BUSINESS INFORM JOURNAL.

УКР

ENG

Search:

REGISTRATION CERTIFICATE

KV #19905-9705 PR dated 02.04.2013.

FOUNDERS

RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE)

According to the decision No. 802 of the National Council of Television and Radio Broadcasting of Ukraine dated 14.03.2024, is registered as a subject in the field of print media.
ID R30-03156

PUBLISHER

Liburkina L. M.

SITE SECTIONS

Data Science Methods: Analyzing Approaches to Customer Segmentation
Andrusyk Y. V., Kaganovskyi O. S.

Andrusyk, Yevhenii V., and Kaganovskyi, Oleksandr S. (2025) “Data Science Methods: Analyzing Approaches to Customer Segmentation.” Business Inform 1:497–503.
https://doi.org/10.32983/2222-4459-2025-1-497-503

Section: Management and Marketing

Article is written in Ukrainian
Downloads/views: 0

Download article (pdf) -

UDC 33.330.4

Abstract:
The article addresses the issue of enhancing the efficiency of marketing strategies in the context of the modern dynamic market environment, characterized by a high level of competition, information overload, and rapid changes in consumer preferences. The analysis conducted revealed that traditional segmentation methods based on demographic, geographic, and socioeconomic characteristics fail to account for individual needs, preferences, and behavioral traits of consumers, which diminishes the efficiency of marketing campaigns. It is noted that in these conditions, innovative alternative approaches based on the use of unstructured data, dynamic segmentation, and psychographic factors come to the forefront. The implementation of alternative approaches is impossible without the application of Data Science methods such as k-means, hierarchical clustering, and DBSCAN, which allow for the detection of hidden patterns in customer behavior and the formation of more accurate segments. The advantages and disadvantages of clustering methods during the implementation of alternative approaches to customer segmentation have been identified. It is specified that the choice of segmentation method should be based on a comprehensive analysis in the context of a specific task (selective approach) or hybridization, which allows for enhancing the advantages and mitigating the disadvantages of each method. It is underscored that selection or hybridization when working with unstructured data requires the transformation of data (text, audio, media) into numerical form. The combination of K-means, hierarchical clustering, or DBSCAN, along with other methods such as keyword extraction, topic modeling, word vector representations, natural language processing (NLP), or computer vision, will assist in solving this task. For dynamic segmentation, mini-batch K-means, which is a modification of the K-means algorithm used for clustering large datasets, can be employed. This significantly accelerates the clustering process, especially when dealing with substantial amounts of information. It is emphasized that psychographic factors likely hold the greatest significance, as they more deeply reflect the values and motivations of consumers. Hierarchical clustering allows for the visualization of groups of different psychographic factors, but the interpretation of such associations will require additional analysis using AI. The most appropriate method for segmenting clients based on psychographic factors is DBSCAN. This method allows for the identification of groups with similar profiles, even if they have complex shapes, as well as clusters with nonlinear boundaries. It is underlined that further research should be directed towards the development of hybrid models that combine various clustering algorithms with alternative approaches to enhance the effectiveness of the company’s marketing strategy.

Keywords: marketing activities, dynamic segmentation, psychographic factors, Data Science, k-means method, mini-batch K-means, hierarchical clustering, DBSCAN.

Tabl.: 2. Bibl.: 10.

Andrusyk Yevhenii V. – Postgraduate Student, Department of Economic Cybernetics and Systems Analysis, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]
Kaganovskyi Oleksandr S. – PhD, Postgraduate Student, Department of Management and Business, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]

List of references in article

Sanket, Lodha et al. “Customer segmentation using machine learning“. Shodhak: A Journal of Historical Research. 2023. https://www.researchgate.net/publication/376396210_CUSTOMER_SEGMENTATION_USING_MACHINE_LEARNING
Mudunuri, Varma et al. “Use of Big Data in the Process of Customer Segmentation in the Retail Sector“. Technoarete Transactions on Advances in Data Science and Analytics, vol. 1, no. 2 (2022): 7-12. DOI: https://doi.org/10.36647/TTADSA/01.02.A002
Zatonatska, T., and Fareniuk, Ya. “Vykorystannia Data Science tekhnolohii v E-komertsii: uspishni keisy“ [Using Data Science Technologies in E-commerce: Successful Cases]. Data Science ta informatsiino-analitychni systemy: zastosuvannia v ekonomitsi ta finansakh. 2024. https://goo.su/5YyGA
Qian, Zhou, and Bo, Sun. “Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem“. Data and Information Management, vol. 8, no. 3 (2024): 1-12. DOI: https://doi.org/10.1016/j.dim.2023.100064
Zaitsev, D. et al. “Ohliad zasobiv efektyvnoi sehmentatsii zobrazhen z vykorystanniam metodiv klasteryzatsii danykh“ [Overview of Effective Image Segmentation Tools Using Data Clustering Methods]. Systemy upravlinnia, navihatsii ta zviazku, no. 1(75) (2024): 77-81. DOI: https://doi.org/10.26906/SUNZ.2024.1.077
Dingsheng, Deng. “Application of DBSCAN Algorithm in Data Sampling“. J. Phys.: Conf. Ser. 1617 012088. https://stats.iop.org/article/10.1088/1742-6596/1617/1/012088
Perevozova, I. V., Zemliakov, I. S., and Shaiban, V. M. “Alhorytmy personalizatsii kontentu u web-marketynhu yak chynnyk pidvyshchennia konversii internet-prodazhiv“ [Content Personalization Algorithms in Web Marketing as a Factor in Increasing Online Sales Conversion]. Akademichni vizii. 2023. https://www.academy-vision.org/index.php/av/article/view/1340
Stamat, V. M., and Skoruk, A. Yu. “Sehmentatsiia tsilyovoi audytorii yak vazhlyvyi etap marketynhu na rynku hotelno-restorannoho biznesu“ [Target Audience Segmentation as an Important Stage of Marketing in the Hotel and Restaurant Business Market]. Modern Economics, no. 35 (2022): 112-117. DOI: https://doi.org/10.31521/modecon.V35(2022)-17
Ashraf, Uddin et al. “Data-driven strategies for digital native market segmentation using clustering“. International Journal of Cognitive Computing in Engineering, vol. 5 (2024): 178-191. DOI: https://doi.org/10.1016/j.ijcce.2024.04.002
Blomker, J., and Carmen-Maria, A. “Psychographic segmentation of multichannel customers: investigating the influence of individual differences on channel choice and switching behavior“. Journal of Retailing and Consumer Services, vol. 79 (2024): 1-17. DOI: https://doi.org/10.1016/j.jretconser.2024.103806

FOR AUTHORS

License Contract

Conditions of Publication

Article Requirements

Regulations on Peer-Reviewing

Publication Contract

Current Issue

Frequently asked questions

INFORMATION

The Plan of Scientific Conferences

OUR PARTNERS

Journal «The Problems of Economy»

The site and its metadata are licensed under CC BY-SA.

Write to webmaster