Revenue Operations

Create a Lead Qualification Framework

Turn verified ideal-customer criteria and disqualifiers into a reviewable lead-qualification framework with explicit evidence and unknown states.

Quick facts

Best for
Owners · Founder-operators · Sales leaders
Prompt type
Effectiveness
Expected result
A qualification framework with evidence-backed conditions, explicit unknown and insufficient-data states, flagged criteria, and a human decision checklist.
Time saved
60-90 minutes per framework draft
Required inputs
IDEAL CUSTOMER CRITERIA · DISQUALIFIERS · OBSERVED LEAD FIELDS · QUALIFICATION OWNER
Works with
ChatGPT + Claude

What this prompt does

  • Translates supplied fit criteria into observable qualification questions and evidence requirements.
  • Keeps qualified, disqualified, and insufficient-data outcomes separate for human review.

When to use

  • When a service business needs a consistent, explainable first-pass qualification framework before changing its intake or CRM process.

When not to use

  • Do not use it to make a live eligibility decision, reject a lead, change a policy, update an account, or contact anyone without a named human reviewer.

The prompt

#CONTEXT:
Draft a lead-qualification framework for a service business. Use only the supplied business rules and observed lead fields. This is a recommendation for human review, not an eligibility decision or live workflow.

#INPUTS:
- Approved ideal-customer criteria and definitions: [IDEAL CUSTOMER CRITERIA]
- Approved disqualifiers and their business rationale: [DISQUALIFIERS]
- Fields actually observed in the lead record, including blanks: [OBSERVED LEAD FIELDS]
- Named human qualification owner: [QUALIFICATION OWNER]

#INSTRUCTIONS:
1. Separate each criterion into its observable evidence, qualifying condition, disqualifying condition, and unknown condition.
2. Identify criteria that cannot be evaluated from the observed fields. Mark the outcome "Insufficient data"; do not guess, invent, infer, or fabricate a fact.
3. Distinguish a verified disqualifier from a missing answer. Missing evidence is not evidence of disqualification.
4. Do not qualify, disqualify, score, or route a person based on a protected trait or a proxy for a protected trait. Flag any criterion that could function as a proxy for removal and human review.
5. Explain the service-business reason for every retained criterion without creating new policy.
6. Recommend no adverse decision when the evidence is unknown, conflicting, or insufficient.
7. Do not access or update a CRM, contact a lead, send a message, change a rule, or execute routing.
8. A named human owner must review and approve every qualification, disqualification, scoring, or routing rule change before operational use.

#RESPONSE FORMAT:
## Assumptions and source limits
- Supplied fact — source input
- Unknown — why it matters

## Qualification framework
| Criterion | Observable evidence | Qualifying condition | Disqualifying condition | Unknown / insufficient-data state | Rationale |
|---|---|---|---|---|---|

## Criteria requiring removal or revision
- Criterion — protected-trait, proxy, unsupported, or ambiguous concern — human review needed

## Human decision checklist
- Qualification owner named
- Every rule traceable to supplied policy
- Insufficient-data path preserved
- Rule changes approved before operational use

Input checklist

  • IDEAL CUSTOMER CRITERIA
  • DISQUALIFIERS
  • OBSERVED LEAD FIELDS
  • QUALIFICATION OWNER

Example input

Fictional example — IDEAL CUSTOMER CRITERIA: Northstar Field Services serves commercial facilities within 40 miles of Cedar Junction; requests should concern planned HVAC maintenance for one or more sites. DISQUALIFIERS: Residential-only request or verified location outside the approved coverage map. OBSERVED LEAD FIELDS: Organization name: Harbor Workshop; request: quarterly HVAC maintenance; site type: commercial; address: blank; number of sites: unknown. QUALIFICATION OWNER: Elena, Revenue Operations Manager.

Expected output structure

  • A qualification framework with evidence-backed conditions, explicit unknown and insufficient-data states, flagged criteria, and a human decision checklist.

Customize this prompt

  • Replace broad fit language with observable evidence the intake team can consistently collect.
  • Review disqualifiers quarterly so an old capacity or territory rule does not become hidden policy.

Guardrails

  • Do not qualify, disqualify, score, route, or recommend an adverse decision based on a protected trait or any proxy for a protected trait.
  • Do not invent missing lead facts; preserve Unknown and Insufficient data as valid outcomes.
  • A named human owner must review and approve every qualification, disqualification, scoring, routing rule change, policy exception, account update, external message, price, promise, permission, and legal commitment before any operational use.
  • The assistant must not access or change a live CRM, route a lead, contact anyone, or send an external message.

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