How to Compare ROAS, CAC, and CPA Across Google, Meta, LinkedIn, and TikTok Using AI

Summary

Yes — you can compare ROAS, CAC, and CPA across Google Ads, Meta Ads, LinkedIn Ads, and TikTok Ads from a single Claude or ChatGPT conversation through Adspirer. Connect all four platforms once via OAuth, then ask plain-English questions like “which platform gave me the best ROAS last month?” and get a ranked answer with actual numbers from your live accounts — no spreadsheet exports, no manual consolidation.

If you run ads on more than one platform, you’ve done the spreadsheet dance. Export last month’s Google Ads data. Switch tabs, export Meta. Log into LinkedIn Campaign Manager, export. Log into TikTok Ads Manager, export. Open a blank sheet, clean the column names so they match, build a pivot table, and finally — 45 minutes later — see a comparison that’s already stale.

This is one of the most reliably recurring complaints in r/PPC. “How are you all doing cross-platform reporting?” comes up monthly. The top answers are always some version of: manually, in a spreadsheet, or paying $300/month for a reporting tool that still requires you to interpret the output.

The underlying problem is that there is no native way to pull Google Ads, Meta, LinkedIn, and TikTok data side by side. Each platform is a silo with its own attribution model, its own definition of a conversion, and its own UI that was designed to keep you inside it — not comparing it to competitors. Getting an honest cross-platform performance read requires either a lot of manual work or a third-party tool that can talk to all four APIs simultaneously.

Adspirer is an MCP server that does exactly that. Connect it to Claude or ChatGPT, link all four ad platforms via OAuth, and you can ask cross-platform performance questions in plain English and get answers pulled from live account data — in the time it used to take to log into a second platform.

Info

Want to try this now? Adspirer connects Claude and ChatGPT to Google Ads, Meta Ads, LinkedIn Ads, and TikTok Ads — no API setup, no spreadsheets. Setup takes 2 minutes.

Start comparing platforms for free →

Why Cross-Platform Comparison Is Hard Without AI

The short answer: each platform speaks a different language and has strong incentives to not translate.

Attribution windows don’t match. Google Ads defaults to a 30-day click attribution window. Meta defaults to a 7-day click and 1-day view window. LinkedIn and TikTok have their own defaults, which differ by objective. This means the same purchase can be claimed by Google (30-day window) and Meta (7-day window) simultaneously — and both are “right” by their own accounting. A naive side-by-side comparison ignores this and produces a number that’s misleading.

Conversion events aren’t standardized. On Google, “conversion” might mean a form fill. On Meta, it might mean “Purchase” events fired by your Pixel. On LinkedIn, it might mean Lead Gen Form completions. On TikTok, it might be a custom event. Comparing CPA across platforms is only valid if you’re comparing the same conversion type — which requires knowing how each platform is tracking it before you pull numbers.

The interfaces were designed to keep you in-platform. Google wants you to see your Google numbers and feel good about them. Meta wants you to see Meta numbers. None of them offer a “here’s how we stack up against the other platforms you use” view, because that comparison might work against them.

Manual consolidation introduces lag and error. By the time you’ve exported, cleaned, and merged four data sources, you’re analyzing last week’s numbers with this week’s budget decisions. A campaign that was underperforming on Monday has often already accumulated another $500 in spend by the time you catch it Friday.

The AI-plus-MCP approach collapses this. When Claude or ChatGPT has live API access to all four platforms simultaneously, it can pull the numbers, apply consistent definitions, and return a ranked comparison in seconds — with caveats about attribution differences surfaced in the answer rather than buried in footnotes you’d have to write yourself.

Set Up Once, Compare Forever

The one-time setup connects each ad platform to Adspirer and then adds Adspirer to your AI tool of choice. After that, cross-platform comparisons are a single prompt away.

Connect Your Ad Platforms to Adspirer

Sign up at adspirer.ai and connect your ad platforms via OAuth. You can connect Google Ads, Meta Ads, LinkedIn Ads, and TikTok Ads — no API keys or developer credentials required. Adspirer handles the authentication. Connect as many or as few as you actively run.

