领先优先级的相关性:基于机器学习的B2B领先评分模型
Laura González-Flores1, Jessica Rubiano-Moreno2, Guillermo Sosa-Gómez1
1Universidad Panamericana, Facultad de Ciencias Económicas y Empresariales, Zapopan, Jalisco, Mexico.
Frontiers in artificial intelligence
|March 24, 2025
概括
一个新的机器学习模型通过准确识别高质量的潜在客户,显著改善了企业对企业 (B2B) 领导得分. 这种数据驱动的方法优化了资源配置,提高了营销和销售业绩.
科学领域:
- 业务分析 业务分析
- 机器学习 机器学习
- 消费者行为理论 消费者行为理论
背景情况:
- 企业对企业 (B2B) 公司在有效的领先识别,资格和优先级方面扎.
- 有效的领先优先级对资源配置,销售重点以及最大限度地提高B2B数字营销ROI至关重要.
研究的目的:
- 为B2B软件公司开发和评估基于数据分析和机器学习的领先评分模型.
- 应用消费者理论原则来提高领先评分的准确性和有效性.
主要方法:
- 从CRM系统中利用了2020年1月至2024年4月的真实头数据.
- 分析和评估了十五个分类算法,包括梯度提升分类器.
- 进行特征重要性分析,以确定关键的预测因素,如"来源"和"领先状态".
主要成果:
- 与其他算法相比,梯度提升分类器在准确性和ROC AUC方面表现出卓越的性能.
- 确定了"来源"和"领先状态"作为改善转化预测准确性的关键特征.
- 开发的模型显著提高了对传统方法的高质量潜在客户的识别.
结论:
- 该研究验证了消费者行为理论和机器学习在B2B领先评分中的应用.
- 开发的模型为B2B组织的质量识别提供了显著的改进.
- 强调营销人员和数据科学家在优化营销和销售收入表现的领先评分方面的协作作用.
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