Large Language Model Virtual Assistants for International Business and Product Marketing
In order for emergent and incumbent businesses to establish themselves on the global market, each company needs to navigate the rules, regulations, and policies native to the regions outside of their current area of operation. This is an understandably time-consuming process, and it can be challenging to understand the explicit and implicit compliances of each country for targeted product marketing. The recent development of Artificial Intelligence (AI), Natural Language Processing (NLP) and Large Language Models (LLMs) presents a unique opportunity to solve these challenges and expedite the process in which a company expands their foreign influence. The proposed solution is to retrain a Pre-trained LLM model on datasets which encompass a region's social and legal characteristics, such as its culture, politics, language differences, and ethical norms. Business professionals can leverage the proposed LLM concepts to provide answers for customer behavior and expectation related questions, which can allow them to gain a comprehensive overview of various barriers that need to be considered when introducing a new product in a foreign region or to gain ideas when developing products to meet regional needs. The efficiency of an organization is improved as a result, since the initial steps to launching the successful release of a product can be automated using machine learning processes, and administrative work can be reduced across many levels. During the search for existing work, the author did not find any research paper that constructed and evaluated a specialized LLM, to publicly available models, that supported the demands related to international business and product marketing. The advantages, challenges, and process of implementation for a fine-tuned LLM solution is explored, with the intended users being international business and market analysts as well as product designers.
Authors
- Pavithra P. Shenoy
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/smart63812.2024.10882556
- Citations
- 1
- Primary Topic
- Collaboration in agile enterprises
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- Lloyd Library and Museum