You open AdSpy, search your competitor's brand name, and you can see their ads. Their creatives, their copy, how long the ad has been running. It feels like intelligence. It feels like you're getting a look inside their operation.
You're not. What you're looking at is the public layer — the same information any random person could find by clicking "Why am I seeing this ad?" on Facebook. Spy tools like AdSpy, Minea, and BigSpy all draw from the same source: Meta's public Ad Library. And the Ad Library, by design, hides the data that actually drives results.
The metrics that separate a winning campaign from a losing one — exact spend, ROAS, CTR, audience targeting, bidding strategy — are completely invisible to every spy tool on the market, no matter what you pay. After working with over 500 ecom brands across Meta, Google, and Shopify intelligence, we've seen this gap create serious blind spots for brands who think they know what their competitors are doing. This article explains exactly what spy tools can and can't show you, and what a real intelligence picture looks like.
What spy tools actually do — and where their data comes from
Spy tools are not magic. They're scrapers. Every major tool — AdSpy, Minea, BigSpy, PowerAdSpy — pulls its data from one of two places: Meta's public Ad Library, or directly from users' Facebook feeds through browser extensions and app data collection.
The Ad Library was launched by Meta in 2018 as a transparency measure, primarily for political advertising. It was later expanded to cover all commercial ads. The idea was simple: anyone should be able to see what ads are running on the platform. What Meta made public was the creative layer — the ad itself. What Meta kept private was everything that determines whether that ad is profitable.
The result is a library where you can see what a competitor is saying, but not how much it's costing them, who they're saying it to, or whether it's working.
The data that spy tools cannot show you — and why it's the most important data
This is the part the tools' marketing pages don't highlight. According to Meta's own Ad Library documentation, here is what is explicitly not available for commercial advertisers:
Actual ad spend
Spy tools cannot show you how much a competitor is spending. Not even an accurate estimate. Meta only discloses spend ranges for political ads and EU-regulated ads — for all other commercial advertisers, spend data is completely withheld from the public record.
When tools claim to show "estimated spend," they are doing reverse arithmetic — trying to infer budget from public engagement data. It is guesswork dressed as data. You could be looking at an ad your competitor is running at €500/day or €50,000/day, and the tool would not be able to tell the difference with any reliability.
ROAS and conversion data
Return on ad spend, conversion rates, cost per purchase, cost per click — none of this exists in any publicly accessible format. As Meta explicitly states, performance metrics are exclusive to the advertiser's own Ads Manager. No third party can access them. This means you have no way of knowing, from a spy tool, whether a competitor's ad that has been running for 90 days is a massive winner or a stubborn campaign someone forgot to turn off.
Audience targeting
Who your competitor is targeting — the interests, behaviors, demographics, lookalike audiences, and custom lists behind their campaigns — is completely private. Meta's privacy architecture ensures that targeting parameters never reach the Ad Library. This is arguably the most strategically valuable piece of data in any competitor's campaign, and it is entirely invisible to every spy tool.
Bidding strategy and structure
Whether a competitor is using lowest cost, cost cap, or bid cap — and at what values — is private. So is their campaign structure: how many ad sets, how they're organized, how budget is distributed across them. The Ad Library shows you an individual ad, not the infrastructure around it.
A/B test results
You can sometimes spot that a competitor is running multiple creatives simultaneously, which suggests testing. But you cannot see which version is winning, by how much, or what they're testing for. You're watching the experiment without access to the results.
What spy tools ARE good for — and where they genuinely help
To be fair: spy tools have real value. The issue is that many brands use them for a job they were never designed to do. Used correctly, tools like AdSpy and Minea are useful for:
Creative research and inspiration
Seeing a large volume of ads across a category, understanding what formats brands are using, and identifying recurring messaging patterns is genuinely useful — particularly for early-stage brands doing market validation or for media buyers looking for creative angles. AdSpy's comment search feature, which lets you see what real users are saying about competitor ads, is one of the more underrated features in this space.
Trend identification and product research
Minea is well-suited for dropshippers looking to identify trending products before they saturate. If an ad for a product has been running for 60 days across multiple markets, that's a signal worth noting. For product sourcing decisions, this kind of surface-level signal has value.
Identifying that competitors are testing or scaling
If a competitor goes from running 5 ads to 40 ads in a short period, that's a signal. You don't know what's working — but you know something is happening. Ad longevity (how long an ad has been running) is one of the more useful proxy signals these tools provide, because ads that keep running tend to be profitable.
Knowing that a product or angle exists in the market
Sometimes the most useful insight from a spy tool is simply knowing that a competitor is actively advertising a specific product, angle, or offer. It validates that the market exists. It doesn't tell you how profitably they're doing it.
