Service · Ad intelligence data

Ad Intelligence & Creative Data

From public ad libraries, with spend estimates deliberately excluded.

Ad intelligence data scraping is the automated collection of publicly disclosed advertising data from platform ad transparency libraries — active and historical creative, ad copy, run dates, platforms, advertiser identity and landing pages. Spend and impression estimates are excluded because platforms do not publish them for commercial advertising.

Platform ad libraries are the most under-used public dataset in marketing. Every active ad your competitor is running is disclosed, with creative and run dates. What is not disclosed is what they paid, and no amount of scraping produces that.

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ad_creative.jsonl LIVE FEED

{"ad_id":"aw-ad-771204881", "library":"platform_ad_library", "advertiser_name":"Example Brand Ltd", "advertiser_verified":true, "country_shown":["GB","IE"], "first_seen":"2026-06-14", "last_seen":"2026-08-05", "days_running":52, "still_active":true, "format":"video","variants":6, "headline":"Summer edit now 40% off", "body_text_hash":"sha1:88f2…4c", "cta_label":"Shop Now", "landing_domain":"example-brand.com", "landing_path":"/collections/summer-edit", "creative_archived":true, "spend_estimate":"not_collected", "impressions":"not_published_commercial"} {"ad_id":"aw-ad-771204902", "ad_category":"political_or_issue", "spend_range_published":"GBP 5,000-9,999", "impressions_range_published":"100k-500k", "note":"ranges disclosed by platform for this category only"}

2 of 8,412,900 ad recordscreative archived 97.4% · schema v2.8

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Written and reviewed by the Actowiz Solutions Data Engineering team Published 5 August 2026 Last updated 5 August 2026 Reading time 11 min

Key facts at a glance

What it is: Managed collection of publicly disclosed advertising data from platform ad transparency libraries
Sources: Platform ad libraries and ads transparency centres that publish active advertising
Creative: Ad copy, format, variant counts and archived creative references
Timing: First seen, last seen, days running and active status
Landing: Landing domain and path, so campaign destination is measurable
Commercial spend: Not collected — platforms do not publish it for commercial advertising
Political ads: Where platforms publish spend and impression ranges, we capture the published ranges
Who it's for: Brand marketing, competitive intelligence, agencies and researchers

Public librariesas the only sourceno panels, no models

97.4%creative archivedwith change detection

Days runningcomputed from library datesa real longevity signal

Zeromodelled spend estimatesfor commercial ads

Key takeaways

What it is: Managed collection of publicly disclosed advertising data from platform ad transparency libraries
Sources: Platform ad libraries and ads transparency centres that publish active advertising
Creative: Ad copy, format, variant counts and archived creative references
Timing: First seen, last seen, days running and active status
Landing: Landing domain and path, so campaign destination is measurable
Commercial spend: Not collected — platforms do not publish it for commercial advertising

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is ad intelligence data, and why exclude spend?

Ad intelligence data scraping is the automated collection of what advertising platforms publicly disclose about active advertising: the creative itself, ad copy, formats, the countries an ad was shown in, when it started and last appeared, the advertiser, and where it links to.

These libraries exist because of transparency regulation and platform policy, and they are genuinely public. That makes this one of the more comfortable collection categories we run — and one of the most under-used, because most teams do not realise how much is disclosed.

What is disclosed, and what is not

Disclosed for commercial ads: creative, copy, format, run dates, active status, advertiser identity, countries shown, and often the landing destination.
Disclosed only for political and issue advertising: spend ranges and impression ranges, because regulation requires it. We capture the published ranges as published.
Not disclosed for commercial ads: spend, impressions, reach, targeting parameters, audience definitions or performance.

Why we exclude spend estimates

Vendors do sell commercial spend estimates. Those are models — derived from panels, sampled exposure and proprietary assumptions — not observations. Presenting a model inside a dataset labelled as ad library data is the same problem as app revenue estimates or ESG scores, and we take the same position.

spend_estimate ships as a constant not_collected, and impressions as not_published_commercial, so the exclusions are visible in the data rather than looking like gaps.

What creative longevity tells you instead

The observable proxy for performance is how long an ad runs. Advertisers pause creative that does not work. An ad running fifty-two days with six variants is being invested in; one that ran four days and stopped was probably a test that failed. That is not spend, but it is evidence, and it is fully observable.

What we collect

Six categories of ad intelligence data

Creative longevity and variant counts are where the analytical value sits. Landing page capture is the most under-used.

Creative & copy

The ad itself.

Headline and body copy as published
Format: image, video, carousel, text
Variant counts within an ad set where disclosed
Call-to-action label
Creative reference for archival and change detection

Run timing & longevity

The observable performance proxy.

