# Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations

arXiv: https://arxiv.org/abs/2501.10685 · HTML: https://mlsanity.com/paper/2501.10685/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 70 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Understanding LLMs: A Comprehensive Overview from Training to Inference](https://mlsanity.com/paper/2401.02038/) (2024)
- [Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices](https://mlsanity.com/paper/2406.03777/) (2024)
- [Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media](https://mlsanity.com/paper/2405.04656/) (2024)
- [Large Language Models for Social Networks: Applications, Challenges, and Solutions](https://mlsanity.com/paper/2401.02575/) (2024)
- [Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce](https://mlsanity.com/paper/2410.12829/) (2024)
- [Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)](https://mlsanity.com/paper/2405.09770/) (2024)
- [SOMONITOR: Combining Explainable AI & Large Language Models for Marketing Analytics](https://mlsanity.com/paper/2407.13117/) (2024)
- [DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home](https://mlsanity.com/paper/2304.08027/) (2023)
- [Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs](https://mlsanity.com/paper/2405.06713/) (2024)

## Built on by (most cited first, 2 of 2)

- [Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility](https://mlsanity.com/paper/2604.19113/) (2026)
- [Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governance](https://mlsanity.com/paper/2511.14709/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation, in marketing management. In this paper, we explore the transformative potential of LLMs along with the current applications, future directions, and strategic recommendations for marketers. In particular, we focus on LLMs major business drivers such as personalization, real-time-interactive customer insights, and content automation, and how they enable customers and business outcomes. For instance, the ethical aspects of AI with respect to data privacy, transparency, and mitigation of bias are also covered, with the goal of promoting responsible use of the technology through best practices and the use of new technologies businesses can tap into the LLM potential, which help growth and stay one step ahead in the turmoil of digital marketing. This article is designed to give marketers the necessary guidance by using best industry practices to integrate these powerful LLMs into their marketing strategy and innovation without compromising on the ethos of their brand.