Google Ads geographic reports give you three distinct data views — User Location, Geographic, and Locations — that reveal where your budget performs and where it leaks. Read each report for what it actually measures, apply bid modifiers by location tier, and audit targets against real conversion data to tighten spend.

Most advertisers set a location target, launch a campaign, and never look at where their clicks actually originate. That gap between where you think you’re advertising and where conversions actually happen is where budget disappears quietly. Geographic reports close that gap — but only if you know which report to pull and what action to take from it.

This guide walks through all three major Google Ads geographic reports, what each one measures, and the specific steps to turn that data into tighter targeting and better return on ad spend. The same structured thinking that applies here — audit first, then act — is the foundation of sound operations work. If you’re applying that logic to internal processes as well, OpsMap discovery follows the same principle before any automation is touched. For teams looking at broader workflow visibility, 7 questions to ask before you automate anything provides a useful pre-action checklist.

Before diving into steps, here is a quick-reference table of the three reports and their primary use cases:

Step 1: Pull the User Location Report and Segment by Geography Tier

The User Location report is the cleanest geographic data source in Google Ads. It shows performance broken down by country, region, city, DMA, and in some cases zip code — regardless of how broad your original targeting was.

That last point matters. If you targeted the entire United States, this report does not show you one row for the US. It shows you rows for every state, city, and region where impressions, clicks, and conversions actually occurred. You are not limited to the aggregated level of your target.

How to Access It

1. In Google Ads, navigate to Predefined Reports.
2. Select Geographic.
3. Click User Locations.
4. Use the dimension menu to toggle between country, region, city, and zip code views.

What to Do With the Data

1. Sort by conversions descending. Identify the top 10 geographic units driving results.
2. Sort by cost descending with zero conversions. These are your budget leaks.
3. Export both lists. You will use them in Step 3 to set bid modifiers and exclusions.
4. Look for city or state-level patterns that do not match your assumptions. A region you never explicitly targeted is sometimes your best cost-per-conversion source — the data will show it clearly.

The User Location report is especially useful when running a single national campaign and evaluating whether to break it into regional campaigns with tighter controls. It also integrates cleanly with external data sources — you can match it against your CRM’s customer geography to validate whether your best customers and your best ad performance align. For a parallel look at how data visibility drives better operational decisions, see how manual data gaps silently drain productivity.

Step 2: Run the Geographic Report to Capture Intent-Based Reach

The Geographic report looks nearly identical to the User Location report, but it adds a critical column: Location Type. This column distinguishes between two user categories:

• Physical location: The user is physically present in the location.
• Location of interest: The user is located elsewhere but searching about or for the specified location.

By default, Google Ads targets both groups simultaneously. That is not wrong — but it means your performance data is a blend of two fundamentally different user behaviors unless you separate them.

How to Access It

1. Navigate to Predefined Reports > Geographic > Geographic.
2. Add the Location Type column if it is not already visible.

What to Do With the Data

1. Filter to show only Location of interest rows. Compare conversion rate against your Physical location rows for the same geography.
2. If interest-based traffic converts at a significantly lower rate, consider adjusting your location settings to Presence only — this removes users who are searching about a location but are not physically there.
3. Conversely, if interest-based traffic converts well (common for travel, real estate, and relocation services), keep both options active and note which geos drive the strongest interest-based conversions.

This distinction is one of the most underused levers in Google Ads. Advertisers who sell locally but unknowingly attract out-of-market searchers are bleeding budget on traffic that converts at a fraction of the rate of in-market visitors. Separating the two in this report surfaces that problem in minutes.

The same principle — separating signal from noise before acting — applies across operations. See why auditing before automating produces better outcomes for a related framework.

Step 3: Review the Locations Tab and Apply Bid Modifiers

The Locations tab (accessed directly inside a campaign, not through Predefined Reports) shows performance data for the geographic targets you have already set. This is where you translate the insights from Steps 1 and 2 into actual bid adjustments.

How to Access It

1. Open the specific campaign you want to review.
2. In the left navigation, click Locations.
3. You will see rows for each active geographic target with full performance metrics.