Add Adspirer to Claude or ChatGPT

Claude: Go to Customize → Connectors, click Add custom connector, and enter https://mcp.adspirer.com/mcp. Claude auto-discovers all available tools. See the full Claude setup guide.

ChatGPT: Open ChatGPT → Explore GPTs and search for Adspirer. Install and authenticate. See the full ChatGPT setup guide.

Verify All Four Platforms Are Connected

Start a new conversation and run this check:

List all my connected ad platforms and show me one active campaign from each.

You should see platforms, account names, and campaign examples. If a platform is missing, return to your Adspirer dashboard and connect it before running comparisons — partial platform data produces incomplete rankings.

Run Your First Cross-Platform Query

Use the prompts in the next section. Claude and ChatGPT pull live data from each connected platform simultaneously and return a unified comparison — no spreadsheet required.

The 5 Cross-Platform Comparison Prompts

These five prompts cover the core performance metrics that matter for budget allocation decisions. Each one asks Claude to pull data from all connected platforms and rank or compare them in a single response.

1. ROAS Ranking Across All Platforms

Which platform gave me the best ROAS in the last 30 days? Rank all four — Google, Meta, LinkedIn, and TikTok — from highest to lowest ROAS.

For each platform, show me: total spend, total conversion value, calculated ROAS, and number of conversions. Flag any platform where ROAS is below 1.0 (spending more than returning). Note if any platform has fewer than 10 conversions in the period — small sample sizes make ROAS comparisons unreliable.

What to expect: Claude returns a ranked list with the dollar figures underneath each platform’s ROAS. It will also flag data reliability issues — a platform with 3 conversions in 30 days has a ROAS number that’s statistically meaningless, and a good prompt surfaces that caveat rather than letting you act on a misleading number.

2. CAC Comparison by Platform and Conversion Type

Compare my customer acquisition cost across Google, Meta, LinkedIn, and TikTok for the last 90 days. Which platform has the lowest CAC for my primary conversion type?

For each platform: total spend, total new customers or leads acquired, calculated CAC, and the conversion event being tracked. If platforms are tracking different conversion events (purchases vs. leads vs. form fills), flag that so I can compare apples-to-apples on the ones that match.

What to expect: This prompt often produces the most useful insight in cross-platform analysis, because it forces the conversion event comparison that manual spreadsheets usually skip. Claude will surface whether LinkedIn is tracking lead gen form completions while Google is tracking purchase events — a comparison that looks like a win for Google but is actually measuring different things.

3. CPA Trends Week-Over-Week Across All Platforms

Show me CPA trends week over week across all platforms for the last 8 weeks. For each platform, show CPA by week and tell me: is CPA trending up (getting worse), down (getting better), or flat? Which platforms are improving and which are deteriorating?

Flag any week where CPA spiked more than 30% from the prior week, and tell me what else was happening that week (budget changes, audience changes, if visible in the data).

What to expect: Trend data over 8 weeks is where cross-platform comparison earns its keep. A platform with the second-highest current CPA that has been improving 15% week-over-week is a different investment decision than a platform with the best current CPA that’s been deteriorating for a month. This prompt surfaces the trajectory, not just the snapshot.

4. Budget Allocation Recommendation Based on Last Month

I have $10,000 in additional monthly budget to allocate across my ad platforms. Based on last month’s ROAS and CAC, which platform should get the most? Give me a specific dollar allocation recommendation across Google, Meta, LinkedIn, and TikTok.

Walk me through the logic: what each platform’s efficiency numbers suggest, where you expect the best marginal return on additional spend, and any platform where adding budget is unlikely to improve results without other changes first (audience saturation, creative fatigue, etc.).

What to expect: This is the decision-support prompt. Claude won’t blindly recommend putting everything into the highest-ROAS platform — it will surface constraints, like a Meta campaign that’s already showing audience saturation at current scale, or a LinkedIn campaign that needs creative refresh before more budget will help. The AI surfaces the data and the relevant context. You make the call.

5. Efficiency Comparison — CPM, CPC, and CTR

Compare my CPM, CPC, and CTR across Google, Meta, LinkedIn, and TikTok for the last 30 days. Where am I paying the most for attention (CPM), the most per click (CPC), and getting the lowest engagement rate (CTR)?

