JACIII Vol.28 No.3 pp. 541-551
doi: 10.20965/jaciii.2024.p0541

Research Paper:

Research on Customer Group Division and Precision Marketing Based on the DWKCN Algorithm

Yanhong Li

Department of Economics and Management, Sichuan Post and Telecommunication College
No.536 Jingkang Road, Jinjiang District, Chengdu, Sichuan 610067, China

Corresponding author

September 27, 2023
January 5, 2024
May 20, 2024
customer group segmentation, precision marketing, k-means, depth weighting, DWKCN

Classifying customers according to their characteristics can effectively meet the genuine needs of different customer groups. It also helps enterprises formulate reasonable marketing strategies and obtain considerable profits. Currently, there are many ways to classify customers. However, the procedures involved are complicated and cannot comprehensively and objectively reflect customer characteristics. Therefore, a customer group classification model is designed based on the deep cross network (DCN). The DCN algorithm can automatically learn simple data features, achieving data clustering. For the defects in this model, the deep weighted k-means clustering network (DWKCN) customer group classification method is constructed, improving the DCN algorithm. From the results, the algorithm has a high accuracy of 99.5%. Therefore, the proposed DWKCN algorithm can realize the customer group’s precise division and the marketing plan design, providing the references for different types of customers to formulate personalized needs.

Cite this article as:
Y. Li, “Research on Customer Group Division and Precision Marketing Based on the DWKCN Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 541-551, 2024.
Data files:
  1. [1] S. M. Basha and D. S. Rajput, “An innovative topic-based customer complaints sentiment classification system,” Int. J. of Business Innovation and Research, Vol.20, No.3, pp. 375-391, 2019.
  2. [2] S. Feng, “Enterprise marketing strategy and path under the background of double cycle,” Modern Economics & Management Forum, Vol.3, No.2, pp. 123-129, 2022.
  3. [3] Z. F. Ikatrinasari et al., “Development of digital marketing strategy in the education industry,” Int. Review of Management and Marketing, Vol.10, No.4, pp. 63-67, 2020.
  4. [4] N. S. Majdina, M. A. Soeleman, and C. Supriyanto, “Application of particle swarm optimization (PSO) to improve k-means accuracy in clustering eligible province to receive fish seed assistance in Java,” IOSR J. of Computer Engineering, Vol.24, No.1, pp. 43-49, 2022.
  5. [5] T. Vovan et al., “An automatic clustering for interval data using the genetic algorithm,” Annals of Operations Research, Vol.303, No.1, pp. 359-380, 2021.
  6. [6] M. Fahreza, “Marketing communication strategy to reopen a business venture,” J. of Socioeconomics and Development, Vol.2, No.2, pp. 116-124, 2019.
  7. [7] J. Choi et al., “Identification of additional jets in the ttbb events by using deep neural network,” J. of the Korean Physical Society, Vol.77, No.12, pp. 1100-1106, 2020.
  8. [8] D. Hong et al., “X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.167, pp. 12-23, 2020.
  9. [9] Q. Fan, H. Han, and S. Wu, “Credibility analysis of water environment complaint report based on deep cross domain network,” Applied Intelligence, Vol.52, No.7, pp. 8134-8146, 2022.
  10. [10] Z. Zhang and D. Li, “Hybrid cross deep network for domain adaptation and energy saving in visual internet of things,” IEEE Internet of Things J., Vol.6, No.4, pp. 6026-6033, 2019.
  11. [11] Y. Qiu and P. Hao, “Self-supervised deep subspace clustering network for faces in videos,” The Visual Computer, Vol.37, No.8, pp. 2253-2261, 2021.
  12. [12] H. Li et al., “TSDCN: Traffic safety state deep clustering network for real-time traffic crash-prediction,” IET Intelligent Transport Systems, Vol.15, No.1, pp. 132-146, 2021.
  13. [13] I. Sari, R. Kosasih, and D. Indarti, “Clustering and topic modeling of verdicts of narcotics cases using machine learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.6, pp. 1168-1174, 2023.
  14. [14] Y. Liu et al., “Multi-modal emotion classification in virtual reality using reinforced self-training,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.5, pp. 967-975, 2023.
  15. [15] D. Nie, P. Cappellari, and M. Roantree, “A methodology for classification and validation of customer datasets,” J. of Business & Industrial Marketing, Vol.36, No.5, pp. 821-833, 2021.
  16. [16] B. Noori, “Classification of customer reviews using machine learning algorithms,” Applied Artificial Intelligence, Vol.35, No.8, pp. 567-588, 2021.
  17. [17] L. D. C. S. Subhashini et al., “Mining and classifying customer reviews: A survey,” Artificial Intelligence Review, Vol.54, No.8, pp. 6343-6389, 2021.
  18. [18] D. Chen, D. Zhang, and A. Liu, “Intelligent Kano classification of product features based on customer reviews,” CIRP Annals, Vol.68, No.1, pp. 149-152, 2019.
  19. [19] W. Zhao, “Research on hotel customer relationship management system based on the classification algorithm,” Int. J. of Information Systems and Supply Chain Management, Vol.12, No.2, pp. 68-75, 2019.
  20. [20] R. Hirt, N. Kühl, and G. Satzger, “Cognitive computing for customer profiling: Meta classification for gender prediction,” Electronic Markets, Vol.29, No.1, pp. 93-106, 2019.
  21. [21] Y. Kanzawa, K. Atsuta, and G. Midorikawa, “Collaborative filtering with q-divergence-based relational fuzzy c-means clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.6, pp. 1070-1078, 2023.
  22. [22] W. Tian et al., “Partition of GB-InSAR deformation map based on dynamic time warping and k-means,” J. of Systems Engineering and Electronics, Vol.33, No.4, pp. 907-915, 2022.
  23. [23] S. Zhang, C. Bi, and M. Zhang, “Logistics service supply chain order allocation mixed K-means and Qos matching,” Procedia Computer Science, Vol.188, pp. 121-129, 2021.
  24. [24] S. Ma et al., “Study on an autonomous distribution system for smart parks based on parallel system theory against the background of Industry 5.0,” J. of King Saud University – Computer and Information Sciences, Vol.35, No.7, Article No.101608, 2023.
  25. [25] K. Sai Lekha and N. Deluxni, “Comparative analysis of customer behavior using K-means algorithm over convolutional neural network with increase inaccuracy of prediction,” ECS Trans., Vol.107, No.1, pp. 12459-12471, 2022.
  26. [26] P. M. Hasugian et al., “Best cluster optimization with combination of K-means algorithm and elbow method towards rice production status determination,” Int. J. of Artificial Intelligence Research, Vol.5, No.1, pp. 102-110, 2021.
  27. [27] W. Herulambang, E. Prasetyo, and A. Nur, “Clustering for searching type of house suitable for new consumer candidates using K-means clustering method (case study of PT. Maxima Jaya Perkasa),” J. of Electrical Engineering and Computer Sciences, Vol.4, No.2, pp. 723-728, 2019.
  28. [28] S. Rogić, L. Kašćelan, and M. P. Bach, “Customer response model in direct marketing: Solving the problem of unbalanced dataset with a balanced support vector machine,” J. of Theoretical and Applied Electronic Commerce Research, Vol.17, No.3, pp. 1003-1018, 2022.

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Last updated on Jun. 03, 2024