Ads targeted by location help you reach the right customers at the right time; learn how to set and refine geographic settings in Google Ads using official guidance at Target ads to geographic locations – Google Ads Help. You’ll understand bid adjustments, radius targeting, exclusion zones, and how location reports inform optimization so your campaigns drive more relevant traffic and conversions.
Key Takeaways:
- Pick the right targeting option (people in, people who show interest, or both) to control whether ads reach local residents or anyone searching about a location.
- Combine location types-country, region, city, postal code, and radius-to match campaign goals and scale while using exclusions to prevent wasted spend.
- Adjust bids and budgets by geography using location bid adjustments or separate campaigns for high-value areas to maximize ROI.
- Enable location extensions and link Google Business Profile to surface addresses, drive store visits, and track local conversions.
- Use geographic and user-location reports to identify top-performing areas, exclude poor performers, and test tailored creatives by region.
Understanding Location Targeting
Definition of Location Targeting
You set precise geographic parameters-country, region, city, ZIP/postal code, or a radius around an address-to control where your Google Ads appear. You can choose “people in” versus “people interested in” and exclude locations to prevent wasted spend. This lets you align bids and creatives with local demand, for example promoting a storefront within a 5-10 mile radius or running city-specific offers during events.
Importance of Location Targeting in Advertising
Narrowing geographic targeting improves efficiency and local relevance: you’ll cut impressions from out-of-area users, lower cost-per-acquisition, and lift conversion rates. For example, focusing on a 10-mile radius around stores and importing offline sales data lets you tie ad spend to actual store visits and purchases. Use location bid adjustments to invest more where foot traffic or lifetime value is higher.
Test multiple geographies and bid multipliers: split-test city versus radius targeting, apply negative locations, and combine with ad scheduling and audience layers. You should monitor CTR, conversion rate, and CPA by location and reallocate budget to top-performing ZIPs or cities; many advertisers shift 20-40% of local spend to high-performing pockets after initial testing to maximize ROI.
Types of Location Targeting Options
Choose among several targeting types to match your campaign goals: country, region, city, postal/ZIP code, and proximity. You can allocate budget and bids differently by location, exclude underperforming areas, and test geographies for lift; for example, launch in three metro areas (New York, Chicago, Los Angeles) to compare performance. Any option can be layered or combined to sharpen reach and control spend.
- Country
- Region/State
- City
- Postal/ZIP code
- Radius (proximity)
| Country | Broad reach for international or nationwide campaigns; useful when scaling product launches. |
| Region/State | Target specific states/regions to match distribution or legal availability (e.g., 5-state rollout). |
| City | Focus on metro performance-target top 3-10 cities and set city-level bid adjustments. |
| Postal/ZIP code | Pinpoint neighborhoods for direct mail matches, local promotions, or service-area businesses. |
| Radius (proximity) | Set a distance around an address (e.g., 1, 3, 10 miles) to capture nearby intent and foot traffic. |
Geographical Targeting
When you use geographical targeting, you select administrative boundaries like countries, states, cities, or postal codes to align spend with market availability; for instance, you might target five metropolitan areas where distribution exists and increase bids by 10-20% in top-performing cities. You should exclude areas with low conversion rates and monitor metrics by location to reallocate budget efficiently.
Radius Targeting
Radius targeting lets you define a circle around an address or GPS point-common for stores, services, and events-so you can target users within a specific distance (examples: 1, 3, or 10 miles/km). You can prioritize immediate proximity by applying higher bid adjustments closer to the center to capture higher-intent, local traffic.
More strategically, combine radius targeting with ad scheduling and bid modifiers: test rings (1, 3, 10 miles), raise bids during peak hours (e.g., lunch or commute), and measure outcomes like calls or store visits to calculate CPA by radius, then iterate based on performance data.