Rank each metric separately and flag any outliers — platforms where a metric is more than 2x the average across all platforms. Tell me what the typical benchmark range is for each platform type so I can see where I’m above or below industry norms.

What to expect: Efficiency metrics tell a different story than outcome metrics. LinkedIn CPM is structurally 3–5x higher than Meta — that’s not a sign of underperformance, it’s the price of reaching a B2B audience. Claude will return the raw numbers and flag which differences are platform-structural versus which represent actual inefficiency in your specific campaigns.

Platform-by-Platform Context

Cross-platform numbers need platform-specific context to be useful. A ROAS of 3.0 means something different on Google versus LinkedIn. Here’s what to watch for on each platform when you’re reading comparison data.

Typical ROAS range: 2.0–8.0 for e-commerce, 0.5–3.0 for lead gen (where ROAS is measured in lead value, not direct revenue).

What makes Google different: Search intent is the structural advantage. People searching “buy running shoes” are much further down the funnel than someone scrolling a feed. This means Google CPA is often lower than social platforms for purchase-intent campaigns — but the comparison breaks down if your Google campaigns include broad match terms capturing research intent rather than purchase intent.

What to watch: Search term reports. A Google ROAS or CPA figure that looks strong is only meaningful if your search terms are actually matching purchase-intent queries. Ask Claude to pull the top 20 search terms by spend alongside your ROAS — a campaign with 4.0 ROAS driven by branded terms is a different story than 4.0 ROAS on competitive non-brand terms.

Attribution default: 30-day click, data-driven model (for accounts with sufficient conversion volume).

Typical ROAS range: 1.5–5.0 for e-commerce, lower for awareness objectives. Varies heavily by product price point and creative quality.

What makes Meta different: Audience targeting at scale and creative iteration speed. Meta’s strength is upper-funnel reach and retargeting — a platform where you can test 10 creative variations in a week and have statistically meaningful data on which performs. This means Meta CAC comparisons are only valid when you’re comparing similar funnel stages.

What to watch: Frequency and creative fatigue. A Meta campaign showing declining ROAS over 8 weeks is often a creative problem, not an audience problem. Before writing off Meta as a lower-ROAS platform, check whether you’ve refreshed creative in the last 60 days. Ask Claude to show ROAS trend alongside average ad frequency to see if fatigue is the culprit.

Attribution default: 7-day click, 1-day view — which means Meta claims credit for purchases that happened within 1 day of an impression, even with no click. This is why Meta ROAS often looks better than Google ROAS in a naive side-by-side: it has a more aggressive attribution window.

Typical ROAS range: Hard to calculate directly — LinkedIn is predominantly a B2B lead gen platform where “conversion” is usually a lead, demo request, or content download. CPA (cost per lead) is the more useful metric; typically $50–$200+ depending on audience specificity.

What makes LinkedIn different: The B2B audience precision. Job title, company size, seniority, and industry targeting is genuinely better than any other platform for reaching specific professional profiles. The trade-off is that CPM is structurally 3–6x higher than Meta or TikTok — you’re paying a premium for audience quality.

What to watch: Lead quality, not just lead volume. LinkedIn’s cost per lead looks terrible compared to Meta’s cost per lead until you look at SQL conversion rates downstream. A LinkedIn lead at $180 that converts to a customer at 25% is more valuable than a Meta lead at $40 that converts at 5%. Ask Claude to surface the LinkedIn numbers alongside any offline conversion data you’ve synced to make this comparison honest.

Attribution default: 30-day click, 7-day view.

Typical ROAS range: 1.0–4.0 for direct-response e-commerce, variable for awareness. TikTok performance varies more by creative than any other platform — the right short-form video can outperform by 10x, the wrong one is money down the drain.

What makes TikTok different: Discovery intent. Users aren’t searching for products; they’re discovering them. This means TikTok works differently from Google Search — it’s closer to Meta in funnel position, but with an even heavier dependency on creative format (native, entertainment-first video). ROAS benchmarks from Google or LinkedIn don’t apply directly.

