Inbound Lead Qualification

Inbound Lead Qualification

Takes a set of inbound leads and validates each against your full ICP criteria. Not a fast-pass triage (that's inbound-lead-triage) — this is the thorough qualification step that determines whether a lead is genuinely worth pursuing, and produces a scored CSV for the team.

When to Auto-Load

Load this composite when:

• User says "qualify these inbound leads", "check if these leads are ICP", "score my inbound"
• An upstream triage has been completed and leads need deeper qualification
• User has a batch of leads and wants a qualified/disqualified verdict on each

Architecture

[Inbound Leads] → Step 1: Load ICP & Config → Step 2: CRM/Pipeline Check → Step 3: Company Qualification → Step 4: Person Qualification → Step 5: Use Case Fit → Step 6: Score & Verdict → Step 7: Output CSV

Step 0: Configuration (Once Per Client)

On first run, establish the ICP definition and CRM access. Save to the current working directory or wherever the user prefers (e.g., config/lead-qualification.json).

{
  "icp_definition": {
    "company_size": {
      "min_employees": null,
      "max_employees": null,
      "sweet_spot": "",
      "notes": ""
    },
    "industry": {
      "target_industries": [],
      "excluded_industries": [],
      "notes": ""
    },
    "use_case": {
      "primary_use_cases": [],
      "secondary_use_cases": [],
      "anti_use_cases": [],
      "notes": ""
    },
    "company_stage": {
      "target_stages": [],
      "excluded_stages": [],
      "notes": ""
    },
    "geography": {
      "target_regions": [],
      "excluded_regions": [],
      "notes": ""
    }
  },
  "buyer_personas": [
    {
      "name": "",
      "titles": [],
      "seniority_levels": [],
      "departments": [],
      "is_economic_buyer": false,
      "is_champion": false,
      "is_user": false
    }
  ],
  "hard_disqualifiers": [],
  "hard_qualifiers": [],
  "crm_access": {
    "tool": "HubSpot | Salesforce | CSV export | none",
    "access_method": "",
    "tables_or_objects": []
  },
  "existing_customer_source": {
    "tool": "HubSpot | Salesforce | CSV | none",
    "access_method": ""
  },
  "qualification_prompt_path": "path/to/lead-qualification/prompt.md or null"
}

If lead-qualification capability already has a saved qualification prompt: Reference it directly — don't rebuild ICP criteria from scratch.

On subsequent runs: Load config silently.

Step 1: Load ICP Criteria & Parse Leads

Process

1. Load the client's ICP config (or qualification prompt from lead-qualification capability)
2. Parse the inbound lead list — accept any format:
   • Output from inbound-lead-triage (already normalized)
   • Raw CSV with any column structure
   • Pasted list of names/emails/companies
   • CRM export
3. Identify what data is available vs. missing per lead:
   • Have: Fields present in the input
   • Need: Fields required for qualification but missing
   • Gap report: "X leads have company name, Y have title, Z have nothing but email"

Output

• Parsed lead list with available/missing field inventory
• Gap report for the user

Human Checkpoint

If >50% of leads are missing critical fields (company name or person title), recommend running inbound-lead-enrichment first. Ask: "Many leads are missing company/title data. Want me to enrich them first, or qualify with what's available?"

Step 2: CRM & Pipeline Check

Process

For each lead, check against existing data sources to identify overlaps:

Check 1 — Existing customer?

• Search customer database by company domain/name
• If match found: Flag as existing_customer with customer details (plan, account owner, contract status)
• This is NOT a disqualification — it's a routing flag (upsell vs. new business)

Check 2 — Already in pipeline?

• Search your CRM (HubSpot, Salesforce, CSV) for the company in active deals
• If match found: Flag as in_pipeline with deal details (stage, owner, last activity)
• Critical: Sales rep should know before reaching out that a colleague already has this account

Check 3 — Previous engagement?

• Search outreach logs for the email/company
• If match found: Flag as previously_contacted with history summary (when, what channel, outcome)

Check 4 — Known from signal composites?

• Search your CRM or signal tracking system for the company
• If match found: Flag as signal_flagged with signal type and date

Output

Each lead tagged with:

• pipeline_status: new | existing_customer | in_pipeline | previously_contacted
• pipeline_detail: One sentence explaining the overlap (or null)
• signal_flags: Any signal composite matches

Handling Overlaps

• Existing customer: Don't disqualify. Mark separately. Might be expansion/upsell.
• In pipeline: Don't disqualify. Flag for sales rep coordination. Note the existing deal owner.
• Previously contacted but no response: Still qualify. The inbound signal means they're now warmer.
• Previously contacted and rejected: Still qualify the inbound. People change their minds. Note the prior context.

