> ## 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.
# Hourly Audience
> Meta Ads performance by hour of day and day of week in Ocular — for dayparting analysis, ad scheduling decisions, and time-based bid adjustments.
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](/data-models/meta-ads/platform-and-device) with a time filter.
## 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.
```mermaid theme={null}
flowchart LR
R1["Spend & conversions"]:::cat --- S
R2["Clicks & impressions"]:::cat --- S
S(["Metrics"]):::root
S --- R3["Cost efficiency"]:::cat
S --- R4[Engagement]:::cat
S --- R5["Conversion funnel"]:::cat
classDef root fill:#0f172a,color:#f8fafc,stroke:#0f172a,stroke-width:2px
classDef cat fill:#2563eb,color:#fff,stroke:#1d4ed8,stroke-width:1.5px
```
```mermaid theme={null}
flowchart LR
sc1[Ad Spend]:::leaf --- SC["Spend & conversions"]
sc2[Conversions]:::leaf --- SC
ci1[Clicks All]:::leaf --- CI["Clicks & impressions"]
ci2[Impressions]:::leaf --- CI
ci3[CTR All]:::leaf --- CI
SC:::cat --- S
CI:::cat --- S
S(["Metrics"]):::root
S --- CE["Cost efficiency"]:::cat
S --- En[Engagement]:::cat
S --- CF["Conversion funnel"]:::cat
CE --- ce1[CPC]:::leaf
CE --- ce2[CPM]:::leaf
CE --- ce3[CPA]:::leaf
En --- en1[Landing Page Views]:::leaf
En --- en2[Engagement Rate]:::leaf
CF --- cf1[View Content]:::leaf
CF --- cf2[Add to Cart]:::leaf
CF --- cf3[Checkouts Initiated]:::leaf
classDef root fill:#0f172a,color:#f8fafc,stroke:#0f172a,stroke-width:2px
classDef cat fill:#2563eb,color:#fff,stroke:#1d4ed8,stroke-width:1.5px
classDef leaf fill:#eff6ff,color:#1e3a8a,stroke:#bfdbfe,stroke-width:1px
```
**Dimensions, grouped by category** — every way you can split, filter, or group those metrics. Expand below for examples in each.
```mermaid theme={null}
flowchart LR
D1[Daypart]:::cat --- M
D2["Campaign hierarchy"]:::cat --- M
M(["Dimensions"]):::root
M --- D3[Time]:::cat
M --- D4["Account & creative"]:::cat
M --- D5[Attribution]:::cat
classDef root fill:#0f172a,color:#f8fafc,stroke:#0f172a,stroke-width:2px
classDef cat fill:#9333ea,color:#fff,stroke:#7e22ce,stroke-width:1.5px
```
```mermaid theme={null}
flowchart LR
dp1[audience_hour]:::leaf --- Dp[Daypart]
dp2[day_of_week]:::leaf --- Dp
ch1[campaign_id]:::leaf --- CH["Campaign hierarchy"]
ch2[adset_id]:::leaf --- CH
ch3[ad_id]:::leaf --- CH
Dp:::cat --- M
CH:::cat --- M
M(["Dimensions"]):::root
M --- Ti[Time]:::cat
M --- AC["Account & creative"]:::cat
M --- At[Attribution]:::cat
Ti --- t1[date]:::leaf
Ti --- t2[week_start_date]:::leaf
AC --- ac1[ad_account_id]:::leaf
AC --- ac2[creative_id]:::leaf
At --- at1[attribution_window]:::leaf
classDef root fill:#0f172a,color:#f8fafc,stroke:#0f172a,stroke-width:2px
classDef cat fill:#9333ea,color:#fff,stroke:#7e22ce,stroke-width:1.5px
classDef leaf fill:#faf5ff,color:#581c87,stroke:#e9d5ff,stroke-width:1px
```
**Source:** Meta Ads API, daily refresh.
## Slice by
Unique to this data model:
| Dimension | Type | Values |
| --------------- | ------- | --------------------------------------------------- |
| `audience_hour` | Integer | `0` (midnight–1am) … `23` (11pm–midnight) |
| `day_of_week` | String | Derived from date — `Monday`, `Tuesday`, … `Sunday` |
Plus the standard campaign hierarchy, time, account, and attribution dimensions — documented once on the [Meta Ads Overview](/data-models/meta-ads/overview).
## 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?
## Available metrics
Everything you can compute on this data model. Need a metric not listed? See the [Meta Ads metric availability matrix](/data-models/meta-ads/overview#metric-availability-matrix).
| Metric | Formula |
| ------------------------------- | ------------------------------------ |
| **Spend & conversions** | |
| Ad Spend | Sum of daily spend |
| Conversions | Sum of reported orders |
| **Clicks & impressions** | |
| Clicks (All) | Sum of clicks (inline + outbound) |
| Impressions | Sum of impressions |
| CTR (All) | Clicks ÷ Impressions × 100 |
| Outbound Clicks | Sum of clicks leading off Meta |
| Inline Clicks | Sum of on-platform engagement clicks |
| **Cost efficiency** | |
| CPC (All) | Ad Spend ÷ Clicks |
| CPM | Ad Spend ÷ Impressions × 1,000 |
| CPA | Ad Spend ÷ Conversions |
| Cost Per Outbound Click | Ad Spend ÷ Outbound Clicks |
| **Engagement** | |
| Landing Page Views | Sum |
| Post Shares | Sum |
| Engagement Rate | Post Engagement ÷ Impressions × 100 |
| Cost Per Post Engagement | Ad Spend ÷ Post Engagements |
| Cost Per Inline Post Engagement | Ad Spend ÷ Inline Post Engagements |
| **Conversion funnel** | |
| View Content | Sum |
| Add to Cart | Sum |
| Checkouts Initiated | Sum |
**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`