> ## 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.

# Region

> Meta Ads performance by geography in Ocular — for regional budget allocation, market expansion decisions, and geo-targeted creative localization.

Geographic performance — which regions are paying back, and how to localize creative for the ones worth doubling down on.

<Note>
  Region granularity is **set by your ad targeting**, not by this data model. If the campaign targets countries, you'll see countries; if it targets states or cities, you'll see those. You can't slice deeper than the targeting allows.
</Note>

## What this data model represents

**Grain:** one row per `date × ad_account × campaign × adset × ad × region`.

**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

    S(["Metrics"]):::root

    S --- R4[Engagement]:::cat
    S --- R5["Conversion funnel"]:::cat
    S --- R6["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
```

<Accordion title="Show sample metrics in each category">
  ```mermaid theme={null}
  flowchart LR
    sr1[Ad Spend]:::leaf --- SR["Spend & revenue"]
    sr2[Conversion Value]:::leaf --- SR
    sr3[ROAS]:::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

    SR:::cat --- S
    CI:::cat --- S
    CE:::cat --- S

    S(["Metrics"]):::root

    S --- En[Engagement]:::cat
    S --- CF["Conversion funnel"]:::cat
    S --- RF["Reach & frequency"]:::cat

    En --- en1[Landing Page Views]:::leaf
    En --- en2[Post Engagement]:::leaf

    CF --- cf1[View Content]:::leaf
    CF --- cf2[Add to Cart]:::leaf
    CF --- cf3[Checkouts Initiated]:::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
  ```
</Accordion>

**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[Geography]:::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
```

<Accordion title="Show sample dimensions in each category">
  ```mermaid theme={null}
  flowchart LR
    g1[region]:::leaf --- Geo[Geography]

    ch1[campaign_id]:::leaf --- CH["Campaign hierarchy"]
    ch2[adset_id]:::leaf --- CH
    ch3[ad_id]:::leaf --- CH

    Geo:::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
  ```
</Accordion>

**Source:** Meta Ads API, daily refresh.

## Slice by

Unique to this data model:

| Dimension | Type   | Example values                                                                                           |
| --------- | ------ | -------------------------------------------------------------------------------------------------------- |
| `region`  | String | Countries: `United States`, `Canada` · States: `California`, `Texas` · Cities: `Los Angeles`, `New York` |

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 should I shift budget — which regions have the highest ROAS?
* Which new markets are emerging based on early-stage high performance?
* How should creative be localized for regional language and cultural preferences?
* How does Conversion Value differ across regions — what does that tell me about pricing 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                       |
| **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              |
| **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 Engagement          | Sum (reactions + comments + shares + clicks) |
| Post Shares              | Sum                                          |
| Cost Per Post Engagement | Ad Spend ÷ Post Engagements                  |
| **Conversion funnel**    |                                              |
| View Content             | Sum                                          |
| Add to Cart              | Sum                                          |
| Checkouts Initiated      | Sum                                          |
| **Reach & frequency**    |                                              |
| Reach                    | Sum of unique people who saw ads             |
| Frequency                | Impressions ÷ Reach                          |

<Tip>
  **Reach** and **Frequency** are only available here and on [Platform & Device](/data-models/meta-ads/platform-and-device). Use Region for geographic concentration of reach; use Platform & Device for platform-mix concentration.
</Tip>

<Accordion title="Raw fields (for custom calculations)">
  This data model exposes:

  `ad_spend` · `clicks` · `impressions` · `reported_orders` · `reported_revenue` · `unique_clicks` · `inline_clicks` · `outbound_clicks` · `landing_page_views` · `add_to_cart` · `checkouts_initiated` · `view_content` · `post_engagement` · `post_share`
</Accordion>