Volume 16, Number 2

Revolutionizing Lead Qualification: The Power of LLMs Over Traditional Methods

  Authors

Shantanu Sharma ¹ and Naveed Afzal ², ¹ ZoomInfo, USA, ²Takeda Pharmaceuticals, USA

  Abstract

This paper examines the potential of Large Language Models (LLMs) in revolutionizing lead qual- ification 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 addressthese 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 theirflexibility, maintenance requirements, and accuracy in lead qualification.

  Keywords

Large Language Model, Chatbots, Dynamic Branching, Lead Qualification, Sales & Marketing

Full Text    Volume 16

 

 

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