The data wall: why no third-party tool can go beyond public creatives
This is not a limitation of the tools themselves — it is a hard architectural constraint set by Meta. Understanding why this wall exists helps you understand why it cannot be bypassed by any subscription tool, regardless of price.
Meta's Marketing API — the interface through which any platform can interact with Facebook's advertising system — only provides access to performance data for an advertiser's own account. By design, it is impossible to use the Marketing API to retrieve another advertiser's ROAS, spend, or targeting data. This isn't a feature Meta forgot to build — it's a deliberate architectural decision.
The only public-facing API Meta provides for competitor research is the Ad Library API. As confirmed in detailed API documentation, this API does not provide click-through rates, conversion rates, or exact spend for commercial advertisers. Spend is provided only as broad ranges, and only for political ads and EU-regulated categories.
Every spy tool on the market — AdSpy at $149/month, Minea at $399/month for their top tier, BigSpy, PowerAdSpy, all of them — is working within this same constraint. They have access to the same public data. The differences between them are in database size, search functionality, and user interface — not in the depth of competitor data they can access.
"You're paying for a better way to search the public library. You're not paying for access to the private files."
What real competitive intelligence looks like — the data that actually drives decisions
There is a meaningful difference between what spy tools provide and what an actual intelligence picture looks like. Here is a direct comparison of the two:
The difference is not incremental. It is the difference between seeing a competitor's shop window and having access to their full Ads Manager dashboard — including every campaign, every ad set, every metric, every targeting decision.
Why the targeting data matters most
Of all the hidden data, audience targeting is arguably the most strategically valuable. Your competitor's creatives are a creative signal. Their targeting is a strategic signal. Knowing exactly which interests, demographics, and lookalike audiences are driving their profitable campaigns tells you something that no amount of creative analysis can: who their actual customer is, at a level of specificity they have spent money and time to discover.
This matters especially if you're entering a category where competitors have been advertising for years. They've done the audience testing. They know which segments convert. Without access to that data, you're starting from scratch. With it, you're compressing months of testing into a starting position that's already close to optimised.
Why spend data changes everything
Knowing what a competitor is spending — not a range, but an exact figure — gives you information that completely changes how you read everything else. An ad that has been running for 90 days at €500/day is a very different signal from the same ad running at €30,000/day. The first might be a slow burn. The second is a serious scaling campaign with strong economics behind it. You cannot understand the signal without the number.
How brands using real intelligence actually use the data
When a brand has direct access to a competitor's account-level data, the applications are meaningfully different from what's possible with spy tools. Here is how the brands we work with at Unlimited Scaling typically use competitive intelligence:
Validating which products are actually generating profit
Seeing that a competitor runs ads for 20 different products tells you very little. Seeing that 3 of those products account for 80% of their spend — and those 3 are generating a 4x ROAS — tells you exactly where their business is making money. This is the kind of product validation that changes sourcing and positioning decisions at a fundamental level.
Identifying profitable audience segments before you pay to find them
If a competitor is consistently pushing €20,000/day into a specific interest-based audience and sustaining a strong ROAS on it, that audience has been validated. You can enter it as a known quantity instead of as a test. The cost of that knowledge is covered in the first few days of not running audience tests that would have failed.
Knowing when a competitor is scaling — and acting before the auction gets expensive
When you can see a competitor's daily spend in real time, you can anticipate auction pressure before it hits your own CPMs. If a major competitor doubles their daily spend in a specific targeting window, your CPMs in that window are about to increase. Knowing it a few days ahead changes how you allocate budget and when you adjust bids.
Understanding what's working in a market you're entering
For brands entering a new category or geography, intelligence on the top 3–5 competitors in that space — their spend, their ROAS by campaign type, their audience breakdown — provides a strategic starting point that would otherwise take 3–6 months of your own testing to develop. Brands we work with routinely compress their go-to-market timeline significantly using exactly this kind of data.
Shopify and Google competitive data — the same gap applies
The data limitation isn't unique to Facebook. The same principle holds across the other two major intelligence surfaces.
Shopify stores
Tools like SimilarWeb and various Shopify store trackers can estimate traffic. But actual Shopify metrics — real-time revenue, conversion rate, average order value by product category, checkout flow data — live inside a private Shopify dashboard. No tool can access them from the outside. What we provide through our competitive intelligence service is direct access to these internal metrics: AOV, conversion rate across traffic sources, revenue per product, and cross-store comparisons with related stores in the same niche.
Google Ads
Google's Keyword Planner and tools like SEMrush or Ahrefs can show estimated keyword volumes and some competitive data. But exact Google Ads spend, campaign ROAS, Quality Scores, and bidding strategies are private — just as they are on Meta. Real Google intelligence means seeing exactly which keywords a competitor is spending on, at what bid, generating what return.
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