Measurement & Analytics Framework — AI US Growth Agency

Data Architecture

Shopify (orders, products, customers)
    │
    ├──→ GA4 (web behavior, conversion events)
    │       │
    │       └──→ Looker Studio / Dashboard
    │
    ├──→ Meta Ads (impressions, clicks, conversions via CAPI)
    │       │
    │       └──→ Meta Attribution
    │
    ├──→ Google Ads (impressions, clicks, conversions via GTag)
    │       │
    │       └──→ Google Attribution
    │
    └──→ Warehouse / Spreadsheet (merged truth)
            │
            └──→ SKU-level P&L

Core Metrics

Primary (Customer Economics)

Metric	Formula	Target	Frequency
New Customer CAC	Total ad spend / New customers acquired	<$50	Weekly
New Customer Revenue %	New customer revenue / Total revenue	>60%	Weekly
MER	Total revenue / Total ad spend	>3.0x	Weekly
Contribution Margin (pre-ad)	Revenue - COGS - shipping - transaction fees	Track	Weekly
Contribution Margin (post-ad)	CM pre-ad - ad spend	>25%	Weekly
First-order Payback	CAC / Monthly contribution per customer	<60 days	Monthly
30/60/90 Day Repurchase	Cohort retention rates	Track	Monthly

Secondary (Platform Diagnostics)

Metric	Platform	Use
Platform ROAS	Meta, Google	Diagnostic only — NOT primary KPI
CTR	Both	Creative quality indicator
CPM	Both	Audience/competition indicator
Frequency	Meta	Ad fatigue warning
Impression Share	Google	Budget constraint indicator

Attribution Rules

Why Platform ROAS is NOT the Primary Metric

Meta and Google both claim credit for same conversions
Platform attribution windows differ (7-day click vs 1-day view)
Neither platform sees organic, email, or direct revenue
Incrementality ≠ platform-reported conversions

MER as Truth Anchor

MER (Marketing Efficiency Ratio) = Total Revenue / Total Ad Spend

Cannot be gamed by attribution windows
Shows the full picture of paid + organic
Simple enough for founders to understand

Data Quality Checklist

Weekly

[ ] Conversion API (CAPI) events firing correctly
[ ] GA4 purchase event matches Shopify order count (±5%)
[ ] New vs returning customer flag accurate
[ ] UTM parameters preserved through checkout
[ ] Product feed updated (Google Merchant Center)

Monthly

[ ] SKU cost data refreshed
[ ] Shipping cost averages updated
[ ] Transaction fee rates verified
[ ] Return/refund rate calculated
[ ] Attribution overlap between Meta and Google estimated

SKU Contribution Margin Model

Revenue per unit: $X
- COGS: ($X)
- Shipping: ($X)
- Transaction fees: ($X)
- Packaging: ($X)
= Contribution Margin (pre-ad): $X

- Ad cost per order: ($X)  ← weighted average CAC
= Contribution Margin (post-ad): $X

Incrementality Test Design

Geo-Holdout Test (Quarterly)

Select 2-3 comparable geographic regions
One region: pause all paid ads for 2 weeks
Compare organic/direct revenue delta
Calculate true incrementality: (test revenue - holdout revenue) / ad spend

Facebook Conversion Lift (Optional)

Requires minimum $30K/test budget
Platform-native A/B test with holdout group
Valid for Meta-only incrementality

Data Gaps & Assumptions

Data Point	Status	Action
COGS per SKU	Must be client-provided	Include in onboarding questionnaire
Shipping cost avg	Must be client-provided	Include in onboarding questionnaire
Return rate	Available from Shopify	Pull via API
LTV (3+ months)	Not available for new clients	Estimate after 90 days
Cross-device attribution	Not measurable without identity graph	Acknowledge as limitation

AI Venture Studio · AI US Growth Agency