Machine Learning Lead Scoring Model
A machine learning scoring model that identifies high-potential customers from customer and open-source data.
Business issue
Why I was brought into the project
Commercial teams needed a data-driven way to identify high-potential customers and prioritize lead generation efforts.
Context
The environment around the project
Business teams wanted to identify high-potential customers using data.
Functional environment
The functional context covered lead identification, customer scoring and business prioritization.
Technical environment
The technical environment combined machine learning, Dataiku, Python, CI/CD pipelines and Power BI.
Challenges
The model had to combine customer data and open-source data, remain maintainable over time and expose results in a usable business dashboard.
Solution
My contribution and its impact
My contribution to the project
I built a lead scoring model, industrialized it with CI/CD and regular retraining, then exposed the results in an interactive Power BI dashboard.
- Lead scoring machine learning model
- CI/CD and retraining pipeline
- Power BI scoring dashboard
- Business-ready scoring outputs
Impact
The project turned scattered customer and external signals into actionable scores that helped business teams focus on the most promising opportunities.
- Better prioritization of commercial leads
- Dynamic scoring refreshed over time
- Improved usability through dashboard visualization
Impact metrics
Approach
How I structured the work
- Collect and prepare customer and open-source data.
- Build and evaluate a machine learning scoring model.
- Industrialize retraining and expose outputs through Power BI.
Takeaways
What I learned from this project
- A scoring model is useful only when business teams can understand and consume the results.
- Industrialization and retraining are essential for models used in ongoing commercial processes.
- External data can improve signal quality when it is integrated carefully.