Title: User consumption behaviour prediction method in the context of social media marketing

Authors: Gang Chen; Yixi Zhang

Addresses: School of Business, Chongqing City Management College, Chongqing, 401331, China ' School of Civil Affairs and Social Governance, Chongqing City Management College, Chongqing, 401331, China

Abstract: In order to improve the accuracy of user consumption behaviour prediction, mining accuracy of behaviour data, and reduce the long prediction time, this paper designs a user consumption behaviour prediction method in the context of social media marketing. Firstly, research on social media marketing platforms is conducted, and then the impact model between social media marketing and user consumption behaviour is analysed, in order to analyse the impact of social media marketing on user consumption behaviour. Then the user consumption behaviour data is obtained and deeply mined. With the help of integrated weak learning classifier, the behaviour data is classified, and the information entropy of different behaviour data is calculated. Finally, a prediction model based on random forest is constructed to achieve the final prediction analysis. The test results show that the proposed prediction method reduces the prediction error, has high mining accuracy and short prediction time cost for behavioural data, and has good application value.

Keywords: social media marketing; consumer behaviour; prediction method; impact model; weak learning classifier; random forest.

DOI: 10.1504/IJNVO.2023.135949

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.257 - 271

Received: 20 Mar 2023
Accepted: 12 Jun 2023
Published online: 10 Jan 2024 *

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