Lead Scoring Algorithm
A lead scoring algorithm is a programmatic system assigning conversion-likelihood scores to MCA leads based on firmographic, behavioral, and intent signals — enabling priority routing of high-score leads to top reps and automated suppression of low-score leads from active dialing.
Why This Matters
Lead scoring algorithms range from simple rule-based systems (revenue threshold + time-in-business + UCC presence) to ML models trained on historical funded-deal patterns (XGBoost, random forest, neural networks consuming hundreds of features). The economic value is dialer efficiency: routing the top 20% of scored leads to top reps captures 60%+ of total funding volume. Modern lead scoring incorporates behavioral signals (page views, email opens, document downloads), engagement velocity (time between actions), and source quality (vendor, channel, campaign attribution).
Frequently Asked Questions
Frequently Asked Questions
What features matter most in MCA lead scoring?
Top features: monthly revenue (gating), time-in-business (gating), industry SIC/NAICS (risk segmentation), recent UCC filing (intent signal), application completeness (commitment signal), and source vendor quality (historical conversion correlation).
Should I build or buy lead scoring infrastructure?
Build for scale advantage and proprietary data leverage if dialing 10K+ leads/month. Buy (HubSpot, Salesforce Einstein, third-party vendors) for sub-scale operations or to start. Most large funders eventually build proprietary models trained on their funded-deal history.