First seen and last seen dates
Days running, computed
Active or stopped status
Restart detection where an ad returns
Longevity distribution by advertiser

Advertiser identity

Who is running it.

Advertiser name as disclosed
Verification status where shown
Page or account identity
Advertiser portfolio across libraries
New advertiser detection in a category

Geography & targeting disclosure

Only what is published.

Countries the ad was shown in, where disclosed
Regional disclosure where platforms provide it
Language of creative
Multi-market creative comparison
Nothing inferred about audience

Landing destination

Where the money is pointed.

Landing domain and path
Campaign parameters where present in the URL
Destination page type classification
Landing page change detection
Product or collection targeted

Regulated disclosure

Where more is published.

Political and issue ad classification
Published spend ranges for those categories
Published impression ranges
Funding entity where disclosed
Regulatory disclaimer text

Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

Public ad transparency libraries as the only source, no panels or models
Creative longevity computed from library-published dates as the performance proxy
Published ranges for regulated categories retained as ranges, never midpoints
Landing domain and path captured with change detection
Constant fields stating spend and impression exclusions in the data itself
Source discovery, scoping and a written collection plan
Free pilot on your own sources before any commitment
Full pipeline build, hosting and proxy infrastructure
Schema design, validation and sampled human QA on every run
Ongoing maintenance when source layouts change — our cost, not yours
Delivery to your warehouse, bucket, SFTP or API endpoint
Documented methodology and compliance notes for your legal review

× Not included — stated upfront

Modelled spend or impression estimates for commercial advertising
Targeting parameters, audience definitions or performance data
Inferred audience derived from creative content
Converting published ranges into point estimates
Anything behind a login, paywall or credentialed session
Personal data beyond a documented lawful basis
Licensed third-party datasets we do not hold rights to
Guarantees about fields a source simply does not publish

Schema

Ad intelligence fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 80+ and is agreed during scoping.

Deliverable schema — v2.8 core fields (full dictionary: 80+ fields)
Field	Type	What it captures	Refresh
`ad_id / library`	string / enum	Record identity and which public library it came from	Every run
`advertiser_name / advertiser_verified`	string / boolean	Advertiser as disclosed and verification status where shown	Every run
`country_shown`	array	Countries the ad was shown in, where the library discloses it	Every run
`first_seen / last_seen / days_running`	date / int	Run window and computed duration, the observable performance proxy	Daily
`still_active`	boolean	Whether the ad was live at last observation	Daily
`format / variants`	enum / int	Creative format and variant count where disclosed	Every run
`headline / cta_label`	string	Ad copy and call-to-action label as published	Every run
`landing_domain / landing_path`	string	Campaign destination, so what is being promoted is measurable	Every run
`creative_archived`	boolean	Whether the creative reference was captured for change detection	Every run
`spend_estimate / impressions`	constant	not_collected and not_published_commercial, stated in the data	Every run
`spend_range_published / impressions_range_published`	string	For political and issue ads only, the ranges the platform publishes	Where applicable

spend_estimate is a constant not_collected. Vendors do sell commercial spend estimates; they are models built on panels and assumptions rather than observations, and we will not blend one into a dataset labelled as ad library data.

Coverage

Libraries and surfaces we collect from

Coverage follows what platforms publish. Disclosure depth varies considerably between libraries and by ad category.

Major social platform ad librariesSearch platform ads transparency centresVideo platform ad disclosuresShort-form video creative centres where publicRetail media sponsored placement, via our search visibility servicePolitical and issue ad archives with published rangesRegulated-category ad disclosuresAdvertiser page and portfolio viewsCreative archives where platforms retain historical ads40+ countries where libraries disclose geography

Disclosure depth differs sharply between libraries and by ad category. We state per library what is actually published before build rather than implying uniform coverage. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market	Why demand concentrates here
United States	Deepest ad library disclosure and the largest advertiser base, which makes competitive creative analysis most complete here.
European Union & United Kingdom	Transparency regulation drives the fullest disclosure, including regulated-category spend and impression ranges.
India	Very high advertiser volume with rapid creative turnover, where longevity analysis separates tests from proven creative.
Southeast Asia & GCC	Growing disclosure coverage as libraries extend to more markets, with strong short-form creative activity.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

Political and issue advertising research needs reproducible archive collection with published ranges retained.

What we deliver

Regulated-category ads with published spend and impression ranges, funding entities and disclaimer text.

Metric that moves

Reproducibility

Use cases

How ad intelligence data gets used

Four patterns, with the outcome each is judged on.

Creative longevity as a performance proxy

Run dates and active status produce days-running per ad, so creative a competitor keeps investing in is separable from tests they dropped within a week.

