Lead Scoring: Best Leads to Best Closers
When you need this method
In your sales organization, leads are distributed by rotation or chance. The most valuable inquiries regularly land with reps who cannot convert them optimally and expensively generated demand fizzles. At the same time, new hires burn good leads they should really only be learning on.
Approach
- 1Set up lead scoring that reflects likelihood to close and potential value (company size, fit, signal strength, source).
- 2Define routing rules: high-value leads to demonstrably strong closers, low-scored leads to new hires for practice.
- 3Measure performance per rep and lead class so routing rests on data rather than seniority.
- 4Design compensation so top performance is visibly rewarded above average, the spread attracts further talent.
Typical application
A typical case: a B2B SaaS company with mixed inbound volume distributes demo requests strictly round-robin. Analysis by rep and lead class shows substantial differences: on large accounts, two experienced AEs convert markedly better than the rest of the team. The company introduces simple scoring by company size and fit and routes the top lead classes to exactly those two, while new hires practice on smaller inquiries. Expensively generated demand gets converted where the probability is highest.
Limits and counter-indications
Without solid data tracking per rep and lead class, the routing is arbitrariness with a label. Concentrating good leads on a few people creates key-person risk and can stall the rest of the team's development, training and progression paths must be designed alongside. In small teams with few leads per month, the machinery is hardly worth it.
How to measure impact
Conversion rate per lead class and rep, plus revenue per generated lead. If yield from the same lead volume rises after the routing change, the lever is working.
Related methods
Tools for this
Sources
- 1.González-Flores, Rubiano-Moreno, Sosa-Gómez: The relevance of lead prioritization: a B2B lead scoring model based on machine learning, Frontiers in Artificial Intelligence, 2025 (opens in a new tab) · Frontiers in Artificial Intelligence · 2025 · academic and scholarly literature · supports the underlying mechanismEstablishes the basis of the method: in a case study on real lead data from a B2B software vendor (January 2020 to April 2024), a gradient boosting model ranks inbound requests by close probability and outperforms the comparison models on accuracy and ROC AUC. Better use of sales resources is the stated purpose of that ranking, but the study does not measure it as an outcome.
- 2.Alex Hormozi: Sales Was Hard Until I Understood These 9 Concepts, YouTube, 2024 (opens in a new tab) · YouTube (Alex Hormozi) · 2024 · practitioner source · describes the methodCarries the routing rule itself, namely best requests to the strongest sellers and weak requests as practice material for new hires, plus the effect of visibly high top earnings on recruiting.
Origin: Hormozi · Adapted from: Alex Hormozi ($100M Offers/$100M Leads, YouTube-Langform)