This repository documents a lead scoring system designed to evaluate and prioritize inbound leads using data and behavioral signals.

The full implementation is private. This repo explains what the system does at a high level.

If you want to review the model logic or discuss real-world use cases, contact me directly.

For each lead, the system outputs:

• A score from 0 to 100
• A qualification level: High / Medium / Low
• The key signals that influenced the score

This helps sales and marketing teams focus attention on leads most likely to convert.

The model can work with a wide range of signals, including:

• User or company attributes
• Engagement and behavior signals
• Source and acquisition channel
• Custom business rules

Inputs are flexible and can be adapted to different pipelines.

The system is designed to stay simple, explainable, and easy to integrate.

• Clear and explainable scoring logic
• Handles messy, real-world data
• Easy to customize and extend
• Built for practical sales and marketing use

• Python
• Data processing logic
• Rule-based and weighted scoring

The full model and scoring logic are not public.

If you want a demo, walkthrough, or access to the implementation, reach out to discuss.