Year: 2025
Type: article
Abstract: This paper examines the potential of Large Language Models (LLMs) in revolutionizing lead qualification processes within sales and marketing. We critically analyze the limitations of traditional methods, such as dynamic branching and decision trees, during the lead qualification phase. To address these challenges, we propose a novel approach leveraging LLMs. Two methodologies are presented: a single-phase approach using one comprehensive prompt and a multi-phase approach employing discrete prompts for different stages of lead qualification. The paper highlights the advantages, limitations, and potential business implementation of these LLM-driven approaches, along with ethical considerations, demonstrating their flexibility, maintenance requirements, and accuracy in lead qualification.
Authors: Shantanu Sharma, Naveed Afzal
Institution: Takeda (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409461560