How to Apply Bid Modifiers

1. Click the bid adjustment column (or the pencil icon) next to a location.
2. Enter a positive modifier (e.g., +20%) for high-converting locations identified in Step 1.
3. Enter a negative modifier (e.g., -30%) for locations with high spend and low conversion rates.
4. For locations with zero conversions and meaningful spend, add them as exclusions rather than applying a negative modifier — exclusions are cleaner and do not allow partial reach.

Bid modifiers compound with other adjustments (device, audience, time of day), so apply them methodically. Start with the clearest signals — your top 5 converters and your top 5 budget drains — before making smaller adjustments across a full list.

Step 4: Cross-Reference All Three Reports Before Making Final Changes

Each of the three reports tells a different part of the story. Acting on only one gives you an incomplete picture. Here is how to use them together:

• User Location report → Tells you where users are physically located when they click and convert.
• Geographic report → Tells you whether those users are locals or out-of-market searchers with interest in the location.
• Locations tab → Tells you how your active targets are performing so you know where to adjust bids or add exclusions.

A common mistake is using only the Locations tab, which only reflects targets you have already set. If you have a national target, you see one row — not the city-by-city breakdown visible in the User Location report. Running all three gives you the full picture before you act.

Step 5: Build an Exclusion List From Underperforming Locations

Adding location exclusions is one of the fastest ways to improve campaign efficiency without touching creative, bids, or keywords. The logic is straightforward: if a geographic area is consuming budget and not converting, stop showing ads there.

How to Add Location Exclusions

1. Inside the campaign, go to Locations.
2. Click the Location exclusions tab.
3. Click Edit and search for the location to exclude.
4. Add exclusions at the most granular level possible — city exclusions are cleaner than state exclusions unless state-level patterns are consistent.

What to Exclude

• Locations with 50+ clicks and zero conversions over a 90-day window.
• Locations with cost-per-conversion more than 3x your account average.
• Out-of-market interest-based locations identified in Step 2 that convert at a low rate.

Build and maintain this list as a running document. Every 30 to 60 days, re-run the User Location report and compare against your exclusion list to catch new underperformers before they accumulate significant wasted spend.

How to Know It Worked

After applying bid modifiers and exclusions, give campaigns at least 2 to 4 weeks to accumulate data before evaluating results. Look for these indicators:

• Cost-per-conversion decreasing — Spend is concentrating in higher-performing locations.
• Conversion rate increasing — Traffic quality improves when low-converting geos are excluded.
• Impression share stable or improving in target regions — Budget freed from excluded areas flows back into priority markets.
• Geographic report showing cleaner Location Type split — If you adjusted presence settings, the intent-based mix should reflect your decision.

Re-run all three reports after each optimization cycle. Geographic performance shifts over time — seasonal patterns, local events, and competitive changes all affect where your ads perform best. This is not a one-time fix; it is a recurring audit cycle.

Common Mistakes When Using Geographic Reports

• Using only the Locations tab: This shows only active targets. It misses where users actually are when they click. Always run the User Location report to find gaps.
• Ignoring Location Type in the Geographic report: Blending physical and interest-based traffic without reviewing each separately obscures real performance differences.
• Applying exclusions too broadly: Excluding an entire state because one city underperformed cuts off potentially strong volume. Work at the most granular level the data supports.
• Acting on small samples: A location with 3 clicks and zero conversions is not a meaningful signal. Use 90-day windows with meaningful click volume before drawing conclusions.
• Never revisiting exclusions: Markets change. A location that underperformed 6 months ago deserves a fresh look before staying permanently excluded.

Additional Reading

• 7 Questions to Ask Before You Automate Anything (The OpsMap Checklist)
• What Is OpsMap? The Discovery Step That Prevents Automation Mistakes
• Manual Data Entry: The Silent Killer of Business Productivity & Profit
• What Is Automation-First? Why You Should Automate Before You Add AI
• OpsMap vs. Skipping Discovery: What Happens When You Automate Without a Map