MaRGen: Multi-Agent LLM Approach for Self-Directed Market Research and Analysis
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912516425121792 |
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| author | Koshkin, Roman Dai, Pengyu Fujikawa, Nozomi Togami, Masahito Visentini-Scarzanella, Marco |
| author_facet | Koshkin, Roman Dai, Pengyu Fujikawa, Nozomi Togami, Masahito Visentini-Scarzanella, Marco |
| contents | We present an autonomous framework that leverages Large Language Models (LLMs) to automate end-to-end business analysis and market report generation. At its core, the system employs specialized agents - Researcher, Reviewer, Writer, and Retriever - that collaborate to analyze data and produce comprehensive reports. These agents learn from real professional consultants' presentation materials at Amazon through in-context learning to replicate professional analytical methodologies. The framework executes a multi-step process: querying databases, analyzing data, generating insights, creating visualizations, and composing market reports. We also introduce a novel LLM-based evaluation system for assessing report quality, which shows alignment with expert human evaluations. Building on these evaluations, we implement an iterative improvement mechanism that optimizes report quality through automated review cycles. Experimental results show that report quality can be improved by both automated review cycles and consultants' unstructured knowledge. In experimental validation, our framework generates detailed 6-page reports in 7 minutes at a cost of approximately \$1. Our work could be an important step to automatically create affordable market insights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MaRGen: Multi-Agent LLM Approach for Self-Directed Market Research and Analysis Koshkin, Roman Dai, Pengyu Fujikawa, Nozomi Togami, Masahito Visentini-Scarzanella, Marco Computation and Language Information Retrieval We present an autonomous framework that leverages Large Language Models (LLMs) to automate end-to-end business analysis and market report generation. At its core, the system employs specialized agents - Researcher, Reviewer, Writer, and Retriever - that collaborate to analyze data and produce comprehensive reports. These agents learn from real professional consultants' presentation materials at Amazon through in-context learning to replicate professional analytical methodologies. The framework executes a multi-step process: querying databases, analyzing data, generating insights, creating visualizations, and composing market reports. We also introduce a novel LLM-based evaluation system for assessing report quality, which shows alignment with expert human evaluations. Building on these evaluations, we implement an iterative improvement mechanism that optimizes report quality through automated review cycles. Experimental results show that report quality can be improved by both automated review cycles and consultants' unstructured knowledge. In experimental validation, our framework generates detailed 6-page reports in 7 minutes at a cost of approximately \$1. Our work could be an important step to automatically create affordable market insights. |
| title | MaRGen: Multi-Agent LLM Approach for Self-Directed Market Research and Analysis |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2508.01370 |