Outcome: Creative direction informed by what competitors sustain rather than by what they launched.

Competitor launch and messaging detection

New ads are detected daily per advertiser with copy captured, so campaign launches and messaging shifts surface as they happen.

Outcome: Competitive response measured in days rather than in quarterly reviews.

Promoted product and collection analysis

Landing domain and path capture reveals which products, collections or offers competitors are actively driving traffic to.

Outcome: Assortment and promotional response based on where competitors point spend, not where they say they focus.

Regulated advertising research

Political and issue ads are collected with the spend and impression ranges platforms publish, plus funding entities and disclaimer text.

Outcome: Reproducible archive research with published figures retained rather than modelled.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Consumer brand · US

Competitor creative was tracked by screenshot

Situation

The brand team collected competitor ads manually and had no view of which creative was sustained versus tested and dropped.

What we ran

Ad library collection with run dates, variant counts and computed days-running across the competitor set.

Result

Sustained creative became separable from failed tests, changing how creative direction was set.

Agency · EU

Clients asked for competitor spend and the agency had no defensible answer

Situation

Available spend figures were vendor models the agency could not explain or stand behind in a client meeting.

What we ran

Creative longevity and variant counts as the observable performance proxy, with spend explicitly excluded and the reason documented.

Result

Client reporting moved to observable evidence the agency could defend rather than modelled figures it could not.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 24-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

Real extraction from your actual sources
Returned inside two business days
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Build vs buy

Should you build ad library collection in-house or hire it as a service?

Libraries change structure and disclosure rules frequently, and creative archival is storage-heavy.

In-house build vs self-serve tool vs Actowiz managed service
Consideration	In-house scraping team	Generic proxy / DIY tool	Actowiz managed feed
Time to first usable data	6–12 weeks of engineering before anything is trustworthy	Days, but output needs manual cleanup before use	Free pilot in 24 hours, production in 5–10 business days
Who fixes it when a source changes	Your engineers, at the cost of their roadmap	You do — tools report failures, they don't resolve them	We do, same business day, inside the retainer
Data quality assurance	Whatever your team has time to build	None beyond HTTP success	Schema validation plus sampled human QA on every run
Compliance documentation	Rarely produced, then requested urgently by legal	Not provided; terms risk sits with you	Sources, method and lawful basis documented for review
Accountability	Distributed across a team with other priorities	A support ticket queue	A named engineer and an account owner
True annual cost	Engineer salaries, proxies, hosting, ongoing maintenance	Low licence fee plus significant hidden analyst time	One fixed monthly retainer, quoted after scoping

Creative longevity is the honest performance signal

Everyone buying ad intelligence wants to know what is working. Spend is not published, performance is not published, and the estimates on the market are models. But there is a real, fully observable signal, and it is under-used.

Why days-running is meaningful

Advertisers pause losers. Nobody keeps a failing creative live for two months. Longevity is a revealed preference.
Variant count signals investment. An ad set with six variants has had production effort spent on it; one variant is often a test.
Restart events are informative. A creative paused and later relaunched has usually been validated in between.
Distribution matters more than individual ads. An advertiser whose median ad runs four days is testing heavily; one whose median runs forty is running proven creative. Those are different marketing operations.
It is comparable across competitors in a way that modelled spend is not, because it comes from the same disclosed dates for everyone.

The limits, stated

Longevity is a proxy, not a measurement. A brand may run creative for brand reasons rather than performance ones, and library dates can be imprecise where a platform reports coarsely. We deliver first_seen, last_seen and days_running from library-published dates, and where a library reports a date range rather than a date we retain the range rather than picking a midpoint.

It is still the strongest observable signal in this category, and it costs nothing beyond collecting the dates properly.

Where more is disclosed, and where nothing is

Ad library disclosure is uneven in a way that matters for scoping, and it is worth understanding before you commit to a comparison across categories.

Commercial advertising

Creative, copy, format, run dates, advertiser and often geography. No spend, no impressions, no reach, no targeting. This is the majority of what we collect and it is genuinely rich — it just does not include money.

Political and issue advertising

Regulation requires more. Platforms publish spend ranges, impression ranges, funding entities and disclaimer text. We capture the published ranges as ranges — not midpoints, not point estimates. A range of five to ten thousand is what was disclosed, and converting it to seven and a half thousand invents precision.

Regulated commercial categories

Some categories carry additional disclosure requirements depending on jurisdiction. Coverage varies and we state it per library and market rather than generalising.

What this means for your scope

If your question is about competitor creative, messaging and what they are promoting, ad libraries answer it well. If your question is share of voice in monetary terms, they do not, and no vendor's model will give you a figure you can defend in a board paper. In that case licence a measurement product and be clear it is modelled.

