The Computational Marketer: A Comprehensive Review of Machine Learning Applications in Digital Strategy

Abstract

The integration of the so-called Machine Learning (ML) and Artificial Intelligence (AI) has fundamentally restructured the industry of digital marketing, turning it into a science of prescriptive and real-time optimization instead of the science of descriptive analytics. 2022-2025 studies have highlighted the maturation of AI applications in the marketing spectrum between the developed predictive customer intelligence, the algorithmic creative optimization, and the advanced programmatic media buying. Empirically, organizations have reported selling improvement in ML sales, Return on Investment (ROI) of 10-20%, with high levels of operational returns of up to 25% churn of customers.

However, ethical governance is necessary in the pursuit of performance. It is the complex algorithms that result in high returns that need to be put on the focus of Explainable AI (XAI) to contain the imminent biases and ensure privacy of the data. The review compiles the available academic sources, defining the specific algorithms (i.e., XGBoost, Reinforcement Learning, Convolutional Neural Networks) that allow gaining competitive advantages and points out the most significant finding that the effectiveness of technology is intrinsically linked with the perceived transparency and trust and makes the ethical compliance not only a regulatory consideration but a competitive condition of high consumer acceptance and financial sustainability.