Analyze · ICP-Fit & Lead-Quality Analysis

Analyze Inbound Lead Quality vs ICP

Score last month's inbound leads against your ICP so you know whether your top-of-funnel is attracting the right agency clients.

foundermanagerIntermediate⏱ 2-3 hours per month
When to use
Use at the end of each month when you have a fresh batch of inbound form-fills, demo requests, or chat leads. It tells you what % of inbound actually matches your ICP and which channels are sending junk. Run before changing ad spend, gated content, or SDR scripts.
The prompt
You are an analytics-driven head of marketing for a digital marketing agency. You think in ICP scorecards, not vibes.
Agency: [AGENCY_NAME] — [SERVICES]
ICP: [ICP_DEFINITION]
Data (last 30 days of inbound):
[LEAD_LIST] — each row has: company, industry, employee_count, revenue_band, source (channel), reason_for_inquiry
Score every inbound lead against the ICP and tell me whether our top-of-funnel is attracting the right buyers or wasting our SDRs' time.

- Score each lead on the explicit ICP criteria (industry, size, revenue, fit reason) — show the full matrix, do not skip rows.
- Use a 0–3 scale per criterion. Sum = ICP fit score. Bucket: A (10–12), B (7–9), C (4–6), D (0–3).
- No judgement on D-tier leads — just classify and route.
- Segment results by [SOURCE] so I can see which channels send A/B vs C/D.
- Recommend ICP refinements only if a pattern shows up in 3+ leads.

1) Scored matrix table (lead | each ICP criterion score | total | bucket | source). 2) Source-level summary (% A/B by channel). 3) 3 concrete actions: kill, double down, or refine.
Variables
  • [AGENCY_NAME] — Your agency's name
  • [SERVICES] — Core services you sell (e.g., SEO, paid media, web design)
  • [ICP_DEFINITION] — Your ICP from the Structure stage — industry, size, revenue, pain
  • [LEAD_LIST] — Last 30 days of inbound leads with company, industry, headcount, revenue band, source, reason
  • [SOURCE] — Channel field name (e.g., paid_search, organic, referral, podcast)
Example input
Agency: Northbeam Digital — SEO + paid social for DTC brands
ICP: US/CA ecommerce brands, $3–25M revenue, 10–80 staff, Shopify, struggling with blended CAC
Leads (last 30 days):
1. GreenLeaf Tea | DTC tea | 22 staff | $6M | Google Ads | wants to lower CAC
2. Joe's Plumbing | local services | 4 staff | $400k | Facebook | needs leads
3. NovaSkin | DTC skincare | 35 staff | $11M | Referral | scaling Meta
4. SaaSCo | B2B SaaS | 60 staff | $8M | Newsletter | wants SEO
5. PetPaws | DTC pet | 12 staff | $2.4M | Google Ads | new launch
Example output
Scored Matrix:
| Lead | Industry | Size | Revenue | Fit reason | Total | Bucket | Source |
| GreenLeaf | 3 | 3 | 3 | 3 | 12 | A | Google Ads |
| Joe's Plumbing | 0 | 0 | 0 | 1 | 1 | D | Facebook |
| NovaSkin | 3 | 3 | 3 | 3 | 12 | A | Referral |
| SaaSCo | 0 | 2 | 3 | 1 | 6 | C | Newsletter |
| PetPaws | 3 | 3 | 1 | 2 | 9 | B | Google Ads |

By source: Google Ads 67% A/B, Referral 100% A, Facebook 0% A/B, Newsletter 0% A/B.

Actions:
1. Kill Facebook lead-gen campaign — pulling local SMBs.
2. Double down on referral program — every referral is A-tier.
3. Refine newsletter CTA to filter out B2B SaaS — clearly off-ICP.
Pro tips
  • Re-run monthly and watch the A/B % trend — that's your lead-quality north star.
  • Paste in your last paid ads headlines too — often the messaging is what's attracting D-tier.
  • Score blind first (hide source column), then reveal source — removes confirmation bias.
Works with
ClaudeChatGPTGemini
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