For paid placement inside retailers rather than on social and search platforms, that sits in our retail search and share of shelf service, where sponsored slots are detected on the retailer's own results pages.

How it works

How an ad intelligence engagement goes live in 5 to 10 business days

Advertiser set, libraries, markets and whether creative archival is required are scoped first, since creative storage drives cost.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We collect from public ad transparency libraries and ads centres without accounts or credentials. We do not collect targeting parameters, audience definitions or performance data, none of which is published. We do not provide modelled spend or impression estimates for commercial advertising. Published ranges for regulated categories are retained as ranges.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment	What we hold ourselves to
Pilot turnaround	A real sample from your own sources within 24 hours of scoping, at no cost.
Go-live	Production collection running within 5–10 business days of sign-off.
Delivery punctuality	99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response	Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality	Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation	A named engineer and an account owner, not a shared ticket queue.
Change requests	Field additions and source changes handled inside the retainer, not re-quoted.
Exit	Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
QA is part of the service. Schema validation and sampled human review run before delivery, every run.
Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.

Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Creative longevity: How long an ad runs, computed from library-published dates. Advertisers pause creative that fails, so longevity is a revealed preference and the strongest observable performance proxy.
Published range: A spend or impression band that a platform discloses for regulated ad categories. Retained as a range, never converted to a midpoint, because a midpoint invents precision the disclosure lacks.
Modelled spend: A commercial advertising spend figure derived from panels and assumptions rather than disclosed. Excluded from the deliverable, with the exclusion stated in the data.

FAQ

Ad intelligence data: frequently asked questions

What marketing, agency and research teams ask during evaluation.

No for commercial advertising, because platforms do not publish it. spend_estimate ships as a constant not_collected so the exclusion is visible in the data.

Vendors do sell spend estimates — those are models built on panels and proprietary assumptions, not observations. If you need a spend figure, licence a measurement product and label it as modelled. We will not blend a model into a dataset labelled as ad library data.

Creative longevity, which is fully observable. Advertisers pause creative that fails, so days-running is a revealed preference. An ad live for fifty-two days with six variants is being invested in; one that ran four days was probably a failed test.

The distribution matters most: an advertiser whose median ad runs four days is testing heavily, one whose median runs forty is running proven creative. Those are different marketing operations, and the comparison is fair because the dates come from the same disclosure for everyone.

Yes, for political and issue advertising, where regulation requires platforms to publish spend and impression ranges. We capture those as ranges.

We do not convert a five-to-ten-thousand range into a midpoint. That invents precision the disclosure does not contain, and for research use in particular the range is the finding.

We capture copy, headline, format, variant count and CTA label as text, plus a creative reference for change detection, at about 97% coverage.

Where creative archival is required — storing the image or video itself — that is scoped separately because storage drives cost materially. Most clients find copy and metadata sufficient for analysis and archive selectively.

No. Targeting parameters and audience definitions are not published in ad libraries. We capture the countries an ad was shown in where a library discloses that, and nothing beyond it.

We do not infer audience from creative, which is a thing some tools do. An inferred audience presented as data is a guess wearing a field name.

Major social platform ad libraries, search platform ads transparency centres, video platform disclosures and short-form creative centres where public.

Disclosure depth differs sharply between them, so we state per library what is actually published before build. A comparison across libraries needs to account for those differences rather than assuming parity.

These libraries exist specifically for public transparency, which makes this one of the more comfortable categories we operate in. Some platforms also provide official APIs for their libraries, which we use in preference to page collection where available.

We respect stated rate limits, collect without accounts, and provide a written methodology document per library plus a DPA before signature.

Yes — landing domain, path, any campaign parameters present in the URL, and destination page type. This is the most under-used field in ad intelligence.

It answers a question creative alone cannot: which products, collections or offers a competitor is actually pointing spend at. Landing page change detection on a running ad often signals a promotional shift before the creative changes.

We quote individually. Drivers are advertiser count, library count, market count, refresh frequency and whether creative archival is required — archival is the main cost variable because of storage.

A focused competitor set across two libraries in one market at daily refresh sits at the lighter end. Broad advertiser coverage across libraries and markets with full creative archival sits considerably higher. One scoping call, a free pilot on your own competitor set within 24 hours, then a fixed monthly quote. Request a quote.

See what your competitors are actually running

Send us a competitor list. We return their active and recent ads with copy, run dates, variant counts and landing pages within 24 hours.

Get a free ad intelligence sample Book a 20-min scoping call

Free pilot, no card, no obligation. No modelled spend figures — those are not published by anyone.

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