How to Turn Claude Code Into Your Marketing Command Center

Key Takeaways

• Claude Code + marketing APIs lets you cross-reference Google Ads, Meta Ads, and GA4 data in seconds using natural language
• Setup time: About an hour for initial config, then 5 minutes per weekly data refresh
• Biggest win: Cross-platform attribution analysis surfaces budget optimization opportunities in a single query
• No dashboards required. Data lands in JSON files, and you talk to Claude Code directly
• Technical requirement: API credentials for your ad platforms and basic comfort with the terminal

What You're Building

A project directory where Claude Code has access to scripts that pull live data from your marketing platforms. You fetch the data, it lands in JSON files, and then you ask questions across all sources simultaneously.

No dashboards to build. No Looker Studio templates to maintain.

marketing-project/
├── config.json              # Account IDs + API credentials
├── fetchers/
│   ├── fetch_google_ads.py  # Google Ads campaigns + search terms
│   ├── fetch_meta_ads.py    # Meta Ads campaigns + ad sets
│   └── fetch_ga4.py         # Google Analytics 4 events
├── data/
│   ├── google-ads/          # Campaign performance, search terms
│   ├── meta-ads/            # Ad sets, creatives, audiences
│   └── ga4/                 # Traffic, conversions, attribution
└── reports/                 # Generated analysis

Step 1: Set Up API Authentication

Before Claude Code can analyze your marketing data, you need API access to each platform. Here's the setup for each:

Google Ads API

1. Create a Google Cloud project at console.cloud.google.com
2. Enable the Google Ads API
3. Create OAuth2 credentials (or a service account for automated access)
4. Note your Customer ID from the Google Ads interface

Meta Marketing API

1. Go to developers.facebook.com
2. Create an app and add the Marketing API product
3. Generate a long-lived access token
4. Note your Ad Account ID

Google Analytics 4

1. In your Google Cloud project, enable the Analytics Data API
2. Add your service account email as a viewer on your GA4 property
3. Note your GA4 Property ID

Step 2: Build Your Data Fetchers

Each fetcher is a Python script that pulls data from one platform and saves it as JSON. Claude Code can then read all these JSON files together.

The key insight: you're not building a dashboard. You're building a data layer that Claude Code can reason over.

Google Ads Fetcher

Your Google Ads fetcher should pull:

• Campaign performance: spend, clicks, impressions, conversions by campaign
• Search terms report: what people searched before clicking your ads
• Ad group data: performance broken down by ad group
• Date range: last 30 days by default, configurable

Meta Ads Fetcher

Your Meta Ads fetcher should pull:

• Campaign performance: spend, reach, impressions, results by campaign
• Ad set data: audience targeting + performance per ad set
• Creative performance: which ad creatives are winning
• Date range: matching your Google Ads window

GA4 Fetcher

Your GA4 fetcher should pull:

• Traffic by source/medium: organic, paid, direct, social, referral
• Top landing pages: pageviews and engagement by URL
• Conversion events: your key events (purchases, signups, demos)
• User acquisition: new vs returning users by channel

Step 3: Ask Cross-Platform Questions

This is where it gets powerful. With data from all three platforms in your project, you can ask Claude Code questions like:

Budget optimization:

"Compare my Google Ads and Meta Ads cost per acquisition for the last 30 days. Which platform is more efficient for each campaign objective?"

Attribution gaps:

"Show me landing pages that get organic traffic from GA4 but don't have corresponding paid campaigns in Google Ads. Are there keyword opportunities I'm missing?"

Creative performance:

"Which Meta ad creatives have the highest click-through rate, and do those landing pages also perform well in GA4 engagement metrics?"

Spend analysis:

"My total ad spend across Google and Meta was $X last month. Break down the ROI by platform and campaign type."

What Questions Can You Answer?

Here's a non-exhaustive list of the kinds of cross-platform analysis you can run:

Question Type	Data Sources	Example
Budget allocation	Google Ads + Meta Ads	"Where should I shift $1000 from underperforming campaigns?"
Landing page performance	GA4 + Google Ads	"Which paid landing pages have high bounce rates?"
Audience overlap	Meta Ads + GA4	"Are my Meta audiences reaching the same users as organic?"
Conversion attribution	All three	"What's the true cost per conversion including all touchpoints?"
Search term mining	Google Ads + GA4	"Which search terms drive paid clicks but also rank organically?"

Next Steps

Once your data layer is set up, the ongoing workflow is simple:

1. Run fetchers weekly (or automate with cron)
2. Ask Claude Code whatever questions come up
3. Export insights to reports for stakeholders

The CC for Marketing Command Center automates steps 1 and 2 with pre-built skills that handle the API connections, data formatting, and common analysis patterns.