| 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, |