Case analysis of data mining technology in customer relationship management

Huimin Xing

doi:10.54691/bcpbm.v25i.1689

Authors

Huimin Xing

DOI:

https://doi.org/10.54691/bcpbm.v25i.1689

Keywords:

Data mining, Customer management, Loyalty

Abstract

With the development of digital economy, customer relationship management has been paid more and more attention in life. Many enterprises not only face changeable market demand, but also face more fierce competition between peers, so the survival and development of enterprises need more stable customer groups. As customers will produce a large amount of valuable data in the management process, which can be used to tap the potential value of customers. It may bring more business opportunities, and to deepen the use of information can also detect the potential needs of many customers. Based on the data of customer transaction behavior, this study uses the gradient improvement algorithm to mine the customer loyalty rules and improve the customer management level.

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References

Jun Wu, Li Shi, Wen-Pin Lin, Sang-Bing Tsai, Yuanyuan Li, Liping Yang, and Guangshu Xu. “An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm”.

Ma Qian. “Machine learning-based e-commerce platforms repeat purchase customer forecasts”. Master of Engineering (professional degree). Lanzhou University. Issue # 03, 2018. F416.61; TP181.

M. S. Chen, J.Han, and P. S. Yu .“Data mining: an overview from a database perspective,” IEEE Transactions on Knowledge and Data Engineering, vol.8, no.6, pp.866–883, 1996.

Alex Szalay, Ani R. Thakar, Jim Gray. “The sqlLoader Data-Loading Pipeline”. Computing in Science & Engineering (IF 2.152) Pub Date: 2008-01-01 DOI: 10.1109/mcse.2008.18.

Fan Ying. “Technical Analysis of Customer Forecasting Based on Python Data Mining”. Information Record Material, September 2021, Volume 22, Issue 9, DOI:10.16009/j.cnki.cn13-1295/tq.2021.09.081

Long bingting. “Customer Value Based RFM Model”. Journal of Xichang University (Natural Science Edition),Volume 36, Issue 1, March 2022,DOI: 10.16104 /j.issn.1673-1891.2022.01.010.

Xu Xiangbin, Wang Huaqiang, Tu Huan, and et al. “E-commerce customer segmentation based on improved RFM models [J]”. Computer Application, 2012,32 (5): 1439-1442.

Xiong LAN, Gao Bing. “Customer Segment Research Based on the RFM Multi-Level Customer Value Model [J]”. Business Economy Research, 2017 (5): 55-57.

Rao Shuzhen. “Analysis of several data migration methods of Oracle database”, Volume C omputer Knowledge and Technology 14, Issue 21 (July 2018).