Data-driven strategies for digital native market segmentation using clustering
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 emergen...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | International Journal of Cognitive Computing in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307424000135 |
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| author | Md Ashraf Uddin Md. Alamin Talukder Md. Redwan Ahmed Ansam Khraisat Ammar Alazab Md. Manowarul Islam Sunil Aryal Ferdaus Anam Jibon |
| author_facet | Md Ashraf Uddin Md. Alamin Talukder Md. Redwan Ahmed Ansam Khraisat Ammar Alazab Md. Manowarul Islam Sunil Aryal Ferdaus Anam Jibon |
| author_sort | Md Ashraf Uddin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d915629bfa344e1bb23f8601dba3ca5f |
| institution | OA Journals |
| issn | 2666-3074 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj-art-d915629bfa344e1bb23f8601dba3ca5f2025-08-20T02:13:02ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742024-01-01517819110.1016/j.ijcce.2024.04.002Data-driven strategies for digital native market segmentation using clusteringMd Ashraf Uddin0Md. Alamin Talukder1Md. Redwan Ahmed2Ansam Khraisat3Ammar Alazab4Md. Manowarul Islam5Sunil Aryal6Ferdaus Anam Jibon7School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia; Department of Information Technology, Crown Institute of Higher Education, 116 Pacific Highway, North Sydney, NSW, 2060, AustraliaDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh; Corresponding author.Department of Computer Science and Engineering, Jagannath University, Dhaka, BangladeshSchool of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, AustraliaCentre for Artificial Intelligence and Optimization, Torrens University, AustraliaDepartment of Computer Science and Engineering, Jagannath University, Dhaka, BangladeshSchool of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, AustraliaDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, BangladeshThe 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.http://www.sciencedirect.com/science/article/pii/S2666307424000135Digital nativeSocial networking sitesMarket segmentationClusteringDigital business tools |
| spellingShingle | Md Ashraf Uddin Md. Alamin Talukder Md. Redwan Ahmed Ansam Khraisat Ammar Alazab Md. Manowarul Islam Sunil Aryal Ferdaus Anam Jibon Data-driven strategies for digital native market segmentation using clustering International Journal of Cognitive Computing in Engineering Digital native Social networking sites Market segmentation Clustering Digital business tools |
| title | Data-driven strategies for digital native market segmentation using clustering |
| title_full | Data-driven strategies for digital native market segmentation using clustering |
| title_fullStr | Data-driven strategies for digital native market segmentation using clustering |
| title_full_unstemmed | Data-driven strategies for digital native market segmentation using clustering |
| title_short | Data-driven strategies for digital native market segmentation using clustering |
| title_sort | data driven strategies for digital native market segmentation using clustering |
| topic | Digital native Social networking sites Market segmentation Clustering Digital business tools |
| url | http://www.sciencedirect.com/science/article/pii/S2666307424000135 |
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