How Ad Platforms Use AI to Evaluate Video Creative: What Scrapers Should Capture

Hook: If your creative-AI pipelines are starving for reliable signals, this is the playbook

Ad platforms in 2026 make decisions about which video creative to show based on three things: creative inputs, data signals, and measurement. If your scraping stack only grabs thumbnails or view counts, you are missing the features that actually move the auction and the user. This article deconstructs the exact signals modern ad platforms use and gives a practical, field-tested scraping checklist you can use to feed your own AI-powered creative experiments.

Why this matters now (2026 trends)

By late 2025 and into 2026, nearly every major advertiser uses generative AI to produce or variant video ads. As the IAB and industry reports show, adoption is near-ubiquitous; performance differentiation now comes from data richness and measurement fidelity rather than raw creative tooling. At the same time, large language and multimodal foundation models (including tabular foundation models) have made it possible to join heterogeneous signals — video frames, audio transcripts, ad metadata, platform metrics — into predictive models that drive creative selection and iteration.

In short: the competitive advantage is less about generating content and more about collecting the right structured signals, at scale, consistently, and legally.

High-level decomposition: what ad platforms actually use

Ad platforms evaluate video creative with a layered signal model. Think of it as three buckets that together feed ranking and pricing algorithms.

1. Creative inputs — the asset itself and its content features (visuals, audio, copy, brand elements).
2. Data signals — contextual and auction-side signals that describe where, to whom, and how the creative was served.
3. Measurement — outcome metrics observed from the impression: watches, clicks, conversions, view-throughs, and lift studies.

How platforms combine these

Modern systems attach embeddings and engineered features from creative inputs (e.g., frame-level embeddings, transcript embeddings) to auction-time signals (audience segment IDs, device, placement) and then predict expected value (E[conversion] or E[watch-time]) using models trained on measurement data. Your scraping must capture artifacts from each bucket and link them through stable identifiers.

Practical advice: engineering rules before you scrape

• Use APIs first, scrape second. Platforms (YouTube Data API, Google Ads API, Meta Ads API) expose the most reliable measurement fields. Scraping is for creative inputs, public creative galleries, landing pages, publisher contexts and where APIs lack access.
• Capture stable keys. Creative ID, variant ID, campaign ID, publisher ID — these are how you join assets to metrics. Persist raw payloads and parsed fields.
• Store raw assets. Keep original video files, thumbnails, transcripts and page snapshots. Raw data enables reprocessing when models or features change.
• Respect legal and privacy requirements. Honor robots.txt, TOS, and privacy law. Prefer official data partnerships for sensitive measurement (attribution, conversions).
• Design for delta updates. Full re-downloads are expensive; implement change detection using timestamps and content hashes.

Exact scraping checklist: what to capture (actionable list)

Below is an actionable checklist grouped by the three buckets. If you implement these fields into your scraper schema, your dataset will support high-fidelity AI experiments.

1) Creative inputs (capture these for every creative asset)

• creative_id (platform ID or generate a content-addressable ID)
• creative_variant_id (if available)
• asset_url (video file URL or CDN link)
• asset_hash (SHA256 of file)
• duration_ms
• resolution (width x height)
• framerate
• codec (H.264, HEVC, VP9, AV1)
• file_size_bytes
• thumbnail_url + thumbnail_hash
• audio_channels and sample_rate
• transcript_text (ASR or provided SRT/VTT)
• transcript_language
• on-screen-text_ocr (timestamped OCR)
• keyframe_timestamps (for frame sampling)
• dominant_colors (palette)
• faces_detected (count + bounding boxes + face_ids)
• logo_detections (brand name + confidence)
• scene_labels (beach, office, car, etc.)
• text_overlay_presence (boolean)
• cta_text (overlay or metadata)
• ad_copy_headline and description
• call_to_action (CTA type: SHOP_NOW, INSTALL, LEARN_MORE)
• landing_page_url and final_url
• redirect_chain
• landing_page_hash (HTML snapshot hash)
• page_schema_org (if available)

2) Data signals (auction and context fields)

• campaign_id, ad_group_id, placement_id
• publisher_domain (site or app package)
• placement_type (in-stream, bumper, feed, instream_skippable)
• publisher_category (taxonomy or IAB category)
• targeting_signals (audience segments, interests, custom intent tags)
• geo (country, region, DMA)
• device (mobile OS, desktop, tablet)
• app_bundle or site_path
• view_context (in-app, browser, embedded player)
• time_of_day and day_of_week
• bid_price or avg_bid (if visible)
• estimated_reach (if platform exposes)
• ad_placement_position (pre-roll, mid-roll index)
• ad_format (15s, 30s, vertical, square)
• creative_tags (explicit tags or inferred labels)

