دورية أكاديمية
Data-driven strategies for digital native market segmentation using clustering
| العنوان: | Data-driven strategies for digital native market segmentation using clustering |
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| المؤلفون: | Md Ashraf Uddin, Md. Alamin Talukder, Md. Redwan Ahmed, Ansam Khraisat, Ammar Alazab, Md. Manowarul Islam, Sunil Aryal, Ferdaus Anam Jibon |
| المصدر: | International Journal of Cognitive Computing in Engineering, Vol 5, Iss , Pp 178-191 (2024) |
| بيانات النشر: | KeAi Communications Co., Ltd., 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electronic computers. Computer science LCC:Science |
| مصطلحات موضوعية: | Digital native, Social networking sites, Market segmentation, Clustering, Digital business tools, Electronic computers. Computer science, QA75.5-76.95, Science |
| الوصف: | The rapid growth of internet users and social networking sites presents significant challenges for entrepreneurs and marketers. Understanding the evolving behavioral and psychological patterns across consumer demographics is crucial for adapting business models effectively. Particularly, the emergence of new firms targeting adolescents and future generations underscores the importance of comprehending online consumer behavior and communication dynamics. To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. Leveraging an open-access prototype dataset from social networking sites (SNS), our study employs a variety of clustering techniques, including Kmeans, MiniBatch Kmeans, AGNES, and Fuzzy C-means, to uncover hidden interests of teenage consumers from SNS data. Through rigorous evaluation of these clustering approaches by default parameters, we identify the optimal number of clusters and group consumers with similar tastes effectively. Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. Specifically, it achieves silhouette scores of 63.90% and 58.06% for 2 and 3 clusters, respectively, highlighting its effectiveness in segmenting the digital market. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2666-3074 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666307424000135; https://doaj.org/toc/2666-3074 |
| DOI: | 10.1016/j.ijcce.2024.04.002 |
| URL الوصول: | https://doaj.org/article/d915629bfa344e1bb23f8601dba3ca5f |
| رقم الأكسشن: | edsdoj.915629bfa344e1bb23f8601dba3ca5f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26663074 |
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| DOI: | 10.1016/j.ijcce.2024.04.002 |