Asset Breakdowns

Documentation Index

Fetch the complete documentation index at: https://docs.ocular.dev/llms.txt

Use this file to discover all available pages before exploring further.

Six related data models for creative A/B testing — each slicing performance by a different element of the ad (text copy, visual asset, or full creative package). Pick the one matching the element you’re testing.

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Pick an asset to test

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What they all share

Every asset breakdown carries the same near-complete metric set. The per-asset sections below cover only the slice-by dimensions and per-asset quirks. Metrics, grouped by category — every number you can compute, across all six asset types. Expand below for examples in each.

Show sample metrics in each category

Dimensions, grouped by category — pick an asset type to slice the metric by, plus the standard hierarchy shared across all Meta Ads data models. Expand below for examples in each.

Show sample dimensions in each category

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Common metrics

Need a metric not listed? See the Meta Ads metric availability matrix.

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Common dimensions

Standard campaign hierarchy, time, account, and attribution dimensions — documented once on the Meta Ads Overview.

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Body Asset

Grain: one row per date × ad × body_text. Slice by: Use it to answer:

• Which messaging approach wins — benefit, feature, or urgency framing?
• Which copy gets clicks but no conversions? (clickbait detection)
• Does longer or shorter copy convert better per campaign objective?

Uses standard revenue metric names.

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Title Asset

Grain: one row per date × ad × title_text. Slice by: Use it to answer:

• Which headline patterns drive CTR — questions, numbers, urgency?
• What’s the right title length for each placement?
• Which titles convert vs. which just attract clicks?

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Description Asset

Grain: one row per date × ad × description_text. Slice by: Use it to answer:

• Do longer descriptions with more detail outperform shorter ones?
• Does benefit-focused or feature-focused framing convert better?
• How does description copy affect funnel conversion rates?

Uses standard revenue metric names.

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Image Asset

Grain: one row per date × ad × image_hash. Slice by: Use it to answer:

• Which images perform best regardless of how they’re packaged? (use image_hash)
• Which image style wins — lifestyle, product-shot, or promotional?
• Where is creative fatigue setting in? (CTR decay by image over time)

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Video Asset

Grain: one row per date × ad × video_id. Slice by: Use it to answer:

• Which video creatives drive view-through and conversion?
• What’s the winning video length and style?
• Where does the View Content → Add to Cart funnel break for a given video?

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Creative Asset

Grain: one row per date × ad × asset_id. Slice by: Use it to answer:

• Which full creative packages win? (image + copy + headline + CTA + format as one unit)
• Which creative format scales best — single image, carousel, video, or collection?
• What combinations should be promoted to top performers across campaigns?

Uses standard revenue metric names.

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Channel-reported naming

Three asset breakdowns expose revenue metrics under a different name due to source-schema convention. Values are identical — only the field name differs. When building cross-asset reports that combine all six, normalize to a single name in your query layer.

Raw fields (for custom calculations)

All asset breakdowns expose:ad_spend · clicks · impressions · link_clicks · reported_orders · reported_revenue · landing_page_views · add_to_cart · checkouts_initiated · view_content · add_payment_info · post_engagement · page_engagement · post_reaction · searchesVideo Asset additionally exposes: video_views_total · cost_per_video_view