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Decision Tree Algorithm for Precision Marketing via Network Channel

Yulan Zheng
College of Economic and Management, Fuzhou University, Fuzhou, Fujian 350108, China
* Corresponding Author: Email:

Computer Systems Science and Engineering 2020, 35(4), 293-298. https://doi.org/10.32604/csse.2020.35.293

Abstract

With the development of e-commerce, more and more enterprises attach importance to precision marketing for network channels. This study adopted the decision tree algorithm in data mining to achieve precision marketing. Firstly, precision marketing and C4.5 decision tree algorithm were briefly introduced. Then e-commerce enterprise A was taken as an example. The data from January to June 2018 were collected. Four attributes including age, income, occupation and educational background were selected for calculation and decision tree was established to extract classification rules.The results showed that the consumers of the products of the company were high-income young and middle-aged people, middle-income young people, middle-income middle-aged and elderly people with a college degree or above, low-income middle-aged people with a college degree or above and low-income elderly people with a state-owned enterprise. After precision marketing to these customers, it was found that the monthly sales volume of the enterprise increased by 22.82% and the marketing cost decreased by 28.21%, which verified the effectiveness and application value of precision marketing and showed that the decision tree algorithm could provide enterprises with decision support in precision marketing.

Keywords

data mining, decision tree, precision marketing, C4.5

Cite This Article

Y. Zheng, "Decision tree algorithm for precision marketing via network channel," Computer Systems Science and Engineering, vol. 35, no.4, pp. 293–298, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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