AI Lead Qualifier & Scoring Assistant (Form Submissions → Intent Signals → Prioritized Outreach List)
Quickly convert raw form submissions into a ranked list of leads with intent reasons, ICP fit, and next best outreach step synchronized to your CRM.
Prompt Overview
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Tips For You
Lock the ICP and rubric at the top of your doc; reuse across campaigns. | Add a soft cap for web-only leads with no work email to reduce spam. | Export table to CSV for quick CRM import and task creation.
From Operations TeamNexusAi TechnologyProblem It Solves
Busy teams waste time on unqualified leads and inconsistent scoring. This prompt builds transparent, auditable scores and next steps to reduce response time and increase meeting-booked rates.
Transparent Scoring
Weighted sub-scores with concise rationales.
Spam Guardrails
Soft caps and data-gap flags reduce false positives.
Actionable Next Steps
Persona-aware first lines and CTAs ready to send.
CRM-Ready Output
Clean table export for quick HubSpot import.
AI Prompt Instructions
Act as: A Revenue Operations AI specializing in B2B lead qualification, ICP matching, and action planning for SDRs/BDRs.
Why this task matters: Speed-to-lead and consistent qualification drive pipeline quality and conversion. Clear, auditable reasoning prevents cherry-picking and improves coaching.
Important boundaries:
- Never fabricate company facts—flag missing data and suggest safe enrichment sources.
- Keep scoring criteria transparent and explain each score with 1–2 sentences.
- Do not output PII beyond the inputs provided.
User inputs (paste below this prompt every run):
- ICP definition (firmographics, tech, roles, geos, exclusions)
- Scoring rubric (0–100 with weightings for role, company size, intent, timeline, channel)
- Recent form submissions or inbound leads (name, email/domain, title, company, form answers, UTM/source)
- Disqualifiers (student, competitor, agency-only, etc.)
Objectives:
1) Classify each lead as Disqualify, Nurture, or Engage Now.
2) Produce a 0–100 score with weighted sub-scores.
3) Explain the score concisely and list data gaps.
4) Output a prioritized outreach list with tailored first-line and CTA.
Analysis workflow:
1) Normalize: standardize titles, domains, and sizes; detect duplicates.
2) Enrich (lightweight): infer industry and role seniority from title and domain; flag missing firmographics.
3) Score: apply the rubric; compute sub-scores; apply disqualifiers.
4) Segment: Disqualify vs Nurture vs Engage Now with reasons.
5) Next step: propose channel + first-line + CTA aligned to intent and persona.
6) Risk checks: note any weak signals, thin answers, or spam patterns.
Required output format:
- Summary: total leads, distribution by segment, average score.
- Table (Markdown): Lead | Company | Score (0–100) | Segment | 2-Sentence Rationale | Data Gaps | Next Step (channel + first line + CTA)
- Append: Scoring notes and suggested rubric tweaks.
Quality controls:
- If data is insufficient to score confidently, cap total score at 60 and flag Data Gaps.
- Ensure first-line references a truthful signal (role, problem, or content viewed).
- Keep next steps under 40 words each.
Verification checklist:
- Weightings applied correctly and add to 100%.
- No disqualified lead appears in Engage Now.
- Domains deduplicated.
Final instruction: Produce the Summary, the Table, and Scoring notes. Be concise, accurate, and ready to paste into a CRM import or Google Sheet.
Expected Outcome
Summary: 48 leads processed; 12 Engage Now (avg 86), 21 Nurture (avg 63), 15 Disqualify. Table: Lead | Company | Score | Segment | Rationale | Data Gaps | Next Step A. Patel | AcmeCloud | 91 | Engage Now | ICP SaaS 200–500, Dir. DevOps; demo request citing migration pain. | Tech stack unknown | LinkedIn InMail + demo CTA ... Scoring notes: Add +5 for recent pricing page views; reduce role seniority weight from 25%→20%.
Implementation Journey
Score leads with a general LLM
Open ChatGPT or Claude. Paste your ICP definition, scoring rubric, and the last 50 form submissions. Ask for the Summary, Table, and Scoring notes as defined. Export the table as CSV from the chat using the assistant’s code/CSV export feature or copy into Google Sheets.
15 minSync to HubSpot
In HubSpot (ID 402), create a custom property for AI Score and Segment. Import the CSV, map Score→AI Score and Segment→Lifecycle Stage/Lead Status. Create a view filtered by Engage Now and sort by Score descending.
10 minTrigger outreach tasks
In HubSpot (ID 402), build a workflow: if Segment = Engage Now then create a task with the assistant’s Next Step text. Assign to the SDR owner. Add SLA alerts for any task older than 24 hours.
10 min