Cross-Platform Ad Analysis With AI

The Cross-Platform Data Problem

Every advertising platform is designed to make itself look good. Not through deception — through perspective. Each platform reports from its own vantage point, using its own definitions, and counting conversions through its own attribution lens.

Here is a concrete example. You run a B2B lead generation campaign across Meta and LinkedIn. Both platforms report 50 conversions. Great, they performed equally — right? Not necessarily.

Meta counted any conversion within 7 days of a click or 1 day of a view (its default attribution window). LinkedIn counted conversions within 30 days of a click or 7 days of a view. Some of those conversions were counted by both platforms (a person who saw a Meta ad and later clicked a LinkedIn ad). And neither platform knows about the Google search the person did in between.

The metric names are another layer of confusion. Meta reports "Results" (a configurable metric that depends on your campaign objective). Google reports "Conversions" (which depends on your conversion actions setup). LinkedIn reports "Conversions" (again, dependent on your conversion tracking). TikTok reports "Complete Payment" or "Form Submit" depending on your pixel events. Same business outcome, four different metric names with four different measurement methodologies.

This is not a minor inconvenience. If you cannot accurately compare channels, you cannot accurately allocate budget. And budget allocation is the single highest-leverage decision in multi-channel advertising.

The Metric Translation Challenge

Let us map out the specific translation problems across platforms:

Cost: Meta uses "Amount Spent" in your account currency. Google Ads uses "Cost" but may include fees depending on your billing setup. LinkedIn uses "Total Spent." TikTok uses "Total Cost." The field names differ, but at least the concept is the same — until you manage accounts in multiple currencies.

Conversions: This is where things get complicated. Each platform has its own pixel or tracking tag. Each counts conversions differently. Meta can count view-through conversions (someone saw your ad but did not click, then converted later). Google Search counts click-through only by default. LinkedIn counts both but weights them differently. A single conversion event might be claimed by two or three platforms simultaneously.

Click-through rate: Meta calculates CTR as clicks divided by impressions — but "clicks" can mean link clicks, all clicks (including likes and comments), or outbound clicks. Google calculates CTR as clicks divided by impressions, but only counts ad clicks. LinkedIn includes social actions in its click metrics by default. TikTok separates clicks from profile visits. If you compare raw CTR numbers across platforms, you are comparing different things.

Cost per result: This is the metric most advertisers care about, but it is derived from the metrics above. If the conversion counting differs, the cost-per-conversion comparison is inherently flawed.

The old way to handle this: manually normalize everything in a spreadsheet. Create a "platform-normalized CPA" column. Apply consistent attribution rules. Spend hours reconciling. The result is usually an approximation that took more time to build than it was worth.

How AI Normalizes Metrics Across Platforms

When your ad platforms are all connected through a single MCP server, AI can pull data from every platform in one conversation and handle the normalization for you.

Here is what that looks like in practice:

"Compare cost per lead across Meta, Google Search, LinkedIn, and TikTok for January. Use consistent definitions: only count form submissions as conversions, only count click-through conversions, and show everything in EUR." The AI queries each platform's API, applies your specified filters, and presents an apples-to-apples comparison.

AI handles the translation automatically because it understands each platform's data model. It knows that Meta's "link clicks" is the equivalent of Google's "clicks" (not Meta's "all clicks"). It knows that LinkedIn's "Sponsored Messaging" conversions use a different attribution model than LinkedIn's display ads. It can filter to specific conversion actions on each platform so you are comparing the same business outcome.

The follow-up questions are where it gets really powerful:

"Now add the attribution window context. If I use 7-day click-only attribution for all platforms, how do the numbers change?" This strips away view-through conversions and long attribution windows, giving you a more conservative (and more comparable) view.

"What percentage of Meta's reported conversions came from view-through attribution? Same question for LinkedIn." This reveals how much each platform's numbers are inflated by view-through counting — a critical insight for understanding which platform's numbers you can trust most.

"Normalize for audience size. What is my cost per lead adjusted for the total addressable audience on each platform?" This accounts for the fact that LinkedIn has a smaller but more targeted B2B audience than Meta — a €50 lead on LinkedIn might be more valuable than a €20 lead on Meta.

Channel Comparison: Finding Your Best Performer

Once metrics are normalized, the real analysis begins. "Which channel is best?" is never a simple question — it depends on your objective, your audience, and your stage of the funnel. AI helps you answer it properly.

By objective: "Compare Meta and Google Ads for top-of-funnel awareness. Use CPM and reach as the primary metrics." Then, separately: "Compare Meta and Google for bottom-of-funnel conversions. Use cost per purchase and ROAS." A platform that wins at awareness might lose at conversion, and vice versa. AI can run both comparisons and show you where each platform is strongest.

By audience segment: "For our B2B audience targeting C-suite executives, compare LinkedIn versus Meta. Which delivers better quality leads based on conversion rate from lead to opportunity?" This requires connecting lead quality data with platform performance — a conversation that AI can facilitate when it has access to both platforms.

By creative format: "Compare video ad performance across Meta, TikTok, and LinkedIn. Which platform gives us the best video view rate and cost per completed view?" Video performs very differently across platforms, and knowing where your video content resonates most helps you prioritize creative production.

Diminishing returns by channel: "For each platform, show monthly spend and corresponding ROAS for the last 6 months. Which channels show diminishing returns as spend increases?" This is the most strategic analysis you can do — it reveals your optimal spend level per channel and identifies where additional budget is likely to be wasted.

