Affiliation:
1. Zibo Vocational Institute, Zibo 255000 China
Abstract
Through big data mining, enterprises can deeply understand the consumer preferences, behavior characteristics, market demand and other derived data of customers, so as to provide the basis for formulating accurate marketing strategies. Therefore, this paper proposes a marketing management big date mining method based on deep trust network model. This method first preprocesses the big data of marketing management, including data cleaning, data integration, data transformation and data reduction, and then establishes a big data mining model by using deep trust network to realize the research on the classification of marketing management data. Experimental results show that the proposed method has 99.08% accuracy, the capture rate reaches 88.11%, and the harmonic average between the accuracy and the recall rate is 89.27%, allowing for accurate marketing strategies.
Publisher
North Atlantic University Union (NAUN)
Subject
Electrical and Electronic Engineering,Signal Processing
Reference25 articles.
1. A. Hemonnet-Goujot, D. Manceau, C. Abecassis-Moedas, “Drivers and pathways of NPD success in the marketing-external design relationship,” Journal of Product Innovation Management, vol. 36, no. 2, pp. 196-223, 2019.
2. L. Peng, Z. Q. Hua, S. L. Dang, et al., “Power marketing big data processing method for ubiquitous Internet of things,” Journal of Electrical Engineering, vol. 15, no. 1, pp. 8, 2020.
3. D. J. Yao, Y. Chu, “Research on multi-dimensional value recognition and precision marketing input-output model of power users based on big data analysis,” Power Systems and Big Data, vol. 23, no. 6, pp. 63-68, 2020.
4. P. Lagree, O. Cappe, B. Cautis, et al., “Algorithms for online influencer marketing,” ACM Transactions on Knowledge Discovery from Data, vol. 13, no. 1, pp. 3.1-3.30, 2019.
5. M. C. Zyurt, A. Karadogan, “A new model based on artificial neural networks and game theory for the selection of underground mining method,” Journal of Mining Science, vol. 56, no. 1, pp. 66-78, 2020.
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