The relevance of lead prioritization: a B2B lead scoring model based on machine learning

Area of Science:

• Business Analytics
• Machine Learning
• Consumer Behavior Theory

Background:

• Business-to-business (B2B) companies struggle with efficient lead identification, qualification, and prioritization.
• Effective lead prioritization is crucial for resource allocation, sales focus, and maximizing B2B digital marketing ROI.

Purpose of the Study:

• To develop and evaluate a data analytics and machine learning-based lead scoring model for a B2B software company.
• To apply consumer theory principles to enhance lead scoring accuracy and effectiveness.

Main Methods:

• Utilized real lead data from January 2020 to April 2024 from a CRM system.
• Analyzed and evaluated fifteen classification algorithms, including Gradient Boosting Classifier.
• Performed feature importance analysis to identify key predictive factors like "source" and "lead status".

Main Results:

• The Gradient Boosting Classifier demonstrated superior performance in accuracy and ROC AUC compared to other algorithms.
• Identified "source" and "lead status" as critical features for improving conversion prediction accuracy.
• The developed model significantly enhanced the identification of high-quality leads over traditional methods.

Conclusions:

• The study validates the application of consumer behavior theory and machine learning in B2B lead scoring.
• The developed model offers a significant improvement in lead quality identification for B2B organizations.
• Emphasizes the collaborative role of marketers and data scientists in optimizing lead scoring for marketing and sales revenue performance.