Step 3: Company Qualification

Process

For each lead's company, evaluate against every ICP company dimension:

Dimension 1 — Company Size

• Check employee count against ICP range
• Sources: enrichment data, LinkedIn company page, web search
• Score: match | borderline | mismatch | unknown
• Note: If the lead is from a subsidiary or division, evaluate the relevant unit, not the parent company

Dimension 2 — Industry

• Check against target and excluded industry lists
• Be smart about classification: "AI-powered HR platform" matches both "AI/ML" and "HR Tech"
• Score: match | adjacent (related but not core target) | mismatch | unknown

Dimension 3 — Company Stage

• Seed, Series A, Series B+, Growth, Public, Bootstrapped
• Sources: SixtyFour or Orthogonal, news, enrichment data
• Score: match | borderline | mismatch | unknown

Dimension 4 — Geography

• Check HQ location and/or the specific person's location
• For remote-first companies, check where the majority of the team is
• Score: match | borderline | mismatch | unknown

Dimension 5 — Use Case Fit

• Based on what the company does, could they plausibly use the product?
• This is the most nuanced dimension — requires understanding both the product and the company's operations
• Sources: company website, product description, job postings (hint at internal tools/processes)
• Score: strong_fit | moderate_fit | weak_fit | no_fit | unknown

Output

Each lead gets a company_qualification block:

{
  "company_size": { "score": "", "value": "", "reasoning": "" },
  "industry": { "score": "", "value": "", "reasoning": "" },
  "stage": { "score": "", "value": "", "reasoning": "" },
  "geography": { "score": "", "value": "", "reasoning": "" },
  "use_case": { "score": "", "value": "", "reasoning": "" },
  "company_verdict": "qualified | borderline | disqualified | insufficient_data"
}

Step 4: Person Qualification

Process

For each lead's contact person, evaluate against buyer persona criteria:

Dimension 1 — Title/Role Match

• Check title against buyer persona title lists
• Handle variations: "VP of Marketing" = "Vice President, Marketing" = "VP Marketing"
• Be smart about title inflation at small companies (a "Director" at a 10-person startup ≠ "Director" at a 10,000-person enterprise)
• Score: exact_match | close_match | adjacent | mismatch | unknown

Dimension 2 — Seniority Level

• Map to: Individual Contributor, Manager, Director, VP, C-Level, Founder
• Check against ICP seniority requirements
• Score: match | too_junior | too_senior | unknown

Dimension 3 — Department

• Engineering, Product, Marketing, Sales, Operations, Finance, HR, etc.
• Check against ICP department targets
• Score: match | adjacent | mismatch | unknown

Dimension 4 — Authority Type

• Based on title + seniority, classify:
  • economic_buyer — Can sign the check
  • champion — Wants it, can influence the decision
  • user — Would use it daily, can validate need
  • evaluator — Tasked with research, limited decision power
  • gatekeeper — Can block but not approve
  • unknown

Dimension 5 — Right Person, Wrong Company (or Vice Versa)

• If company qualifies but person doesn't: Flag as right_company_wrong_person — this is a referral opportunity
• If person qualifies but company doesn't: Flag as right_person_wrong_company — rare for inbound, but possible with job changers

Output

Each lead gets a person_qualification block:

{
  "title_match": { "score": "", "value": "", "reasoning": "" },
  "seniority": { "score": "", "value": "", "reasoning": "" },
  "department": { "score": "", "value": "", "reasoning": "" },
  "authority_type": "",
  "person_verdict": "qualified | borderline | disqualified | insufficient_data",
  "mismatch_type": "null | right_company_wrong_person | right_person_wrong_company"
}

Step 5: Use Case Fit Assessment

Process

This step connects the company's likely needs to your product's actual capabilities. It goes deeper than Step 3's company-level use case check.

1. Infer the lead's intent from their inbound action:

   • Demo request message → What did they say they need?
   • Content downloaded → What topic were they researching?
   • Webinar attended → What problem were they trying to solve?
   • Free trial signup → What feature did they try first?
   • Chatbot conversation → What questions did they ask?

2. Map intent to product capabilities:

   • Does the product actually solve what they seem to need?
   • Is this a primary use case or a stretch?
   • Are there known limitations that would disappoint them?

3. Assess implementation feasibility:

   • Based on company size and stage, can they realistically implement?
   • Do they likely have the technical resources / team to adopt?
   • Any known blockers for companies like this? (e.g., "banks need SOC2 and we don't have it yet")