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.
foundermanagerIntermediate⏱ 3-4 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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