Lead Researcher

Score and qualify sales leads against an Ideal Customer Profile (ICP) definition, then draft outreach hooks tied to specific ICP signals.

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Table of Contents

• Keywords
• Quick Start
• Core Workflows
• Tools
• Reference Guides
• Templates
• Best Practices
• Scope & Limitations

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Keywords

lead, leads, prospect, prospecting, sales, outbound, ICP, ideal customer profile, qualify, qualification, scoring, account list, target account, outreach, cold email, BDR, SDR, account executive

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Clarify First

Before scoring leads, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• ICP definition: the must-have / nice-to-have / disqualifier attributes; this IS the scoring model.
• Lead list columns: company, industry, size, country at minimum; missing fields mean no usable score.
• GTM motion: PLG vs sales-led vs channel changes which signals weight highest and the outreach-hook framing.

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

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Quick Start

Score a Lead List in 10 Minutes

1. Define your ICP in icp.json using the schema in assets/icp_schema.json
2. Save your lead list as a CSV with columns: company,industry,size,country,website,signals
3. Run the qualifier:

   python scripts/lead_qualifier.py icp.json leads.csv
   

4. Review the ranked output — top 20% is your A-tier outreach list

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Core Workflows

Workflow 1: ICP-Based Lead Scoring

Goal: Rank a list of candidate accounts so the top of the list reflects the best fit, not the most recent import.

Steps:

1. Build your ICP in icp.json — see assets/icp_schema.json for the full schema
2. Capture leads in a CSV with at minimum: company,industry,size,country
3. Run: python scripts/lead_qualifier.py icp.json leads.csv
4. Sort the result by score (highest first); the top 20% is your A-tier
5. Discard everything below the disqualification threshold rather than mass-emailing

Expected Output: Ranked list with score, tier (A/B/C/disqualified), and reason per lead.

Time Estimate: 10-15 minutes for a list of 200 leads.

Workflow 2: ICP Definition

Goal: Convert a fuzzy "we sell to ops teams at mid-market SaaS" intuition into a structured ICP that the qualifier can actually score against.

Steps:

1. Pull the company names of your last 20-50 best customers
2. Identify the shared signals: industry, size band, geography, tech stack, pain trigger
3. For each, decide whether it's a must-have, nice-to-have, or disqualifier
4. Encode in icp.json per references/icp_framework.md
5. Pressure-test by scoring last quarter's closed-won and closed-lost accounts — the model should rank the wins above the losses

Expected Output: A versioned icp.json that retroactively predicts your past wins.

Time Estimate: 1-2 hours for first pass, 30 minutes per quarterly refresh.

Workflow 3: Outreach Hook Drafting

Goal: Write outreach where the personalization actually mentions a real signal, not a fake "I noticed you posted on LinkedIn."

Steps:

1. Take the qualifier output for an A-tier lead
2. Read the matched ICP signals — these are your hooks
3. Use the outreach template in assets/outreach_template.md
4. Personalize the opening line with the strongest signal (e.g., recent funding, hiring spike, product launch, public quote about a pain you solve)
5. Keep the rest of the email short — sub-90 words

Expected Output: First-touch outreach email under 90 words with a real signal-based hook.

Time Estimate: 5 minutes per A-tier lead.

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Tools

lead_qualifier.py

Reads an ICP JSON file and a leads CSV, returns a scored & tiered list.

# Human-readable
python scripts/lead_qualifier.py icp.json leads.csv

# JSON for programmatic use
python scripts/lead_qualifier.py icp.json leads.csv --json

Scoring model:

• Each ICP attribute has a weight (default 10) and direction (must / nice / disqualify)
• Must-have hits: full weight
• Nice-to-have hits: half weight
• Disqualifier hits: lead drops out of consideration entirely
• Score is normalized to 0-100

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Reference Guides

• references/icp_framework.md — How to define an ICP that actually predicts deal velocity, with worked examples by GTM motion (PLG, sales-led, channel)

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Templates

• assets/icp_schema.json — JSON schema for an ICP definition file
• assets/outreach_template.md — Cold-touch email template with placeholder slots tied to ICP signals

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Best Practices

• Disqualify hard. Mediocre leads are worse than no leads — they consume rep time and damage sender reputation.
• Keep ICP versioned. When deal velocity drops, your ICP is often stale. Re-derive every quarter.
• One signal per email. Multi-signal openers feel like research dumps; one well-chosen signal feels human.
• Leads are not opportunities. A scored A-tier lead is permission to reach out, not a forecasted deal.
• Logs over feel. Track which signals correlate with closed-won — let data update the ICP, not vibes.

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Scope & Limitations

In Scope:

• Scoring a leads CSV against an icp.json definition with lead_qualifier.py: weighted must-have, nice-to-have and disqualifier rules, a 0-100 score, an A, B, C or disqualified tier, and matched signals per lead.
• Building an ICP from closed-won customers, following references/icp_framework.md and assets/icp_schema.json.
• First-touch outreach hooks tied to one matched signal, using assets/outreach_template.md.

Out of Scope:

• Finding or enriching leads. The skill does not scrape websites, LinkedIn or data providers.
• Multi-step email sequences and deliverability (see marketing/cold-email/).
• Segment-level ICP strategy for go-to-market planning (see project-management/gtm/ideal-customer-profile/).
• Pipeline management and revenue forecasting (see business-growth/revenue-operations/).
• Writing scores or tiers back to a CRM.

Important Caveats:

• All lead data comes from a CSV the user supplies. Signals such as funding or hiring are only as current as that export.
• Scores are heuristic matches of field values against the ICP rules. Missing or inconsistently worded fields lower the score.
• A high score means fit with the ICP as written, not buying intent. An ICP that is not tested against past wins and losses produces confident but wrong rankings.

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Integration Points

• Pairs with marketing/cold-email/ for sequence design
• Pairs with sales-success/ skills for account executive handoff
• Feeds into business-growth/ revenue forecasting