-car features, identify potential market segments, and support data-driven decision-making in business and marketing research. Our analysis focuses on e-car data from 2010-2023, utilizing data-driven techniques such as principal component analysis, clustering, trend analysis, and enrichment analysis. The trend analysis considers the series start year of e-car models and their corresponding summarized features as principal components and examines changes over time. Hierarchical cluster analysis then allows us to identify distinct segments in the e-car market, while enrichment analysis with respect to the series start year and brand helps us understand the latest innovations in different segments. Our analysis reveals a potential trend in the e-car market, suggesting a shift towards medium- and medium-large-sized cars that offer improved range, speed, and lower energy consumption. Additionally, the identified six clusters as the latest segments, which, in conjunction with the trend analysis, present opportunities for optimizing product development and identifying new market spaces. Managers can utilize the findings of this study to explore future market opportunities.
KW - e-car data
KW - factor analysis
KW - PCA
KW - segmentation
KW - trend analysis
UR - http://www.scopus.com/inward/record.url?scp=85189804310&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2024.01.007
DO - 10.1016/j.procs.2024.01.007
M3 - Conference article
AN - SCOPUS:85189804310
SN - 1877-0509
VL - 232
SP - 64
EP - 76
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023
Y2 - 22 November 2023 through 24 November 2023
ER -