What's inside — the exact file
This is the complete template, not a sample. The rows are worked examples — replace them with your data (and delete them before any platform import).
Tools — Template Library · Cross-platform · Reporting
Monthly side-by-side of platform-claimed conversions vs. CRM reality — one month modeled across three platforms so the math is obvious.
This is the complete template, not a sample. The rows are worked examples — replace them with your data (and delete them before any platform import).
| month | platform | spend | platform_reported_conversions | crm_leads | crm_sqls | crm_closed_won | crm_revenue | true_cac | notes |
|---|---|---|---|---|---|---|---|---|---|
| 2026-06 | Google Ads | true_cac = spend / crm_closed_won | |||||||
| 2026-06 | Meta Ads | Expect platform numbers to exceed CRM - that gap is the story | |||||||
| 2026-06 | ChatGPT Ads | ||||||||
| 2026-06 | TOTAL | Platforms will claim more than 100% of reality combined |
Every ad platform grades its own homework, and every platform gives itself an A. Google claims a conversion, Meta claims the same one, and your CRM knows only one deal closed. This monthly worksheet puts platform-claimed conversions next to CRM reality — leads, SQLs, closed-won, revenue — per platform, and computes the only CAC that matters: the one based on what actually happened.
One month is pre-modeled across three platforms so the shape of the exercise is obvious, including the polite disagreement between platform-reported conversions and CRM leads. The goal isn't to catch platforms lying (over-attribution is structural, not scandal) — it's to make budget decisions on CRM truth while using platform numbers for what they're good at: relative, within-platform comparison.
| month | The reporting month. |
|---|---|
| platform | One row per platform per month. |
| spend | Actual spend from the platform's billing. |
| platform_reported_conversions | What the platform claims, at its attribution settings. |
| crm_leads | CRM records that month attributed to the platform. |
| crm_sqls | Of those, how many qualified. |
| crm_closed_won | Deals won from the platform's traffic. |
| crm_revenue | Revenue from those deals. |
| true_cac | Spend ÷ CRM outcomes — the honest number. |
| notes | Attribution setting changes, tracking incidents, anomalies. |
Structural reasons, not fraud: view-through attribution, modeled/estimated conversions filling privacy gaps, longer windows, and two platforms both claiming a user who touched both. The reconciliation makes the over-claim ratio visible so you can discount claims appropriately.
The deepest stage your monthly volume can support statistically. High-volume ecommerce: closed-won (orders). Long-cycle B2B: SQL, with closed-won tracked as a rolling quarterly view. Our ROAS & CAC calculator handles the funnel math either way.
There's no universal ratio — what's healthy is stability. If Google has claimed roughly 1.4× your CRM-attributed leads for six straight months, that's calibration. If it jumps to 3× in a month, something changed: attribution settings, tracking, or where your budget went.
This is a Market Disrupt worksheet, not a vendor file. Import-format templates follow each platform's documented layout as of July 2026 — platforms evolve, so validate against current documentation before a large import.
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