Affiliation:
1. Department of Economics and Management, Sichuan Post and Telecommunication College, No.536 Jingkang Road, Jinjiang District, Chengdu, Sichuan 610067, China
Abstract
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.
Funder
Teaching Steering Committee of Industrial and Information-based Vocational Education
Publisher
Fuji Technology Press Ltd.
Reference28 articles.
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. https://doi.org/10.1504/IJBIR.2019.102718
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. https://doi.org/10.32629/memf.v3i2.779
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. 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. https://doi.org/10.9790/0661-2401014349
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. https://doi.org/10.1007/s10479-020-03606-8