are applied to new datasets or scenarios. Domain
adaptation, on the other hand, focuses on adjusting
models to perform well in different but related
domains. These techniques can significantly improve
the generalizability of target marketing models,
allowing them to be more flexible and effective
across various contexts and industries. This would
reduce the resource-intensive process of building new
models from scratch for different applications.
Privacy concerns in target marketing can be
addressed by adopting federated learning approaches.
Federated learning makes it possible to train models
on multiple decentralized devices or servers that
contain local data samples, without having to
exchange the data itself. This method ensures that
customer data remains on their devices, reducing the
risk of privacy breaches. Federated learning also
complies with stringent data privacy regulations, such
as GDPR, by enabling secure, decentralized learning
processes. By implementing federated learning,
organizations can enhance customer trust while still
benefiting from personalized marketing strategies
that leverage large-scale data insights.
4 CONCLUSIONS
The investigation into the latest trends in customer
segmentation reaffirms the critical role that machine
learning plays in enhancing targeted marketing
strategies. As highlighted in the introduction, the
transition from traditional methods to advanced
algorithms offers businesses a competitive edge in
understanding customer behavior and optimizing
marketing efforts. This review’s main contribution
lies in synthesizing the current state of machine
learning applications across various industries,
providing a comprehensive analysis of their strengths
and challenges. By examining case studies from
banking, telecommunications, and healthcare, this
paper demonstrates the effectiveness of machine
learning models like k-means clustering, auto
machine learning, decision trees, and neural networks
in improving segmentation accuracy and customer
insights. However, the review also identifies
significant limitations, including the challenges of
model interpretability, domain applicability, and
privacy concerns. Addressing these issues will
require future research focused on integrating
interpretability tools like SHAP and LIME, exploring
transfer learning and domain adaptation, and adopting
federated learning to enhance privacy. These
advancements are essential for ensuring that machine
learning continues to provide valuable and
trustworthy insights in customer segmentation.
REFERENCES
Beutel, J., List, S., & von Schweinitz, G. 2019. Does
machine learning help us predict banking crises?.
Journal of Financial Stability, 45, 100693.
Ferreira, L., Pilastri, A. L., Martins, C., Santos, P., &
Cortez, P. 2020. An Automated and Distributed
Machine Learning Framework for Telecommunications
Risk Management. In ICAART (2) (pp. 99-107).
Guni, A., Normahani, P., Davies, A., & Jaffer, U. 2021.
Harnessing machine learning to personalize web-based
health care content. Journal of medical Internet
research, 23(10), e25497.
Monge, M., Quesada-López, C., Martínez, A., & Jenkins,
M. 2021. Data mining and machine learning techniques
for bank customers segmentation: A systematic
mapping study. In Intelligent Systems and Applications:
Proceedings of the 2020 Intelligent Systems Conference
(IntelliSys) Volume 2 (pp. 666-684). Springer
International Publishing.
Monil, P., Darshan, P., Jecky, R., Vimarsh, C., & Bhatt, B.
R. 2020. Customer segmentation using machine
learning. International Journal for Research in Applied
Science and Engineering Technology (IJRASET), 8(6),
2104-2108.
Narayana, V. L., Sirisha, S., Divya, G., Pooja, N. L. S., &
Nouf, S. A. 2022. Mall customer segmentation using
machine learning. In 2022 International Conference on
Electronics and Renewable Systems (ICEARS) (pp.
1280-1288). IEEE.
Sharaf Addin, E. H., Admodisastro, N., Mohd Ashri, S. N.
S., Kamaruddin, A., & Chong, Y. C. 2022. Customer
Mobile Behavioral Segmentation and Analysis in
Telecom Using Machine Learning. Applied Artificial
Intelligence, 36(1).
Swenson, E. R., Bastian, N. D., & Nembhard, H. B. 2018.
Healthcare market segmentation and data mining: A
systematic review. Health Marketing Quarterly, 35(3),
186-208.
Taye, M. M. 2023. Understanding of machine learning with
deep learning: architectures, workflow, applications
and future directions. Computers, 12(5), 91.
Turkmen, B. 2022. Customer Segmentation with machine
learning for online retail industry. The European
Journal of Social & Behavioural Sciences.
Yadegaridehkordi, E., Nilashi, M., Nasir, M. H. N. B. M.,
Momtazi, S., Samad, S., Supriyanto, E., & Ghabban, F.
2021. Customers segmentation in eco-friendly hotels
using multi-criteria and machine learning techniques.
Technology in Society, 65, 101528.
Yuping, Z., Jílková, P., Guanyu, C., & Weisl, D. 2020. New
methods of customer segmentation and individual
credit evaluation based on machine learning. In “New
Silk Road: Business Cooperation and Prospective of
Economic Development” (NSRBCPED 2019) (pp. 925-
931) Atlantis Press.