The old way to do any of these analyses: export data from each platform, build a multi-tab spreadsheet, normalize metrics manually, create comparison charts, and spend half a day on what should be a 30-minute strategic discussion. With AI, you have the comparison in seconds and can iterate on the question until you find the insight that matters.

Budget Allocation: Where to Put Your Next Dollar

Cross-platform analysis is only valuable if it leads to better budget decisions. Here is how to use AI to make those decisions:

Marginal return analysis: "I have €5,000 of additional monthly budget. Based on current performance trends, where will each additional euro generate the most value? Rank the platforms by marginal ROAS." This is the fundamental budget allocation question, and it requires cross-platform data to answer properly.

Reallocation opportunities: "If I moved 20% of my LinkedIn budget to Meta, what would the projected impact be based on current performance?" AI can model the scenario using recent data — not guaranteed predictions, but informed estimates that are better than guessing.

Seasonal adjustment: "Compare platform performance in Q4 versus Q1 across all channels. Which platforms show the biggest seasonal swings?" Some platforms spike during holidays (Meta, TikTok) while others are more stable (Google Search, LinkedIn). Knowing these patterns helps you pre-allocate budget before the seasonal shift hits.

Testing budget framework: "I want to test TikTok Ads with 10% of my total budget. Based on our current Meta and Google performance, what ROAS does TikTok need to achieve to justify increasing its share?" Setting clear performance thresholds for new channels prevents both under-investing (not giving the channel enough budget to learn) and over-investing (pouring money into a channel that is not performing).

The key insight for budget allocation is that you need a cross-platform view to make cross-platform decisions. When each platform lives in its own silo — its own dashboard, its own report — you end up making platform-level decisions instead of portfolio-level decisions. AI gives you the portfolio view that makes budget allocation rational instead of political.

For <a href="/blog/ai-powered-agency-reporting">agencies managing multiple clients</a>, this analysis can be templated. "Run the standard cross-platform comparison for Client X" produces a consistent analysis that takes seconds instead of hours.

Attribution Reconciliation

Attribution is the elephant in every cross-platform analysis room. Every platform over-counts conversions because every platform takes credit for any conversion its ads touched. When you add up conversions across Meta, Google, LinkedIn, and TikTok, the total is higher than your actual conversions.

AI helps you navigate this with honest analysis:

"Sum up reported conversions across all platforms for last month. Compare to our actual total conversions from GA4. What is the overlap ratio?" This gives you a concrete number for how much platforms over-count. A 1.4x ratio means platforms collectively claim 40% more conversions than actually happened.

"For each platform, what percentage of their reported conversions are view-through versus click-through?" View-through conversions are where most overlap lives. A person who saw a Meta ad and then searched on Google gets counted by both platforms. Understanding the view-through ratio per platform helps you discount accordingly.

"If I only count click-through conversions with a 7-day window for all platforms, how does the ranking change?" Applying consistent, conservative attribution rules across platforms gives you a more honest comparison. It usually changes the ranking — platforms that rely heavily on view-through attribution (typically Meta and TikTok) look different when you strip that away.

"Which platforms are most likely to be claiming overlapping conversions?" AI can analyze the timing and conversion paths to identify where double-counting is happening. If a user converts after clicking both a Google ad and a Meta ad, both platforms claim it — but only one conversion actually happened.

Perfect attribution does not exist. But directionally accurate attribution — knowing roughly which channels drive the most incremental value — is achievable with AI. And directionally accurate is vastly better than the default, which is trusting each platform's self-reported numbers.

For deeper attribution analysis, consider connecting <a href="/guides/connect-google-analytics-to-claude">Google Analytics 4</a> alongside your ad platforms. GA4 provides a single-source-of-truth view that can arbitrate between platform claims.

Building a Cross-Platform Analysis Workflow

Here is a practical monthly workflow for cross-platform analysis:

Week 1 — Performance snapshot: "Give me a cross-platform performance summary for last month. Show spend, conversions, CPA, and ROAS for Meta, Google, LinkedIn, and TikTok. Use consistent attribution (7-day click). Highlight the best and worst performing channel per metric." This sets the baseline for the month.

Week 2 — Deep-dive on the weakest channel: Based on the Week 1 snapshot, dig into whichever channel is underperforming. "Meta had the highest CPA last month. Break down by campaign type. Is the problem in prospecting, retargeting, or both? Compare to the same month last year." Decide whether to optimize, reduce budget, or hold steady.

Week 3 — Budget reallocation modeling: "Based on the trends from the first three weeks, model what would happen if I shifted 15% of budget from the lowest-ROAS channel to the highest-ROAS channel." Use the model to inform your next month's budget split.

Week 4 — Attribution reconciliation: "Compare total platform-reported conversions to GA4 actual conversions. Which platform is over-counting the most? How should I adjust my per-platform targets?" This keeps your performance benchmarks honest.

To get started, connect your ad platforms at <a href="https://app.adsuperpowers.ai">app.adsuperpowers.ai</a>. The free tier covers Meta, Google Ads, and GA4. For LinkedIn and TikTok, the Pro plan gives you all six platforms through a single MCP connection — one URL, all your data, instant cross-platform analysis.

Frequently Asked Questions

Related Articles

Setup Guides