What to watch: Creative-level performance variance. A campaign-level TikTok ROAS of 1.8 might be hiding one ad with 5.0 ROAS and three ads with 0.3 ROAS. Ask Claude to break down performance by individual creative to see what’s actually working. Scaling TikTok spend without this breakdown is the most common waste pattern on the platform.

Attribution default: 7-day click, 1-day view (similar to Meta).

What to Do With the Comparison

Getting the ranked comparison is step one. Acting on it is where the budget impact happens. These prompts help you move from “I now know which platform is performing best” to “I’ve made the changes.”

Reallocate Budget from Bottom to Top Performer

Based on last month’s ROAS comparison, I want to shift 20% of budget from my lowest-ROAS platform to my highest. What’s the exact dollar amount to move, and what should I change the daily budgets to on each platform?

Show me the current budgets, the proposed new budgets, and the expected ROAS impact based on current efficiency. List the changes before making any of them.

Pause Bottom-Performing Campaigns Before Next Budget Cycle

Identify the three lowest-ROAS campaigns across all my platforms in the last 30 days. For each one: show me the campaign name, platform, current daily budget, ROAS, and total spend in the period.

For campaigns below 1.0 ROAS (spending more than they return), recommend whether to pause, restructure, or give it more time based on how long the campaign has been running. List the recommendations and wait for my approval before pausing anything.

Scale the Winning Platform Without Hitting Saturation

My top platform by ROAS last month is [platform]. I want to increase its budget by 40% without hitting audience saturation or diminishing returns. What’s the current budget, and what signals would indicate I’m pushing past the efficient spend range for this platform and campaign structure?

Tell me what to watch in the first two weeks after the budget increase, and set a reminder prompt I can run in 14 days to check whether ROAS held.

The Attribution Caveat

Cross-platform performance numbers are not perfectly apples-to-apples. This isn’t a weakness of the AI approach — it’s an inherent feature of how ad platforms work. Understanding the gaps helps you interpret the comparison correctly.

Attribution windows inflate platform credit. Meta’s 1-day view attribution means Meta claims credit for a purchase if the user saw your ad (without clicking) and then purchased within 24 hours. Google’s 30-day click window means Google claims credit for any purchase within a month of a click. The same customer can be claimed by both platforms simultaneously. When you compare ROAS across platforms, you’re seeing each platform’s version of what it contributed — not a unified, deduplicated truth.

The practical implication: Cross-platform ROAS comparison is most useful for directional decisions, not for precise attribution accounting. If Google shows 6.0 ROAS and TikTok shows 0.8 ROAS over 90 days across meaningful spend, that’s a real signal worth acting on — even if the exact numbers aren’t perfectly comparable. Where it breaks down is in marginal decisions, like choosing between a 3.1 ROAS and a 2.9 ROAS platform — that’s within the attribution noise.

Conversion events must match. If Google is counting lead form submissions and LinkedIn is counting Lead Gen Form completions and Meta is counting Pixel purchase events, you’re not comparing the same thing. The CAC and CPA prompts above are designed to surface this — Claude will flag when platforms are tracking different conversion events so you can exclude or adjust before acting on the comparison.

View-through attribution is especially hard to compare. TikTok and Meta both claim significant view-through credit. Google Search does not. If you’re comparing CPA across platforms and not accounting for view-through attribution differences, your Google CPA will look worse than it structurally is relative to social platforms.

The honest framing: AI surfaces the data as each platform reports it. You interpret it with the knowledge that platform-reported numbers have built-in attribution bias. For the large directional decisions — which platform is clearly performing better, where to cut, where to scale — the comparison is useful and actionable. For precise fractional allocation, you need incrementality testing.

Warning

Don’t optimize for the metric that looks best — optimize for the metric that maps to your actual business goal. A B2B company comparing ROAS across Google and LinkedIn is comparing purchase revenue efficiency against lead gen efficiency. Those aren’t the same thing. Before running the comparison prompts, decide what “performance” means for each platform in your specific funnel. AI surfaces the data accurately; you supply the business context.

FAQ

Do I need to have all four platforms connected to use these prompts?

