Audit geo-targeting performance
Analyze geo-level ROAS/CPA against benchmarks to surface scaling opportunities and stop wasted spend in off-plan locations
Your best-performing geos might be hitting budget caps while underperformers burn through media plans unchecked
This template analyzes geo-level performance across all platforms to identify locations beating your ROAS benchmarks by 10% or more that deserve additional budget. It compares actual spend distribution against your media plan allocations, catches off-plan geo leakage from loose location settings, and prioritizes fixes by severity—from critical exclusions to optimization opportunities.
Why this matters
Location performance varies dramatically—your UK campaigns might deliver 2x the ROAS of Germany, but if your spend allocation doesn't reflect this, you're leaving money on the table. Worse, loose location settings like Google's "Presence or Interest" can leak 15-20% of budget to unintended geos. This template surfaces every geo-based optimization opportunity, from scaling winners to plugging leaks, with specific platform settings to adjust.
How to customize
Set custom ROAS/CPA thresholds based on your margin requirements by geo. Add state or city-level analysis for larger markets like the US. Include day-parting analysis to catch timezone-related performance differences. Configure alerts for specific geos that require manual approval before scaling. Add conversion lag windows if your product has longer consideration cycles in certain regions.
You are auditing geo-targeting performance across campaigns. Your goals are to:
* Identify geos over- or under-performing against account average
* Detect spend skews vs media plan allocations
* Flag off-plan spend and location setting misconfigurations
* Provide prioritised, actionable recommendations
**Data you have access to**
* Campaign geo-targeting settings (including location setting type)
* Geo-level spend
* Geo-level performance (ROAS, CPA, or primary KPI)
* Account average ROAS/CPA
* Media plan geo allocations (% spend by country/region)
**Where to fetch geo data**
* Google / Meta / LinkedIn / TikTok → geo performance reports by country/region
* DSPs (DV360, TTD) → geo performance at IO or line-item level
* CRM/GA4 (if available) → conversion by geo
**Platform location setting reference**
* **Google Ads**: Presence, Presence or Interest
* **Meta Ads**: Living in, Recently in, Reach more people likely to respond
* **LinkedIn Ads**: Permanent location, Recent location
* **TikTok Ads**: Country, region, state/province, city, postal, DMA
**Expected normal pattern**
* Geo ROAS/CPA within ±10% of account average
* Spend matches media plan allocations
* No spend in off-plan geos
* Location targeting set to intended option (e.g. Google = Presence, Meta = Living in)
**Interpretation rules**
* **≥10% stronger ROAS / better CPA** with scalable volume → scale spend
* **≥10% weaker ROAS / worse CPA** → reduce spend or optimise
* **Spend skew vs plan >10%** → adjust allocations
* **Spend in off-plan geo** → flag and recommend exclusion
* **Suspicious location setting** → recommend tightening
* **Low volume geos** → monitor only
**Output format must be:**
1. **Coverage check** – % of spend in planned geos
2. **Geo performance table**, columns:
* Geo
* Planned vs actual spend %
* ROAS/CPA vs account average (% diff)
* Location setting used
* Recommendation (scale, reduce, adjust, exclude, fix setting, monitor)
* Severity (High / Medium / Low)
3. **Group recommendations into:**
* Do Now (critical, high severity)
* Do Next (optimisation, medium severity)
* Monitor (low severity)
4. **Plain-language one-line summary** (e.g. "Scale UK +20%, reduce DE -15%, exclude AT leakage, tighten Google Ads to 'Presence'").
Be concise, structured, and action-orientated.
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