Scholarlink

In the competitive landscape of B2B sales, identifying and prioritizing high-potential leads at trade shows remains a significant challenge. This paper presents an AI-powered agent designed to automate trade show lead matching, scoring, and enrichment, thereby optimizing the lead qualification process. The AI agent seamlessly integrates with Customer Relationship Management (CRM) systems, external data providers, and enrichment APIs to ensure accurate and high-quality lead identification. By leveraging advanced machine learning algorithms and public data sources, such as LinkedIn, Crunchbase, and Apollo.io, the system enhances lead prioritization through a structured scoring mechanism. This AI-driven approach reduces manual effort, enhances decision-making efficiency, and empowers sales teams to focus on the most promising prospects, ultimately improving conversion rates and business outcomes.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI Agent Design for Trade Show Lead Matching

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Victoria Martin del Campo Alcocer,
  • David Efrain Munoz Morales

摘要

In the competitive landscape of B2B sales, identifying and prioritizing high-potential leads at trade shows remains a significant challenge. This paper presents an AI-powered agent designed to automate trade show lead matching, scoring, and enrichment, thereby optimizing the lead qualification process. The AI agent seamlessly integrates with Customer Relationship Management (CRM) systems, external data providers, and enrichment APIs to ensure accurate and high-quality lead identification. By leveraging advanced machine learning algorithms and public data sources, such as LinkedIn, Crunchbase, and Apollo.io, the system enhances lead prioritization through a structured scoring mechanism. This AI-driven approach reduces manual effort, enhances decision-making efficiency, and empowers sales teams to focus on the most promising prospects, ultimately improving conversion rates and business outcomes.