Lead Scoring

---|-------|--------| | Qualified — Hot | 85-100 | Immediate sales outreach. High urgency, strong fit. | | Qualified — Warm | 75-84 | Active pursuit within 24 hours. Good fit, moderate urgency. | | Borderline | 50-74 | Requires human review. Qualified with caveats — flag specific concerns. | | Near Miss | 30-49 | Nurture sequence or referral opportunity. Not ready for sales. | | Disqualified | 0-29 | Does not fit ICP. Includes competitor employees. Polite decline. |

Handling Unknown Data

Score unknown dimensions at 30 points (out of 100 for that dimension). This acknowledges data absence without automatically rejecting leads. A lead missing company size data is not the same as a lead with the wrong company size. Flag unknowns for enrichment rather than penalizing them.

Inbound Intent Premium

Prospects who initiate contact demonstrate genuine interest. For borderline cases (scores 50-74), inbound signals should tip the scoring decision toward qualification. A borderline lead who requested a demo is a better prospect than a slightly-above-threshold lead who has never engaged.

Pipeline Overlap Routing

Before scoring, check for overlaps and route accordingly:

• Existing customer — Route to account management for upsell/expansion conversation
• Active deal in pipeline — Flag for the assigned sales rep to coordinate, do not create a duplicate
• Prior contact with no deal — Note history and score normally, but include context for the sales rep
• Competitor employee — Auto-disqualify and log for competitive intelligence

Multi-Dimensional Scoring

Company evaluation — Score against: company size, industry vertical, company stage/funding, geography, and use case fit. Weight dimensions based on which most predict closed-won deals in your data.

Person assessment — Score against: job title, seniority level, department alignment, and decision-making authority. A Director of Engineering at a perfect-fit company scores higher than a junior developer at the same company.

Use case alignment — Map the lead's stated or inferred needs to specific product capabilities. Strong alignment on the core use case matters more than broad but shallow fit.

Dual-Threshold MQL Definition

An MQL requires BOTH fit and engagement. Neither alone is sufficient.

• Minimum fit score: 30 points (must have basic ICP match)
• Minimum engagement score: 20 points (must show some intent)
• Combined minimum: 60 points

A perfect-fit company that never engages is not an MQL. A student downloading every whitepaper is not an MQL. The dual-threshold prevents both failure modes.

Maintaining and Iterating

• Recalibrate quarterly. Pull closed-won data and check if the model correctly predicted winners.
• Watch for score inflation. If 80% of leads become MQLs, the threshold is too low.
• Track MQL-to-SQL acceptance rate. If sales rejects more than 30% of MQLs, adjust the model.