Sustainable digital marketing under big data: an AI random forest model approach

Jin, Keyan, Zhong, Ziqi ORCID: 0000-0002-3919-9999 and Zhao, Elena Yifei (2024) Sustainable digital marketing under big data: an AI random forest model approach. IEEE Transactions on Engineering Management, 71. 3566 - 3579. ISSN 0018-9391

Text (Sustainable Digital Marketing under Big Data An AI Random Forest Model Approach) - Accepted Version Download (894kB)

• Author
• Scopus publication

Abstract

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies,

Item Type:	Article
Official URL:	https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?pu...
Additional Information:	© 2024 IEEE
Divisions:	Management
Subjects:	H Social Sciences > HF Commerce H Social Sciences > HM Sociology
Date Deposited:	18 Jan 2024 10:21
Last Modified:	01 Feb 2024 08:18
URI:	http://eprints.lse.ac.uk/id/eprint/121402

Actions (login required)

View Item