No — Adspirer works with however many platforms you’ve connected. If you only run Google and Meta, the cross-platform prompts will compare those two. If you add LinkedIn or TikTok later, they’ll appear in subsequent comparisons automatically. The prompts are written for four platforms but work for any combination.

Why does my Meta ROAS always look higher than Google in these comparisons?

Meta’s default attribution window includes a 1-day view-through, which means Meta claims credit for purchases where a user saw an ad (without clicking) and purchased within 24 hours. Google Search attributes only on clicks. This structural difference inflates Meta’s reported ROAS relative to Google in most side-by-side comparisons. Neither number is wrong — they’re just measuring different things. To normalize them, ask Claude to filter to click-only attribution on Meta and compare against Google’s click-based numbers. The gap typically narrows substantially.

Can Claude actually make budget changes across platforms, or just read the data?

Both. Through Adspirer, Claude and ChatGPT can read live performance data from all four platforms and execute changes — adjusting budgets, pausing campaigns, modifying bids — with your explicit confirmation before anything runs. Every write operation requires your approval first. Claude will tell you exactly what it’s about to change, wait for your go-ahead, and then apply the change. Nothing executes silently. See the capabilities documentation for the full list of supported operations per platform.

How is this different from a reporting tool like Supermetrics or Databox?

Reporting tools pull data into dashboards you read. Adspirer connects an AI to the same data so you can ask questions about it in plain English and act on the answers in the same conversation. The difference isn’t just interface — it’s that the AI can interpret the comparison, flag attribution caveats, identify the cause of performance differences, and then make the changes you agree on, without you switching to four different platform UIs. Reporting tools show you the numbers. Adspirer lets you do something with them without leaving the conversation.

What if my conversion events are tracked differently across platforms?

That’s the most important thing to resolve before trusting a cross-platform CPA comparison. Ask Claude: “What conversion event is each platform tracking, and are they measuring the same thing?” Claude will pull the conversion event names and types from each platform’s configuration. If Meta is tracking Pixel purchase events and LinkedIn is tracking Lead Gen Form completions, your CPA comparison is cross-funnel, not cross-platform — and Claude will flag that so you can decide whether to exclude one platform from the comparison or add context about average lead-to-purchase rates to make it meaningful.

How often should I run cross-platform performance comparisons?

Monthly for strategic allocation decisions (where to shift meaningful budget between platforms), weekly for trend monitoring (is anything deteriorating fast enough to require attention this week), and any time you’re about to make a significant budget change. The 8-week CPA trend prompt is particularly useful before quarterly planning — it shows trajectory, not just where things stand today, which is the right input for forward-looking budget decisions.

Conclusion

Cross-platform performance comparison is one of the highest-value analytical tasks in performance marketing — and one of the most consistently underdone, because doing it manually takes the better part of an hour every time you need to answer a question that should take thirty seconds.

The answer to “which platform is actually performing best?” should not require four browser tabs, a spreadsheet, and forty-five minutes. It should require one conversation.

Connecting Claude or ChatGPT to all four ad platforms through Adspirer makes cross-platform ROAS, CAC, and CPA comparison a prompt, not a project. Run the five prompts above once to establish your current performance baseline. Run the trend prompt monthly to watch trajectory rather than snapshots. Use the action prompts to move budget from what’s not working to what is — without leaving the conversation to log into four separate platform UIs.

The AI surfaces the data and flags the attribution caveats that make the comparison honest. You make the budget decisions. That’s bounded automation: useful, transparent, and fully under your control.

Info

Connect all four ad platforms to Claude or ChatGPT in two minutes. No API setup, no developer credentials, no spreadsheets. Start free — no credit card required.

Start comparing platforms →

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How to Compare ROAS, CAC, and CPA Across Google, Meta, LinkedIn, and TikTok Using AI

Why Cross-Platform Comparison Is Hard Without AI

Set Up Once, Compare Forever

Connect Your Ad Platforms to Adspirer

Add Adspirer to Claude or ChatGPT

Verify All Four Platforms Are Connected

Run Your First Cross-Platform Query

The 5 Cross-Platform Comparison Prompts

Platform-by-Platform Context

What to Do With the Comparison

The Attribution Caveat

FAQ

Conclusion

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