Threshold and Tier Design

A score without a threshold is a number without meaning. Thresholds convert continuous scores into actionable tiers that drive routing, prioritization, and workflow automation. The MQL threshold defines when a lead becomes "marketing qualified" and enters the pipeline. The SQL threshold defines when marketing hands the lead to sales. The thresholds must be set empirically — based on the score values that historically correlate with conversion — not arbitrarily.

1. Empirical Threshold Setting Score all historical closed-won and closed-lost deals. Plot conversion rate by score range. Find the score value above which conversion rates are meaningfully higher. That value is your MQL threshold. The score above which conversion rates justify direct sales engagement is your SQL threshold. Both are derived from data, not policy.
2. Tier Design Create 3-4 tiers with distinct actions. Tier 1 (80+): Immediate sales outreach — call within 1 hour. Tier 2 (60-79): Sales outreach within 24 hours with personalized context. Tier 3 (40-59): Marketing nurture sequence with sales notification. Tier 4 (below 40): Automated nurture only. Each tier has a defined action and a defined SLA.
3. Threshold Recalibration Recalibrate thresholds quarterly. As the scoring model evolves and the lead population changes, the distribution of scores shifts. A threshold set at 65 six months ago might now let through too many unqualified leads (if engagement inflation raised scores) or block qualified ones (if the lead population shifted). Recalibration keeps the tiers aligned with actual conversion behavior.