Lead Scoring Through ML-Based Propensity Modeling
Posted by | Fuld & Company
An enterprise cloud solution provider wanted to increase sales conversions by better prioritizing leads.
To configure, test, and deploy an ML-based propensity model that would prioritize generated leads based on propensity and help drive conversions for the sales team.
- Understood the current scoring scheme and its limitations.
- Collected data for all leads from CRM, web portal, 3P lead partners, and 3P company databases.
- Trained and tested ML classification models on historical customer win/ loss data.
- Tested and used the best-performing algorithm among logistic classification, Random Forest, ensemble classification, and neural networks.
- Clustered leads and created targetable sales plays for the sales and marketing teams
Algorithms used include: classification models such as logistic classification, Random Forest, ensemble classification, and neural networks.
- Visualization of classification outcomes and analyses on a Tableau dashboard
- Integrated classification model with client CRM to identify leads with high probability of conversion
- Increased conversion rate by 11%