Most PPC teams aren’t debating whether to use AI anymore. The question is which one, and whether the difference is worth switching, paying for another subscription, or convincing your team to change tools.

Claude and ChatGPT are the two most common answers.

Both work with Optmyzr MCP and can pull live account data, run audits, and generate strategies without a single CSV export. So we ran the same prompts through both, with the same account and data.

Not to find a winner, but to find out where they differ, and what that means for how PPC managers and agency teams work day to day.

Here’s what came back.

Test-1: Month-over-month account audit

We started with a single prompt asking both AIs to pull four months of account data, flag what’s trending wrong, identify the top campaign drivers, and return a verdict.

What ChatGPT returned

The analysis was accurate and thorough.

ChatGPT identified the right campaigns, flagged the same zero-ROAS budget drain, correctly read ROAS improving from 915% in January to 1,807% in April, and landed on the same verdict.

The issue was that it came back looking like a wall of text.

Accurate, thorough, and about as exciting to read as a terms and conditions page. It was fine for someone who just wants the answer, but not the best for sharing with a client or presenting in a team meeting.

So we asked it to make the response more visually compelling.

ChatGPT did produce charts this time: trend bars, campaign swing breakdowns, KPI scorecards.

While the data was all there, it was basic with plain text-style bar charts and minimal formatting, nothing you’d drop into a client report without reworking it first.

What Claude returned

Claude came back with metric cards, trend charts, and a color-coded breakdown of what was improving and what wasn’t- all in the first response, without being asked.

The kind of output you could screenshot and drop into a Slack message to your team without touching it.

Check out the full audit results here!

So what’s the actual difference here?

The analysis was almost identical. Both AIs called the same Optmyzr MCP endpoint, got back the same data, and reached the same conclusions.

The difference is purely in how the output landed.

Claude led with visuals and ChatGPT led with text and got to visuals when pushed. And even then, Claude’s charts were richer and more ready to use.

For anyone who shares their work: agency teams, in-house managers reporting to stakeholders, anyone who’s ever had to make a Google Slides deck at 5pm on a Friday, that gap is real.

Test-2: Alerts audit and gap analysis

Most PPC accounts have alerts that were configured once, never updated, and are now watching for problems that aren’t the biggest risks anymore.

So we asked both AIs to audit what was already running, find what was missing, and fix it.

What ChatGPT returned

ChatGPT listed every active alert, flagged which ones were already firing, and cross-referenced the existing setup against the performance data to find the gaps.

Its answer was two things.

The account’s search impression share had been climbing steadily (44% in January to 50% in April), but nothing was watching it. If it dropped, nobody would know until a manual check caught it.

And conversions had already fallen 22% from March to April, but the existing alert only tracked that at account level. A campaign-level alert would tell you which campaign caused the drop the moment it happened.

Both recommendations made sense, and both alerts were created.

What Claude returned

Claude audited the same eight alerts, identified the same fired cost alert showing spend running 68% above target on one Search campaign, and then went somewhere different.

When looking for gaps, it found something more specific.

Gap one: the account was tracking conversion volume but not cost per conversion. Those sound similar but they’re not. Volume can hold steady while CPA climbs, and nothing was set up to catch that.

Gap two: One campaign in the account was converting at 3,834% ROAS (every euro spent returning roughly 38 euros in revenue). But it was only winning 1 in every 4 auctions it was eligible for, and there was zero alert watching its impression share.

If that number dropped further, nothing would flag it. Claude flagged it, explained why it mattered, and created an alert specifically for that campaign.

The comparison

Both AIs completed the task, found real gaps, and created new alerts without leaving the conversation. However, they didn’t create the same alerts.

Both AIs had clear reasoning behind their choices and neither is wrong.

The best way to figure out which approach fits your account is to try both and see which set of recommendations makes more sense.

💡Also Read: The PPC Stress Test: ChatGPT vs. Claude vs. Gemini (and Where Optmyzr Wins)

Test-3: Generating a Rule Engine strategy

Building a Rule Engine strategy from scratch usually means starting with a template, configuring conditions manually, and figuring out which campaigns to target before you’ve even written a single rule.

We wanted to skip all of that and see if both AIs could use the audit context they already had to build something specific and useful, without being told what to create

What ChatGPT returned

ChatGPT went back to the audit findings and targeted the issue of budget leaking into campaigns that weren’t returning results. It built a three-rule strategy around that:

Rule 1 targets campaigns that are enabled, spending over €100 in the last 30 days, and generating zero conversions. Instead of pausing them automatically, it applies a label:

Review: Wasted Spend / Low ROAS, so you can investigate before taking action.

Rule 2 catches campaigns spending over €100 but returning less than 200% ROAS (same label and review-first approach).

Rule 3 is a conversion decline detector. It flags any enabled campaign where conversions dropped more than 20% versus the previous period while cost stayed flat or increased

Along with building these rules, it ran them against the account and surfaced three campaigns that were flagged. So you could see the strategy working before deciding whether to keep it.

📝Note: Before anything goes live, ChatGPT shares a direct preview link in Optmyzr where you can review every condition, and edit any thresholds before running it.

Nothing touches your account until you approve it.

What Claude returned

Claude also went back to the audit findings and landed on the same core problem. But the strategy it built took a different stance.

Claude built two rules with direct actions.

Rule 1 targeted zero-conversion campaigns and reduced their bids by 30%, a softer intervention than pausing, but an actual change rather than just a flag.

Rule 2 identified campaigns running above 800% ROAS but below 55% impression share (high performers being starved of budget), and increased their daily budget by 20%.

