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
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 literatureBelegt den Kern der Methode: Auf echten Kundendaten eines Softwareanbieters lassen sich Anfragen nach Abschlusswahrscheinlichkeit ordnen, und diese Reihenfolge bestimmt, welchen Kontakten das Vertriebsteam zuerst Zeit widmet, was Abschlussquote und Ressourceneinsatz verbessert.
- 2.Alex Hormozi: Sales Was Hard Until I Understood These 9 Concepts, YouTube, 2024 (opens in a new tab) · YouTube (Alex Hormozi) · 2024 · practitioner sourceTrägt die Verteilungsregel selbst, also beste Anfragen an die stärksten Verkäufer und schwache Anfragen als Übungsmaterial für Neueinsteiger, sowie die Wirkung sichtbar hoher Spitzenverdienste auf die Anwerbung.
Origin: Hormozi · Adapted from: Alex Hormozi ($100M Offers/$100M Leads, YouTube-Langform)
Last reviewed: 2026-07-25 by Dr. Oliver Gausmann