Purpose

This recipe produces a multi-channel marketing operations dashboard that aggregates spend, impressions, clicks, and conversions from all active ad platforms, overlays Google Analytics 4 session data, calculates channel-level ROAS and blended CAC, visualizes attribution across touchpoints, and tracks budget consumption vs plan. [src1]

Prerequisites

Google Analytics 4 property with data collection active — GA4 setup guide
Ad platform accounts with API access — Google Ads (developer token), Meta Business (system user token), LinkedIn/TikTok as applicable
PostgreSQL database — free tier at Supabase or Neon
Dashboard tool account — Retool, Metabase, or Google Looker Studio
Monthly marketing budget breakdown by channel
Conversion tracking configured in GA4 — at least sign_up, purchase, or lead events firing

Constraints

Google Analytics 4 Data API: 10,000 requests/day per property. Batch dimensions into single requests. [src1]
Meta Marketing API: 200 calls/hour per ad account. Use batch endpoints for multi-campaign pulls. [src2]
Attribution windows differ across platforms. Normalize before cross-channel comparison. [src3]
Cookie consent reduces trackable data by 20-40% in EU markets. Account for unattributed conversions. [src5]
Budget figures must reconcile with accounting within 48 hours to prevent overspend decisions.

Tool Selection Decision

Which path?
├── User is non-technical AND budget = free
│   └── PATH A: No-Code Free — Google Looker Studio + native connectors
├── User is non-technical AND budget > $0
│   └── PATH B: No-Code Paid — Looker Studio + Supermetrics ($39/mo)
├── User is semi-technical or developer AND budget = free
│   └── PATH C: Code + Free — Metabase + PostgreSQL + n8n
└── User is developer AND budget > $0
    └── PATH D: Code + Paid — Retool + PostgreSQL + Airbyte + dbt

Path	Tools	Cost	Speed	Output Quality
A: No-Code Free	Looker Studio + GA4/Ads connectors	$0	3-4 hours	Basic — Google channels only
B: No-Code Paid	Looker Studio + Supermetrics	$39/mo	4-6 hours	Good — multi-channel, limited models
C: Code + Free	Metabase + PostgreSQL + n8n	$0	8-10 hours	Good — full SQL, custom attribution
D: Code + Paid	Retool + PostgreSQL + Airbyte + dbt	$50-100/mo	6-8 hours	Excellent — automated transforms

Execution Flow

Step 1: Design the Multi-Channel Data Model

Duration: 45-90 minutes · Tool: SQL client

Create unified schema: channel_spend (normalized ad platform data), ga4_events (analytics), marketing_budget (plan), attribution_touches (multi-touch credit), content_performance (organic). [src1]

Verify: All 5 tables created. · If failed: Check PostgreSQL permissions.

Step 2: Build Multi-Platform Data Sync

Duration: 2-4 hours · Tool: n8n, Airbyte, or custom scripts

Rate limit: Google Ads = 15,000/day, Meta = 200/hour, GA4 = 10,000/day

Configure ETL pipelines for each ad platform: Google Ads (GAQL queries), Meta Marketing API (insights endpoint), GA4 Data API (runReport). Schedule daily sync at 6:00 AM. [src2]

Verify: channel_spend has data for each platform after first sync. · If failed: Check OAuth tokens and API endpoint versions.

Step 3: Build Attribution and Analytics Queries

Duration: 1-2 hours · Tool: SQL

Create five core queries: channel performance (ROAS, CPA, CTR), budget vs actual, daily spend trend with 7-day moving average, funnel conversion by source/medium, and content performance ranking. [src3]

Verify: Queries return data for each configured platform. · If failed: Check platform name consistency between tables.

Step 4: Assemble the Dashboard UI

Duration: 1-2 hours · Tool: Retool, Metabase, or Looker Studio

Layout: KPI row (total spend, blended ROAS, blended CAC, conversions, budget remaining), channel ROAS chart, budget vs actual chart, daily spend trend, funnel table, content performance table, campaign detail table with date range and platform filters. [src4]

Verify: All sections render. KPI values match platform totals within 5%. · If failed: Check query bindings.

