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Dayparting analysis — which hours of which days are working for your spend, and when to pull back.
This is the only Meta Ads data model that does not expose revenue metrics (Conversion Value, ROAS, AOV). It tracks order count and engagement, not revenue. If you need revenue at hourly granularity, use Platform & Device with a time filter.

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What this data model represents

Grain: one row per date × ad_account × campaign × adset × ad × audience_hour. Metrics, grouped by category — every number you can compute on this data model. Expand below for examples in each.
Dimensions, grouped by category — every way you can split, filter, or group those metrics. Expand below for examples in each.
Source: Meta Ads API, daily refresh.

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Slice by

Unique to this data model: Plus the standard campaign hierarchy, time, account, and attribution dimensions — documented once on the Meta Ads Overview.

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Use it to answer

  • Where are peak conversion hours vs. peak spend hours — and what’s the inefficiency?
  • Which hours should I pause? Which should I bid up?
  • How do hour × day-of-week patterns differ between weekdays and weekends?
  • What time of day has the highest engagement for awareness campaigns?

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Available metrics

Everything you can compute on this data model. Need a metric not listed? See the Meta Ads metric availability matrix.
Engagement Rate as a precomputed metric lives only here. In other data models you’d have to compute it from raw Post Engagement and Impressions.
This data model exposes:ad_spend · clicks · impressions · reported_orders · inline_clicks · outbound_clicks · landing_page_views · add_to_cart · checkouts_initiated · view_content · post_share