Every campaign touched by either rule was labelled automatically so you could track what changed and when. Just like ChatGPT, Claude also shared a direct preview link in Optmyzr where you can see which campaigns would be affected, what actions would be taken, and adjust anything before confirming.

The strategy is built, but you’re still in full control of whether and how it runs.

Test-4: Cross account analysis

One of the biggest advantages of working in an agency is that you see patterns your clients can’t. You’re across ten, twenty, thirty accounts at once, and sometimes what looks like an isolated problem on one account is something happening everywhere.

So we gave both AIs a portfolio of five accounts: one real account pulled live via Optmyzr MCP, four illustrative client accounts with dummy data across different industries, to run a cross-account analysis.

What ChatGPT returned

ChatGPT came back with a detailed account-by-account breakdown, a clear urgency ranking, and a patterns section that pulled back to look at the portfolio as a whole.

The urgency ranking was accurate.

• B2B SaaS at the top (€28,300 spent across four months with zero conversions)
• Legal Services second, spending more every month while conversions and ROAS collapsed simultaneously
• The real account third, E-commerce Fashion and HVAC at the bottom as the healthy performers.

The patterns section also identified three things worth paying attention to:

• Efficient scaling versus inefficient scaling splitting the portfolio into two clear groups
• Search IS as a leading warning sign showing up before conversion losses became obvious
• B2B SaaS needing conversion tracking verified before any optimization decisions could be made.

All correct and useful things to bring to client conversations, presented as tables and text.

What Claude returned

Claude reached the same urgency ranking and flagged the same core problems. But it came back looking completely different.

Instead of tables and paragraphs, it returned a portfolio dashboard with

• color-coded account cards
• urgency tags
• spend bars showing month-by-month trend at a glance
• insight callouts underneath each account explaining what the numbers meant.

The kind of output you could walk into a team meeting with without touching it first.

The patterns section went further. Beyond the efficient versus inefficient scaling split, Claude flagged two additional things an agency would want to know.

One: conversion tracking was the biggest unresolved issue across the portfolio, and it wasn’t just B2B SaaS. The real account also had campaigns spending every month with zero tracked conversions.

Two different clients, same underlying problem.

For an agency, that’s a useful pattern. It means conversion tracking is worth auditing proactively across the book, not just fixing reactively when a client complains.

Two: spring seasonality lifted three accounts simultaneously in March and April- fashion, HVAC, and the real account all showed the same uptick.

Claude flagged that seasonal budget planning ahead of Q1 would have captured more of that window. Again, not a single-client insight, and something an agency could act on across multiple relationships at once.

The comparison

Both AIs ranked the accounts correctly and landed on the same urgent problems. The core analysis wasn’t meaningfully different.

Where they diverged was in two places.

• The output format: ChatGPT returned a thorough text analysis and Claude returned a dashboard. For an agency where analysis regularly gets shared with account managers, team leads, or clients, that difference has real practical implications.
• Pattern depth: ChatGPT spotted the portfolio-level trends and Claude spotted those and two additional patterns (the cross-account conversion tracking issue and the seasonal opportunity) that an agency could use to have better proactive conversations with multiple clients at once.

Whether that additional layer of pattern recognition is consistently repeatable across different portfolios and prompts is worth testing on your own accounts.

🤖 Dive deeper in our AI capabilities: How to Use Optmyzr Sidekick: 38 Real PPC Use Cases Across Reporting, Optimization & Budgeting

Test-5: Review and apply account optimization recommendations

Every PPC account has a to-do list. The problem is usually figuring out where to start: what’s urgent, what can wait, and what looks important but isn’t.

We asked both AIs to pull all optimization recommendations from Optmyzr, make sense of them, and hand back something actionable.

What both AIs returned

Both ChatGPT and Claude came back with a prioritized to-do list grouped by category, a clear explanation of why each recommendation mattered, and direct links to review and apply each fix inside Optmyzr.

The most urgent finding was the same across both: several Brand campaign ad groups were converting profitably under Target ROAS but losing between 73% and 95% of eligible impressions because the ROAS targets were set too aggressively.

Both AIs flagged it, explained it clearly, and linked directly to the recommended fix in Optmyzr where you can review the specific ad groups, see the suggested target adjustments, and apply them when you’re ready.

So, Claude or ChatGPT for PPC?

After five tests with the same account, Optmyzr MCP, and prompts, we could see that it depends on what you need from the output.

Where Claude had a consistent edge

Claude led with visuals every time. The audit came back as metric cards and trend charts without being asked. The cross-account analysis came back as a portfolio dashboard. For anyone who regularly shares work with clients, team leads, or stakeholders, that difference shows up before you’ve even read the analysis.

Where ChatGPT held its own

ChatGPT’s analysis was accurate across every single test. It ranked accounts correctly, flagged the right problems, and built a Rule Engine strategy that was arguably more conservative and safer for accounts you’re less familiar with. The downloadable PPT from the audit prompt is genuinely useful for anyone who needs to package results for a client presentation quickly.

And on optimization recommendations, both AIs returned essentially the same output, because the recommendations came from Optmyzr’s engine, not from the AI itself.

The thing that didn’t change

Across all five tests, the underlying PPC intelligence was the same. Both AIs called the same Optmyzr MCP endpoint and got back the same data, the same recommendations, the same account structure. The analysis quality floor was identical because what both AIs were connected to was identical. That’s the part worth remembering.

AI is the interface, and Optmyzr is the engine.

Whichever one you’re already using, connecting it to Optmyzr MCP is what makes it genuinely useful for PPC work, not the AI on its own.

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