Definition
A lead scoring rubric is a weighted, explicit list of criteria (typically 8-15 lines) that assigns a numerical score to inbound leads based on title fit, industry match, company size, intent signal, and fit notes — auditable as code, version-controlled, and applied consistently regardless of the LLM model behind it.
In Depth
An r/n8n post in April 2026 documented a 220-person SaaS doing 120 leads/week where the scoring rubric was 12 lines and that was the whole product. The pattern beats most 'AI lead scoring' SaaS for teams that own their CRM data because it's auditable: every score has a reason rooted in the rubric, not a black-box ML model. The rubric typically lives in the LLM system prompt and is paired with a Scavio enrichment step that fills what the form doesn't capture (firmographic, recent news, role context). Per-lead scoring cost runs ~$0.01-0.04. The rubric pattern wins below 5K closed/lost leads where labeled-data ML overfits or underfits.
Example Usage
Title fit 30 + industry match 25 + company size 20 + intent signal 15 + fit notes 10 = 100. The LLM applies the rubric per lead, returns { score, reason }. Reason is logged in CRM, auditable any time.
Platforms
Lead Scoring Rubric is relevant across the following platforms, all accessible through Scavio's unified API:
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