> ## 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.
# Gender
> Google Ads performance by inferred gender in Ocular — for cohort-level budget allocation, demographic bid modifiers, and gender-targeted creative.
Google Ads performance by inferred gender — a small dimension with a high-leverage budget question attached.
## What this data model represents
**Grain:** one row per `date × ad_account × campaign × adset × gender`.
**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]:::cat
S --- R6[Video]:::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]:::leaf --- CI["Clicks & impressions"]
ci2[Impressions]:::leaf --- CI
ci3[CTR]:::leaf --- CI
ce1[CPC]:::leaf --- CE["Cost efficiency"]
ce2[CPM]:::leaf --- CE
ce3[CPA]:::leaf --- CE
ce4[RPC]:::leaf --- CE
SR:::cat --- S
CI:::cat --- S
CE:::cat --- S
S(["Metrics"]):::root
S --- En[Engagement]:::cat
S --- Cv[Conversion]:::cat
S --- Vd[Video]:::cat
En --- en1[Engagements]:::leaf
En --- en2[Interactions]:::leaf
En --- en3[CPE]:::leaf
Cv --- cv1[Conversion Rate]:::leaf
Cv --- cv2[View Through Conversions]:::leaf
Cv --- cv3[Cross Device Conversions]:::leaf
Vd --- v1[Video Views]:::leaf
Vd --- v2[Video 100% Watched]:::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[Demographics]:::cat --- M
D2["Campaign hierarchy"]:::cat --- M
M(["Dimensions"]):::root
M --- D3[Time]:::cat
M --- D4["Account & brand"]:::cat
M --- D5["Creative & platform"]:::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
dm1[gender]:::leaf --- Dm[Demographics]
ch1[campaign_id]:::leaf --- CH["Campaign hierarchy"]
ch2[adset_id]:::leaf --- CH
ch3[campaign_type]:::leaf --- CH
Dm:::cat --- M
CH:::cat --- M
M(["Dimensions"]):::root
M --- Ti[Time]:::cat
M --- AB["Account & brand"]:::cat
M --- CP["Creative & platform"]:::cat
Ti --- t1[date]:::leaf
Ti --- t2[week_start_date]:::leaf
AB --- ab1[ad_account_id]:::leaf
AB --- ab2[brand]:::leaf
CP --- cp1[ad_platform]:::leaf
CP --- cp2[final_url]:::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:** Google Ads API, daily refresh.
Gender is **inferred** from user behavior, account info, and browsing patterns — not all users can be classified. Expect a meaningful `Unknown` bucket.
## Slice by
Unique to this data model:
| Dimension | Type | Values |
| --------- | ------ | --------------------------- |
| `gender` | String | `Male`, `Female`, `Unknown` |
Plus the standard campaign hierarchy, time, and account dimensions — documented once on the [Google Ads Overview](/data-models/google-ads/overview).
## Use it to answer
* Which gender segment gives higher ROAS — where should I shift budget?
* Where do CTR and engagement differ by gender — should creative be gendered?
* What bid modifier is justified by the conversion-rate delta?
* How do video completion rates vary — tailor video length per segment?
## Available metrics
Everything you can compute on this data model. Need a metric not listed? See the [Google Ads metric availability matrix](/data-models/google-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 attributed conversion events |
| AOV | Conversion Value ÷ Conversions |
| **Clicks & impressions** | |
| Clicks | Sum of clicks |
| Impressions | Sum of impressions |
| CTR | Clicks ÷ Impressions × 100 |
| **Cost efficiency** | |
| CPC | Ad Spend ÷ Clicks |
| CPM | Ad Spend ÷ Impressions × 1,000 |
| CPA | Ad Spend ÷ Conversions |
| RPC | Conversion Value ÷ Clicks |
| **Engagement** | |
| Engagements | Sum |
| Interactions | Sum |
| CPE | Ad Spend ÷ Engagements |
| CPI | Ad Spend ÷ Interactions |
| **Conversion** | |
| Conversion Rate | Conversions ÷ Clicks × 100 |
| View Through Conversions | Sum of conversions from impressions without clicks |
| Cross Device Conversions | Sum of conversions on a different device from the click |
| **Video** | |
| Video Views | Sum |
| Video 25% Watched | Sum |
| Video 50% Watched | Sum |
| Video 75% Watched | Sum |
| Video 100% Watched | Sum |
`ad_spend` · `conversion_value` · `conversions` · `clicks` · `impressions` · `engagements` · `interactions` · `view_through_conversions` · `cross_device_conversions` · `video_views` · `video_25_watched` · `video_50_watched` · `video_75_watched` · `video_100_watched`