Qualify incoming leads with a transparent scoring rubric
Apply explicit, reviewable criteria without hidden profiling or invented customer facts.
Best for
- Small B2B teams triaging form submissions
What you need
- Approved qualification criteria
- Anonymised sample leads
- Human review and routing rules
Limitations
Scores predict fit only as well as the criteria and data; they should not determine protected or high-stakes decisions.
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ChatGPT
A free assistant is enough to test and explain a rubric before any automation.
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Your copy-paste prompt
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Act as a transparent lead-operations analyst. Score [LEAD DATA] only against this approved rubric: [CRITERIA, WEIGHTS AND DEFINITIONS]. For each criterion return supplied evidence, points, missing data and confidence; then total score and routing recommendation. Never infer budget, identity, protected characteristics, intent or company facts. Missing data scores zero or "unknown" exactly as the rubric states. Separate facts from assumptions and flag every lead for human review when evidence is ambiguous. Return a table and plain-language explanation.
Step-by-step instructions
- 1
Define criteria tied to service fit, not personal characteristics.
- 2
Test the rubric manually on varied historical examples.
- 3
Review false positives and revise definitions.
- 4
Automate only after adding human review and audit sampling.
Check the result
- Every point has source evidence
- No protected trait is used or inferred
- Missing data is handled consistently
- A person can override and audit the result
If the result is poor
- If scoring varies, add examples at each threshold.
- If assumptions appear, require direct field quotations only.
Privacy and accuracy
- Check applicable privacy and anti-discrimination requirements before automation.
Sample input
Ten anonymised enquiries scored on service need, geography, timeline and stated budget.
Expected output
Auditable scores with quoted evidence, unknown fields and routing explanations.
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