> ## 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.
# Platform & Device
> Meta Ads performance across Facebook, Instagram platforms, placements, and devices — the richest breakdown in Ocular for cross-channel ad analysis.
Cross-platform and device performance — for comparing Facebook, Instagram, Messenger, and Audience Network across placements and devices.
This is the **richest** Meta Ads data model. It carries nearly every available metric, plus exclusives like **Reach**, **Frequency**, **Hook Rate**, **Hold Rate**, and per-funnel-step cost. Start here when you don't know which data model to pick.
## What this data model represents
**Grain:** one row per `date × ad_account × campaign × adset × ad × ad_platform × placement × impression_device`.
**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 & revenue"]:::cat --- S
R2["Clicks & impressions"]:::cat --- S
R3["Cost efficiency"]:::cat --- S
R4[Engagement]:::cat --- S
S(["Metrics"]):::root
S --- R5["Conversion funnel"]:::cat
S --- R6[Video]:::cat
S --- R7["Reach & frequency"]:::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
sr1[Ad Spend]:::leaf --- SR["Spend & revenue"]
sr2[Conversion Value]:::leaf --- SR
sr3[ROAS]:::leaf --- SR
sr4[AOV]:::leaf --- SR
ci1[Clicks All]:::leaf --- CI["Clicks & impressions"]
ci2[Impressions]:::leaf --- CI
ci3[CTR All]:::leaf --- CI
ce1[CPC]:::leaf --- CE["Cost efficiency"]
ce2[CPM]:::leaf --- CE
ce3[CPA]:::leaf --- CE
en1[Landing Page Views]:::leaf --- En[Engagement]
en2[Post Engagement]:::leaf --- En
SR:::cat --- S
CI:::cat --- S
CE:::cat --- S
En:::cat --- S
S(["Metrics"]):::root
S --- CF["Conversion funnel"]:::cat
S --- Vd[Video]:::cat
S --- RF["Reach & frequency"]:::cat
CF --- cf1[View Content]:::leaf
CF --- cf2[Add to Cart]:::leaf
CF --- cf3[Checkouts Initiated]:::leaf
Vd --- v1[Video Plays]:::leaf
Vd --- v2[Hook Rate]:::leaf
Vd --- v3[Hold Rate]:::leaf
RF --- rf1[Reach]:::leaf
RF --- rf2[Frequency]:::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["Platform & device"]:::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
pd1[ad_platform]:::leaf --- PD["Platform & device"]
pd2[placement]:::leaf --- PD
pd3[impression_device]:::leaf --- PD
ch1[campaign_id]:::leaf --- CH["Campaign hierarchy"]
ch2[adset_id]:::leaf --- CH
ch3[ad_id]:::leaf --- CH
PD:::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
AC --- ac3[ad_type]:::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 |
| ------------------- | ------ | ------------------------------------------------------------------------------------------------------------ |
| `ad_platform` | String | `Facebook`, `Instagram`, `Messenger`, `Audience Network` |
| `placement` | String | `Feed`, `Stories`, `Reels`, `Right Column`, `In-Stream`, `Search Results`, `Marketplace`, `Instant Articles` |
| `impression_device` | String | `Mobile`, `Desktop`, `Tablet`, `Other` |
Plus the standard campaign hierarchy, time, account, and attribution dimensions — documented once on the [Meta Ads Overview](/data-models/meta-ads/overview).
Querying all three slice-by dimensions at once is highly granular and can produce sparse rows. Start with one or two and add the third only when you need that level of detail.
## Use it to answer
* How does ROAS compare across Facebook vs. Instagram vs. Messenger vs. Audience Network?
* Which placement × device combinations are winning, and which are losing money?
* Does creative format need to change by platform? (vertical for Reels, square for Feed)
* How do Hook Rate and Hold Rate vary by platform to inform video strategy?
## 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 & revenue** | |
| Ad Spend | Sum of daily spend |
| Conversion Value | Sum of reported revenue |
| ROAS | Conversion Value ÷ Ad Spend |
| Conversions | Sum of reported orders |
| AOV | Conversion Value ÷ Conversions |
| **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 |
| Unique Clicks | Avg of deduplicated user clicks |
| Link Click-Through Rate | Link Clicks ÷ Impressions × 100 |
| **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 |
| Cost Per Link Click | Ad Spend ÷ Link Clicks |
| Revenue Per Link Click | Conversion Value ÷ Outbound Clicks |
| **Engagement** | |
| Landing Page Views | Sum |
| Post Engagement | Sum (reactions + comments + shares + clicks) |
| Inline Post Engagement | Sum (on-platform-only) |
| Page Engagement | Sum (Facebook Page likes, follows) |
| Post Reactions | Sum (Like, Love, Haha, Wow, Sad, Angry) |
| Post Shares | Sum |
| Cost Per Post Engagement | Ad Spend ÷ Post Engagements |
| Cost Per Inline Post Engagement | Ad Spend ÷ Inline Post Engagements |
| Cost Per Page Engagement | Ad Spend ÷ Page Engagements |
| Cost Per Landing Page View | Ad Spend ÷ Landing Page Views |
| **Conversion funnel** | |
| View Content | Sum |
| Add to Cart | Sum |
| Checkouts Initiated | Sum |
| Add Payment Info | Sum |
| Cost Per View Content | Ad Spend ÷ View Content |
| Cost Per Add to Cart | Ad Spend ÷ Add to Cart |
| Cost Per Checkout Initiated | Ad Spend ÷ Checkouts Initiated |
| Cost Per Add Payment Info | Ad Spend ÷ Add Payment Info |
| **Video** | |
| Video Plays | Sum |
| Video 25% Watched | Sum |
| Video 50% Watched | Sum |
| Video 75% Watched | Sum |
| Video 100% Watched | Sum |
| Cost Per Video Play | Ad Spend ÷ Video Plays |
| Hook Rate | Video Plays ÷ Impressions × 100 |
| Hold Rate | Video 100% ÷ Video Plays × 100 |
| **Reach & frequency** | |
| Reach | Sum of unique people who saw ads |
| Frequency | Impressions ÷ Reach |
**Hook Rate** and **Hold Rate** are exclusive to this data model. If you need them at audience or creative level, derive from raw fields (`video_play_actions`, `video_views_pct_100`).
This data model exposes a near-complete raw field set (one of the richest, alongside Age & Gender):
`ad_spend` · `clicks` · `impressions` · `reported_orders` · `reported_revenue` · `unique_clicks` · `inline_clicks` · `inline_post_engagement` · `outbound_clicks` · `landing_page_views` · `add_to_cart` · `checkouts_initiated` · `view_content` · `post_engagement` · `page_engagement` · `post_reaction` · `add_payment_info` · `post_share` · `video_play_actions` · `video_view_avg_time` · `video_views_pct_25` · `video_views_pct_50` · `video_views_pct_75` · `video_views_pct_100`