/lead-scoring

name: lead-scoring
description: When a founder needs to qualify inbound leads, define their ICP, build a lead scoring model, set MQL criteria, or route prospects through pipeline stages. Activate when the user mentions lead scoring, ICP, MQL, SQL, lead qualification, inbound leads, or pipeline design.
related: [cold-outreach, sales-script]
reads: [startup-context]

Lead Scoring

When to Use

Activate when a founder needs to evaluate inbound prospects against ICP criteria, build a systematic qualification workflow, score and route leads, establish MQL/SQL definitions, or design pipeline stages. Also use when the user says "which leads should I focus on," "how do I qualify inbound leads," "define my ICP," "set up lead scoring," or "how do I route leads to the right person."

Context Required

From `startup-context` or the user:

• **ICP definition** — Who is the ideal customer (company size, industry, stage, geography, use case)
• **Lead sources** — Where inbound leads come from (website, events, content, referrals)
• **CRM and tooling** — Current stack for managing leads and deals
• **Current customers** — Who are the best existing customers and why
• **Pipeline data** — Existing deals, active customers, prior contacts
• **Sales capacity** — Who handles leads and what is their bandwidth

Work with whatever the user provides. If they have a clear problem area, start there. Do not block on missing inputs.

Workflow

1. **Load ICP and configuration** — Read startup-context if available. Establish the qualification criteria across company attributes, person attributes, and use case fit. 2. **Parse the lead data** — Accept leads in any format (CSV, list, CRM export, single name). Identify data gaps and flag what needs enrichment. 3. **Check pipeline overlap** — Before scoring, check for existing customers (route to upsell), active deals (flag for sales coordination), and prior contacts (note history). Pipeline overlaps are routing flags, not disqualifiers. 4. **Score company fit** — Evaluate against company size, industry, stage, geography, and use case alignment. Weight each dimension based on what predicts closed-won deals. 5. **Score person fit** — Evaluate title, seniority, department, and decision-making authority. A perfect company with the wrong contact still needs routing, not rejection. 6. **Score use case alignment** — Connect the lead's inferred intent to specific product capabilities. Inbound signals (demo requests, pricing page visits) tip borderline cases toward qualification. 7. **Generate composite score and verdict** — Produce a 0-100 composite score and assign a routing recommendation. 8. **Export structured output** — Deliver results in a table or CSV with all qualification data, scores, and routing.

Output Format

Deliver these documents: 1. **Scored lead report** — Each lead with composite score (0-100), sub-scores by dimension, verdict category, and routing recommendation 2. **ICP definition** — Firmographic and demographic criteria with priority tiers 3. **Scoring model** — Complete point-value table for company, person, and use case dimensions with threshold definitions 4. **Pipeline routing rules** — How each verdict category gets handled

Frameworks & Best Practices

Verdict Categories

Assign every lead to one of these routing buckets based on composite score:

| Verdict | Score | Action | |---------|-------|--------| | **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 MQ