Analyze · ICP-Fit & Lead-Quality Analysis

Build a Lead-Quality Trend Report

Generate a month-over-month lead-quality trend report for your agency that leadership can actually read in 60 seconds.

foundermanagerIntermediate3-4 hours per monthly report
When to use
Use for monthly leadership reviews, board updates, or quarterly business reviews. It converts messy CRM exports into a clear trendline of A/B/C/D leads over time so you can see whether the funnel is improving or rotting.
The prompt
You are an analytics-driven head of marketing for a digital marketing agency, building a monthly lead-quality trend report.
Agency: [AGENCY_NAME] — [SERVICES]
ICP: [ICP_DEFINITION]
Lead data (last 3–6 months):
[LEAD_LIST] — each row: month, lead, source, industry, size, revenue, ICP_bucket_if_known
Produce a month-over-month lead-quality trend report showing whether ICP-fit volume is improving, holding, or degrading — broken out by source.

- Score any unbucketed leads on ICP criteria — show the matrix in an appendix.
- Plot A/B vs C/D counts AND % by month.
- Break out by [SOURCE] — sources can't hide in a blended average.
- Call out any source with a >15pp swing month-over-month.
- Do not editorialize — let the numbers carry it; end with one paragraph of interpretation.

1) Monthly trend table (month | total | A/B count | A/B % | C/D count | C/D %). 2) Source breakdown table. 3) Material swings list. 4) One-paragraph interpretation. 5) Appendix: scoring matrix.
Variables
  • [AGENCY_NAME] — Your agency's name
  • [SERVICES] — Services offered
  • [ICP_DEFINITION] — Current ICP
  • [LEAD_LIST] — Multi-month lead export with month, source, firmographics
  • [SOURCE] — Channel field (paid, organic, referral, partner, etc.)
Example input
Agency: Northbeam Digital — SEO + paid social for DTC
ICP: US DTC, $3–25M, 10–80 staff, Shopify
3-month lead data (Mar–May):
Mar: 40 leads (Paid 20, Organic 12, Referral 8) — A/B 18, C/D 22
Apr: 46 leads (Paid 24, Organic 14, Referral 8) — A/B 19, C/D 27
May: 51 leads (Paid 28, Organic 14, Referral 9) — A/B 17, C/D 34
Example output
Monthly Trend:
| Month | Total | A/B | A/B % | C/D | C/D % |
| Mar | 40 | 18 | 45% | 22 | 55% |
| Apr | 46 | 19 | 41% | 27 | 59% |
| May | 51 | 17 | 33% | 34 | 67% |

Source breakdown (A/B %):
| Source | Mar | Apr | May |
| Paid | 35% | 29% | 18% |
| Organic | 50% | 50% | 50% |
| Referral | 75% | 75% | 78% |

Material swings: Paid dropped 17pp Mar→May (>15pp). Organic and referral flat.

Interpretation: Total volume is up 28% but ICP fit collapsed 12pp because all of the growth came from Paid, which is degrading sharply. Referral and organic are stable and high-quality. Investigate paid creative/targeting changes made in April before volume goals override quality.
Pro tips
  • Keep your bucket definitions stable across months or the trend is meaningless.
  • Drop the table into Notion or a deck — execs will only read the trend + interpretation.
  • Pair with pipeline conversion data next month to show A/B leads convert at X× the rate.
Works with
ClaudeChatGPTGemini
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