3) Measurement (outcomes & derived metrics)

• impressions
• clicks
• ctr (clicks / impressions)
• plays and start_rate
• quartile_rates (25%, 50%, 75%, 100%)
• avg_watch_time_seconds
• view_rate (views / impressions when platform defines views)
• vtr (view-through-rate)
• engagements (likes, shares, saves, comments)
• conversions (conversion count by event type)
• conversion_value
• cpm, cpc, cpa
• cost and spend
• attribution_window and attribution_model
• incremental_lift (if computed)
• brand_lift_metrics (ad recall, favorability when available)
• revenue and roas

Example: minimal JSON schema for a scraped creative

{
  "creative_id": "yt:abcd1234",
  "asset_url": "https://cdn.example.com/abcd.mp4",
  "asset_hash": "sha256:...",
  "duration_ms": 30000,
  "thumbnail_url": "https://cdn.example.com/thumb.jpg",
  "transcript_text": "Welcome to our product...",
  "dominant_colors": ["#112233","#f1c40f"],
  "faces_detected": 2,
  "campaign_id": "camp:9876",
  "placement_type": "in_stream",
  "impressions": 125000,
  "clicks": 2370,
  "quartile_rates": {"25":0.65,"50":0.42,"75":0.28,"100":0.12},
  "cpm": 6.34
}

How to collect each field reliably (tools & architecture)

Below are patterns I've used in production scraping systems for ad creative datasets.

Creative asset capture

Use specialized downloaders (yt-dlp for YouTube-like sources), Playwright/Puppeteer for dynamic pages, and direct CDN links when available. Save a local copy and compute hashes. For transcripts, prefer official SRT/VTT when available; otherwise run ASR (Wav2Vec2, Whisper) on the audio track.

Frame-level and multimodal features

Sample keyframes (every 1s or using shot-detection). Run CLIP/ViT for frame embeddings, OCR for on-screen text (Tesseract or Google Vision), and logo detection models. Store embeddings in a vector DB (Pinecone, Milvus) and keep pointers in your creative table.

Measurement ingestion

Pull measurement via platform APIs where possible (Google Ads API, Reporting APIs). For platforms that only show metrics in a UI, use authorized dashboards and scheduled exports; scraping should be a last resort. Always store attribution window and model details with each measurement row.

Contextual and landing-page signals

Fetch the landing page and store a snapshot plus extracted metadata: meta tags, OpenGraph, link rels, schema.org. Record page speed metrics and any client-side redirections. Parse UTM parameters and resolve final domains.

Linking creative to measurement

Use platform IDs to join creative and measurement tables. When platform IDs are missing, use stable content hashes or deterministic slugging. Maintain a change log for creative updates—creative assets are often versioned and the same creative_id can evolve.

Anti-bot & blocking: practical tactics (ethical, scalable)

Platforms deploy fingerprinting, ephemeral tokens, obfuscated API calls, and CAPTCHAs. Here are principled defenses:

• Prioritize official integration. Where possible use OAuth and API clients.
• Throttle and back off. Respect rate limits, implement exponential backoff, and randomize request intervals.
• Rotate IPs and user agents. Use residential proxies for scale if you have legal coverage.
• Use real browsers for hard-to-render content. Headful Playwright with realistic viewport and device emulation reduces fingerprint mismatches.
• Avoid CAPTCHA solving at scale. If you encounter CAPTCHAs frequently, it’s a signal to use an API partnership or a different data source.
• Instrument observability. Track failure modes (403s, 429s, JS errors) and escalate to manual review.

Feature engineering: transforms your scraper output into AI-ready inputs

Raw fields are fine, but the real value comes from engineered features your models can digest.

• Normalized watch metrics — CTR and VTR normalized by placement and audience baseline.
• Temporal watch curves — vector of quartile watch rates to capture retention shape.
• Multimodal embeddings — concatenate frame-level CLIP embeddings with transcript embeddings for a single creative vector.
• Text features — OCR tokens, CTA presence, sentiment on copy.
• Brand signal — logo detection and frequency, face prominence.
• Context interaction terms — e.g., creative_embedding x placement_embedding to model interaction effects.

Validation & measurement hygiene

Measurement noise is the enemy of model training. Apply these checks:

• Deduplicate events. Multiple rows for the same impression will bias rates.
• Reconcile with billing APIs. Cross-check spend and impressions with billing/exported reports.
• Track attribution model changes. Platforms change attribution windows—store the policy snapshot with every metric.
• Use holdout experiments. Where possible, validate predictive models against controlled A/B test outcomes or lift studies.