Step 5: Configure Budget Alerts

Duration: 30-60 minutes · Tool: n8n + Slack/Email

Alert conditions: channel consumed >80% budget with >10 days remaining, ROAS below 1.0, daily spend >2x 7-day average.

Verify: Test alert fires with sample data. · If failed: Check Slack webhook or SMTP config.

Step 6: Deploy and Share Access

Duration: 30 minutes · Tool: Dashboard platform settings

Share URL, set role-based permissions, configure weekly email subscription.

Verify: Non-admin marketing team member can view dashboard.

Output Schema

{
  "output_type": "marketing_operations_dashboard",
  "format": "deployed web application",
  "components": [
    {"name": "channel_performance", "type": "table", "description": "Platform-level spend, ROAS, CPA, CTR"},
    {"name": "budget_tracker", "type": "chart", "description": "Budget vs actual spend by channel"},
    {"name": "spend_trend", "type": "chart", "description": "Daily spend with 7-day moving average"},
    {"name": "funnel_analysis", "type": "table", "description": "Source/medium funnel conversions"},
    {"name": "content_ranking", "type": "table", "description": "Top content by conversion rate"},
    {"name": "kpi_cards", "type": "metrics", "description": "Total spend, ROAS, CAC, conversions"}
  ],
  "refresh_interval": "60 minutes",
  "data_source": "PostgreSQL synced from ad platforms and GA4"
}

Quality Benchmarks

Quality Metric	Minimum Acceptable	Good	Excellent
Data freshness	< 24 hour lag	< 6 hour lag	< 3 hour lag
Spend accuracy	Within 10% of platform totals	Within 5%	Within 1%
Channel coverage	Google Ads + 1 channel	All paid channels	+ organic + referral
Attribution model	Last-click only	First + last touch	Multi-touch linear
Budget reconciliation	Weekly	Daily	Real-time sync

If below minimum: Check ETL job logs. Spend discrepancies usually indicate timezone mismatches or currency conversion issues.

Error Handling

Error	Likely Cause	Recovery Action
Google Ads API 403	Developer token not approved	Apply for basic access at Google Ads API Center
Meta API 190 (token expired)	System user token expired (60-day)	Generate new long-lived token via Business Settings
GA4 API quota exceeded	Too many requests in 24-hour window	Reduce frequency, batch dimensions into fewer requests
Spend data missing for platform	ETL job failed or API changed	Check ETL execution logs, verify API versions
Attribution double-counting	Overlapping attribution windows	Deduplicate conversion_id in attribution table
Budget shows negative remaining	Month boundary date filter issue	Verify DATE_TRUNC alignment with billing cycle

Cost Breakdown

Component	Free Tier	Paid Tier	At Scale ($50K+/mo)
Dashboard tool	$0 (Looker Studio / Retool free)	$10/user/mo	$100/mo
Database	$0 (Supabase)	$25/mo	$25/mo
ETL automation	$0 (n8n self-hosted)	$20/mo	$50/mo
Data connector (Supermetrics)	N/A	$39/mo	$99/mo
Total	$0	$55-95/mo	$175-275/mo

Anti-Patterns

Wrong: Comparing ROAS across platforms without normalizing attribution windows

Google's 30-day click attribution and Meta's 7-day click / 1-day view attribution measure different things. Comparing raw ROAS leads to systematically underfunding the platform with the shorter window. [src3]

Correct: Normalize to a consistent attribution window

Set all platforms to the same window (e.g., 7-day click) for comparison, or use a separate multi-touch model that credits all touchpoints consistently.

Wrong: Using platform-reported conversions as source of truth

Each platform claims credit for the same conversion. Summing Google + Meta conversions double-counts 20-40% of actual conversions. [src5]

Correct: Use GA4 or server-side tracking as single source of truth

Attribute conversions from one system (GA4 or your database), then map platform-reported metrics to that baseline.

When This Matters

Use when a startup is spending on two or more marketing channels and needs unified visibility into performance, attribution, and budget consumption. Requires at least one ad platform with active campaigns and GA4 collecting conversion events. This recipe builds the dashboard — for marketing strategy or channel selection, use a playbook card instead.

Marketing Operations Dashboard