Competitor analysis is essential in modern business due to the influence of industry rivals on strategic planning. It involves assessing multiple aspects and balancing trade-offs to make informed decisions. Recent Large Language Models (LLMs) have demonstrated impressive capabilities to reason about such trade-offs but grapple with inherent limitations such as a lack of knowledge about contemporary or future realities and an incomplete understanding of a market's competitive landscape. In this paper, we address this gap by incorporating business aspects into LLMs to enhance their understanding of a competitive market. Through quantitative and qualitative experiments, we illustrate how integrating such aspects consistently improves model performance, thereby enhancing analytical efficacy in competitor analysis.
@article{arxiv.2504.02984,
title = {Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis},
author = {Amir Hadifar and Christopher Ochs and Arjan Van Ewijk},
journal= {arXiv preprint arXiv:2504.02984},
year = {2025}
}