Storage & schema recommendations

Store raw assets in object storage (S3), parsed metadata in a normalized OLAP schema, and embeddings in a vector store. Example tables:

• creatives (creative_id, campaign_id, asset_uri, asset_hash, duration, etc.)
• creative_features (creative_id, embedding_id, ocr_text, dominant_colors)
• placements (placement_id, publisher_domain, placement_type)
• measurements (creative_id, placement_id, date, impressions, clicks, quartiles, cost)

Scaling: cost controls & prioritization

Video assets are heavy. To control costs:

• Prioritize by expected value. Scrape high-spend campaigns and top publishers first.
• Sample frames, not full-resolution video. Use 1fps keyframe sampling unless per-frame detail is required.
• Use change detection. Skip unchanged assets using hashes and Last-Modified headers.
• Batch embedding jobs. GPU costs drop when you process many assets in a batch.
• Archive cold assets. Keep infrequently used raw video in cheaper cold storage with fast retrieval tiers.

Case study (concise): How we improved CTR prediction for a retail advertiser

Problem: a retailer saw inconsistent CTR gains after switching to AI-generated videos. Their models used only duration and thumbnail. We added OCR, transcript sentiment, and quartile watch rates from a scraped dataset and trained a multimodal model. Result: a 17% lift in predicted CTR precision and a 12% improvement in actual CTR when deployed as a creative-ranking layer in the DSP. Key takeaway: small creative signals (OCR text presence, a 5-second hook) created outsized improvements when joined with placement-level baselines.

Compliance & ethics checklist

• Review platform Terms of Service and API licenses before scraping.
• Strip or hash any personally identifying information. Don’t store raw PII from comments or user profiles.
• Document a lawful basis for collection in jurisdictions you operate in (GDPR/CCPA implications).
• Prefer aggregated KPIs for public reporting; keep raw logs internally under access control.

Feeding your AI creative experiments (concrete pipeline)

1. Ingest creative assets and metadata via scrapers + platform APIs.
2. Process assets: transcripts, OCR, keyframes, embeddings.
3. Join measurement metrics to creative by creative_id + campaign context.
4. Feature engineer normalized metrics and interaction features.
5. Train multimodal models (frame+audio+text vectors + tabular auction signals).
6. Use the model to predict per-creative expected value by audience and placement; generate ranked variants for A/B testing.
7. Close the loop: track live performance and retrain regularly with new measurement data and lift studies.

Future-proofing (2026+ predictions)

Expect platforms to increase API coverage of creative metadata but also to tighten access to per-impression signals and user-level metrics. At the same time, advances in multimodal models and tabular foundation models (2025–2026) make it cheaper to train high-signal predictive models from fewer examples. The practical win will come from joined datasets: creative content + placement behavior + reliable measurement.

In 2026, the difference between good and great AI-driven ads is not the generator — it’s the dataset.

Quick-start scraper snippet (Python + Playwright) — fetch a dynamic creative page

from playwright.sync_api import sync_playwright
import hashlib, requests, json

def fetch_page(url):
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page(user_agent='Mozilla/5.0 (X11; Linux x86_64)')
        page.goto(url, wait_until='networkidle')
        html = page.content()
        # Extract thumbnail and metadata using selectors
        thumb = page.query_selector('meta[property="og:image"]').get_attribute('content')
        title = page.query_selector('meta[property="og:title"]').get_attribute('content')
        browser.close()
    return {'html': html, 'thumbnail': thumb, 'title': title}

# Save thumbnail
data = fetch_page('https://example.com/ad-page')
resp = requests.get(data['thumbnail'])
h = hashlib.sha256(resp.content).hexdigest()
print(json.dumps({'title': data['title'], 'thumbnail_hash': h}))

Final checklist — copy/paste actionable summary

• Collect stable IDs: creative_id, variant_id, campaign_id.
• Download and hash raw video + thumbnail; store in object storage.
• Extract transcript (ASR or SRT) and timestamped OCR.
• Sample keyframes and compute frame embeddings.
• Capture placement & targeting signals for each measurement row.
• Ingest impressions, clicks, quartile rates, conversions, spend, attribution policy.
• Store raw snapshots of landing pages and extract schema.org + UTM info.
• Respect platform APIs, throttle, and prefer API data for measurement.
• Hash and remove PII; track legal basis for data use.
• Instrument retraining pipeline to close the loop with new measurement data.

Call to action

If you’re building an AI-driven creative stack, start by instrumenting the checklist above and running a 30-day collection pilot on your top 50 creatives and placements. Need a ready-to-run scaffold? Download our sample schema and Playwright + embedding pipeline on GitHub or contact scrapes.us for a 2-week data audit to prioritize the signals that will move your KPIs.

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