-
My experience using Claude Code + Codex to actually manage Google & Meta Ads, not just analyze them
Posted by on April 28, 2026 at 3:04 pmI have been using Claude Code and Codex for Google Ads/PPC work beyond reporting. Not just "summarize performance" or "write RSA ideas." Actual account, pull data, inspect tracking, find wasted spend, create negative keyword suggestions, write RSAs, restructure campaigns, and in some cases push changes back.
The stack is basically Google Ads API, GA4, Search Console, CRM, offline conversions, website/CMS access when available, and Meta as well for accounts that run it. The main thing I have learned is that Google Ads alone is not enough context.
Google can tell you a keyword converted. It cannot tell you whether that lead was useless in the CRM, whether sales marked it unqualified, whether the landing page created the wrong expectation, or whether the conversion event itself is broken. So if the model only sees Google Ads, it can optimize the wrong thing very confidently.
Codex has been much better for the data/account side. Search terms, overspending keywords, weird campaign/ad group patterns, wasted spend, conversion action checks, CRM comparison, that kind of analysis.
Claude Code has been better when the task gets closer to language and structure. RSAs, landing page copy, offer angles, ad group-specific messaging, turning a messy campaign into something that matches intent better.
Most boring but useful example: search terms.
Have it pull the search term report through the API, compare spend/conversions against CRM lead quality, and produce negative keyword candidates with the reason. A lot of wasted spend is painfully obvious when you look at it this way. The issue is usually that nobody wants to do the boring pass consistently.
The more interesting one is tracking.
I built a custom tracking skill for this because tracking is where a lot of PPC work secretly lives. It checks GA4, GTM, Google Ads conversions, forms, CRM status changes, offline conversion uploads, etc. That has been much more useful than I expected because so many "Google Ads problems" are actually tracking/funnel/CRM problems.
I do not think any of this replaces senior PPC people. You still need someone who knows what the business is actually trying to get, what a good lead looks like, what not to touch, when Google recommendations are nonsense, and when the model is being too confident.
But I do think it replaces a lot of junior analyst work.
Pulling reports. Checking search terms. Finding tracking issues. Drafting RSAs. Comparing campaign structure to landing pages. Making weekly notes. Flagging obvious waste. Running the same playbook every week without forgetting half of it because everyone is busy or because the person is managing 40 accounts.
It also changes the economics of smaller accounts. A small account usually does not get deep weekly analysis because the time does not justify it. But if Codex can do the first pass across Ads, CRM, tracking, website, Meta, and landing pages, then the human spends time reviewing decisions instead of digging for the obvious stuff.
Big minus: hallucinations.
If you just ask it "what happened in this account?" "make a giga comprehensive google ads analysis. Make no mistakes." it will 100% invent the answer. The only way I trust it is when it runs scripts and saves outputs.
One script pulls search terms. One pulls campaign/ad group spend. One pulls CRM outcomes. One checks conversion actions. One checks tracking. Then it analyzes the files and cites the actual rows/summaries. Then I ask another model to go through the findings, and keep iterating between two models until it's there.
Basically I treat it less like a smart chatbot and more like an operator that has to work from files, logs, APIs, and scripts.
Same with write access. I will let it write changes, but I want staged actions, change logs, and a reason for each change. Especially negatives, budgets, bids, and conversion settings. No "just go optimize it" nonsense.
My current opinion:
Agencies that do not build this into operations are going to get squeezed. Not overnight, and not because the model magically understands PPC. More because the cost of doing thorough account work is dropping, and clients will eventually expect more depth than a monthly PDF and a few generic recommendations.
Curious who else is already doing this. Are you using Claude Code/Codex with Google Ads API? Keeping it read-only? Letting it write? Connecting CRM/offline conversions/Meta too? I am mostly interested in how far people are letting the system go.
-
1 Reply
-
[deleted]
-
Great post. Very interested in your custom tracking skill. 🙂
As far as AI is concerned, build with AI that will help you in your business domain. If you think you are going to be a PPC guru because it can connect to a API? You’re not going to be. Your strategy and knowledge is what builds better skills and prompts and in return better outputs with less issues.
-
Except u can’t track search terms to individual lead quality. Not sure how accurate this post is
-
Does Claude code or Codex have direct integrations/connectors to Google ads api? You didn’t explain how you did that part. Are you using third party tools i.e. N8N or other?
-
Very insightful information since I wanted to test it myself. You mentioned the blind spots regarding lead quality. Would it work better with e-commerces accounts? Considering it would have visibility on purchases and ROAS?
-
Claude should be used as an analyst, not as a buyer role. There should always be a human at the helm approving any changes to an account.
AI can analyze, but humans decide.
-
I went pretty deep on a similar build last year and got about 80% of the way there before the maintenance started eating my weekends. The hallucination problem you described is exactly why I stopped trusting LLMs with money decisions. One confident wrong budget flip and you’re explaining yourself to a client.
The cross-platform context is the real unlock though. Google Ads in isolation is basically a black box guessing at outcomes, and most agencies never pull CRM quality back in. I ended up handing the guardrails off to chadad s so I could keep the fun analysis stuff without being on call for every silent breakage. Still run my own scripts for the creative and strategic layers, but I’m not waking up to burned budgets anymore.
-
Yes, using AI for PPC work will save a lot of time and money, and in the hands of an expert it is very powerful.
The most important thing in my opinion is offline conversions feedback to the PPC platform.
-
Great post interesting skill
-
Yes, doing this with Claude Code against a 110+ account Google Ads portfolio (mostly real estate and local services). Read access goes wide with Google Ads API, GA4, pretty much anything and everything you can enable inside of a GCP and I let it pull whatever it needs to think.
Write access is where the boring engineering matters. Mutation Safety was the first skill I built when I added write access and it was modeled after how Google Ads Editor handles posting, where you stage changes locally and push them as a batch after review. Every mutation goes through a two-step approval pattern: the model proposes the change, dumps changes with the reason, and stages it. Nothing touches a live account until I look at it.
We do still use a good bit of bulk uploads and Google scripts as well as the stack has evolved over many years.
On CRM and offline conversions, I agree that Google Ads alone is not enough context. A keyword can convert at 3% in Ads and produce zero qualified leads in HubSpot. We’re pushing offline conversion integration as far as we can right now, currently testing implementation across a 70-account chunk. Better data leads to better outcomes is our motto.
Far from perfect though as most of the friction isn’t technical, it’s getting alignment between the client, their CRM, and their legal team on what data can actually flow back to Ads. But I think this is the future. The agencies that figure out the offline conversion + AI loop first are starting to look very different from the ones still doing monthly PDF reports.
How far am I letting it go overall: read everywhere, write nowhere without the staged approval. Even on negatives, where the cost of a bad add is low, the staging pattern is non-negotiable.
I open-sourced the Claude Code skills AI I’m comfortable sharing on GitHub if you want a look.
Log in to reply.