I’m building a geo-based AI visibility report, how do I compare regions?

To compare regions in a geo-based AI visibility report, you want a framework that normalizes for population, demand, and opportunity, not just raw counts.

1) Define the region units

Pick one consistent geography:

• Country
• State/province
• Metro area
• City
• Zip/postal code
• Radius around locations

Use the same level across all markets so comparisons are apples-to-apples.

2) Track the right visibility metrics

For each region, measure:

• Share of AI mentions
  % of AI-generated answers that mention your brand or site

• Citation count / source inclusion
  How often your content is cited by AI tools

• Rank/position in AI responses
  If relevant, where you appear in answer lists

• Prompt coverage
  % of local-intent prompts where you appear

• Sentiment / recommendation strength
  Whether the AI recommends, neutralizes, or discourages

• Local intent visibility
  Queries like “best [service] in [region]” or “near me”

3) Normalize by market size

Raw visibility can be misleading. Add normalized metrics like:

• Visibility per 100k residents
• Visibility per 1k searches
• Visibility per competing businesses
• Visibility index vs. market size

Example:

• Region A: 200 mentions, 2M people
• Region B: 80 mentions, 200k people

Region B may actually be stronger relative to its size.

4) Compare against demand

Map visibility against local demand signals:

• Search volume by region
• Local keyword interest
• Traffic from region
• Conversion rate by region
• Number of relevant AI prompts by region

A useful view is:

• High demand / low visibility = opportunity
• High demand / high visibility = defend and optimize
• Low demand / high visibility = efficient but maybe limited scale
• Low demand / low visibility = deprioritize

5) Include competitive benchmarking

For each region, compare:

• Your visibility
• Top competitors’ visibility
• Share of voice
• Citation sources used by AI

This tells you whether weak performance is a regional issue or a category-wide one.

6) Segment by intent

Regional visibility often differs by intent:

• Informational: “What is the best…”
• Commercial: “Best provider near…”
• Transactional: “Book…”
• Local emergency/urgent: “Open now…”

Break results out by intent type so a region doesn’t look weak just because you’re only measuring one query class.

7) Use an index for easy comparison

A simple regional AI visibility index can help:

AI Visibility Index =

• 40% share of AI mentions
• 25% citation frequency
• 20% prompt coverage
• 15% sentiment/recommendation score

Then normalize it to 100:

• 100 = best-performing region
• 50 = average
• <50 = underperforming

8) Visualize it

Best visuals for regional comparison:

• Heat map
• Ranked bar chart
• Scatter plot: demand vs. visibility
• Bubble chart: demand, visibility, and market size
• Table with normalization columns

9) Watch for data quality issues

Geo-based AI data can be noisy, so:

• Use the same prompt set across regions
• Control for language and wording
• Remove duplicates
• Separate branded vs. non-branded prompts
• Account for location targeting in AI tools
• Make sure citations are region-specific when needed

10) A simple comparison table structure

For each region:

Region	AI Mentions	Citations	Prompt Coverage	Demand	Population	Visibility / 100k	Index

This lets you compare both raw and normalized performance.

Recommended approach

If you’re starting out, use this sequence:

1. Define regions
2. Pull AI visibility metrics by region
3. Normalize by population or search demand
4. Compare against competitors
5. Build a regional AI Visibility Index
6. Visualize in heatmaps and ranked tables

If you want, I can help you design:

• a region scoring model,
• a dashboard layout, or
• a sample spreadsheet/template for this report.