The Impact of Machine Learning Algorithms on the Effectiveness of Marketing Texts

Citation: Andrejs Zenkevics, "The Impact of Machine Learning Algorithms on the Effectiveness of Marketing Texts", Universal Library of Business and Economics, Volume 02, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This article examines the impact of modern machine learning algorithms on the quality of marketing copy. Its importance lies in the growing role of text within digital marketing and the need for high-speed generation and testing of a large number of individualized messages. Falling cloud-computing costs and an open API ecosystem have made machine learning available to organizations of all sizes. At the same time, tightening regulations increase the demand for formal quality and security assessments of individualized content. This study shall perform a deep evaluation of generative transformers, segmentation tools (classification and clustering), and dynamic optimization algorithms (multi-armed bandits, Dynamic Creative Optimization) for the betterment of click-through rates and conversion rates via marketing communications. It will evaluate effectiveness at every stage, from text variant generation through automatic live testing to real-time traffic reallocation. The novelty of this study lies in its integration of three beneficial technological components into a single looping design: generative tools that provide text variety, split methods that sharply focus on key readers, and rapid fix systems that modify content instantly using user input. Ideas are presented alongside real-world outcomes from more than 16 diverse sources, including examples from JPMorgan Chase, Pizza Hut, Netflix, and Nespresso. Key findings indicate that using transformers and DCO can give a 4.5 times increase in click-through rate and an 8-12% rise in conversion. In return, contextual bandits can lead to a growth of up to 30% in deals without long A/B test cycles. At once, four major dangers are found: model illusions, algorithmic prejudice, data-privacy worries, and content ‘out-of-dateness’ because of overuse, each needing truth-check layers, fairness reviews, strong privacy safeguards, and ways to bring new creative ideas. The article will be useful for marketing professionals, data analysts, and AI solution developers.

Keywords: Machine Learning; Marketing Texts; Generative Transformers; Segmentation; Multi-Armed Bandits; Dynamic Creative Optimization; Personalization; ROI; A/B Testing.

https://doi.org/10.70315/uloap.ulbec.2025.0203008