Tracking · Lead Scoring & Qualification

Design a 0-100 Lead Score for Inbound Agency Leads

Generate a complete 0-100 lead scoring model tailored to inbound form fills, website behavior, and firmographics for your agency.

foundermanagerIntermediate⏱ 4-6 hours of ops design work
When to use
Use when inbound volume is high enough that reps are wasting time on bad-fit leads, or when you're standing up a new HubSpot/Pipedrive scoring property. Best for agencies running paid traffic to a contact form or audit offer. Re-run quarterly as your ICP sharpens.
The prompt
You are a marketing ops architect who has designed lead scoring models inside HubSpot and Pipedrive for digital agencies receiving 100+ inbound leads per month.
Agency: [AGENCY_NAME] — [SERVICES] | ICP: [ICP] | Inbound sources: [INBOUND_SOURCES] | Form fields captured: [FORM_FIELDS] | Behavioral data available: [BEHAVIORAL_DATA] | Current close rate by source: [CLOSE_RATE_DATA]
Design a complete 0-100 lead scoring model with firmographic points, behavioral points, negative points, and routing thresholds.

- Total must cap at 100 — split roughly 60 firmographic / 40 behavioral
- Include at least 4 NEGATIVE point rules that subtract from the score
- Define routing thresholds: 80+ to senior AE, 50-79 to SDR, under 50 to nurture
- For any data point not present in [FORM_FIELDS] or [BEHAVIORAL_DATA], mark "Unknown — cannot score" rather than inventing
- Tie weights back to [CLOSE_RATE_DATA] where possible
Three tables: (1) Firmographic Points — Attribute / Value / Points / Source Field, (2) Behavioral Points — Action / Points / Decay, (3) Negative Points — Trigger / Points. Then a Routing block and a 3-bullet Implementation Note.
Variables
  • [AGENCY_NAME] — Your agency
  • [SERVICES] — Services sold
  • [ICP] — Ideal client description
  • [INBOUND_SOURCES] — Where leads come from (Google Ads, SEO, referrals, etc.)
  • [FORM_FIELDS] — Exact fields captured on your forms
  • [BEHAVIORAL_DATA] — Tracked behaviors (page views, email opens, demo requests)
  • [CLOSE_RATE_DATA] — Historical close rates by source/segment if known
Example input
Agency: Northwind Digital — SEO + Google Ads for home services | ICP: $5M-$30M HVAC/plumbing US | Sources: Google Ads, organic, referrals | Form fields: company, website, monthly revenue range, services needed, timeline | Behavioral: page views, audit tool completion, pricing page visits, email opens | Close rates: Referrals 28%, Organic 14%, Google Ads 6%
Example output
Firmographic (60 max)
| Attribute | Value | Points | Field |
|---|---|---|---|
| Revenue | $5-30M | 25 | revenue_range |
| Industry | HVAC/Plumbing | 20 | services_needed |
| Source | Referral | 15 | utm_source |
| Source | Organic | 8 | utm_source |

Behavioral (40 max)
| Action | Points | Decay |
|---|---|---|
| Audit completed | 20 | 30 days |
| Pricing visit | 10 | 14 days |
| 3+ page views | 5 | 7 days |
| Email reply | 15 | none |

Negative
| Trigger | Points |
|---|---|
| Revenue under $1M | -30 |
| Non-US | -25 |
| Competitor domain | -50 |
| Job seeker keywords | -40 |

Routing: 80+ Senior AE same-day, 50-79 SDR within 24h, <50 nurture sequence.
Pro tips
  • Validate the model by scoring your last 50 closed deals — the model is wrong if too many closed-wons score under 50
  • Add behavioral decay so dead leads naturally fall out of the AE queue
  • Re-run this prompt every quarter with updated close-rate data — ICPs drift
Works with
ClaudeChatGPTGemini
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