How to Set Up Location Targeting in Google Ads
Begin by defining campaign-level locations and then refine with radius, city, postal code, or location groups; for example, use a 5-15 mile radius for service-area businesses and country-level targeting for national e-commerce. Link your Google Business Profile to import storefronts, upload location feeds for dozens of outlets, and apply exclusions to remove adjacent zip codes. Use campaign settings to set bid adjustments by location and monitor the “Location” report to spot areas with low conversion rates within the first 2-4 weeks.
Account Setup
Set your account time zone and currency correctly at creation since those cannot be changed later, and enable conversion tracking before launching local campaigns. Connect Google Business Profile to sync addresses and use a location feed for 10+ stores to manage at scale. Also configure location extensions, verify business hours, and grant account access to team members so you can run tests across campaigns without losing historical data.
Target Audience Selection
Choose geographic granularity that matches intent: target cities, ZIP/postal codes, or custom radii (common ranges: 1-50 miles). Opt for “people in or regularly in your targeted locations” rather than “interest” if you want actual local foot traffic. For instance, a dentist typically targets a 5-15 mile radius, while a B2B SaaS vendor might target entire states or countries; set location bid adjustments where CPLs differ by region.
Segment performance by location and device daily for the first 2-4 weeks, and exclude underperforming zones-if a suburb shows conversion rate <0.5% and CPA 3× higher than core areas, remove or reduce bids there. Use store visit data or offline conversion imports to validate lift, run A/B tests on 3-4 radius sizes, and apply negative locations to block neighboring metropolitan areas that drive irrelevant clicks.
Best Practices for Effective Location Targeting
You should layer audience and location signals: combine radius, city, and ZIP targeting with location groups (stores, demographics) to focus spend where conversion rates are highest. Test A/B campaigns for at least 30 days or 1,000 clicks to detect meaningful differences, and use negative locations to block poor-performing areas. For example, retailers often see 15-30% higher in-store conversion within a 5-10 mile radius of flagship stores, so prioritize those zones in your bid strategy.
Researching Target Markets
Use Google Analytics location reports, Search Console queries, and Google Trends to compare demand by city, ZIP, or DMA; analyze 90-day conversion rates and average order value to spot high-value pockets. You can supplement with census or proprietary CRM data-household income brackets, population density, and local competitor presence-to decide whether to target a whole metro, specific ZIP codes, or just a 5-10 mile radius around top-performing stores.
Adjusting Bids Based on Location
Apply location bid adjustments where metrics justify it: raise bids for areas with higher ROAS or lower CPA and decrease bids where cost per conversion spikes. For instance, if a ZIP code delivers 3× ROAS versus campaign average, consider increasing bids by 50-200% or switching that ZIP to its own campaign with tailored creative and landing pages to maximize efficiency.
Dig deeper by using granular reporting: set bid modifiers at the campaign or ad group level and monitor performance weekly, then tie adjustments to absolute metrics-cost/conversion, conversion rate, and lifetime value. Google Ads allows wide modifier ranges (e.g., up to +900%), so scale gradually-try +20%, +50%, then +150%-and use automated bidding (target CPA/ROAS) with location-level exclusions when data volume supports reliable signals.
Analyzing Location Targeting Performance
Use city- and ZIP-level splits to identify where your ads drive the best ROI: compare CTR, conversion rate, cost-per-conversion and impression share side-by-side. You might find downtown neighborhoods delivering a 3.2% conversion rate versus 1.1% in outer suburbs, or a ZIP boosting ROAS 2.5× after a weekend promotion. Track results over 30-90 day windows to smooth seasonality before making changes.
Using Google Ads Reports
Open the Locations and User locations reports, then apply the Geographic dimension to break performance by city, region, DMA or postal code. Segment by device and time-of-day to spot micro-trends; for example filter to mobile in downtown areas where CTR rose 18% during lunch hours. Export CSVs or connect BigQuery for multi-campaign analysis across 90 days.
Making Data-Driven Adjustments
Adjust bids and exclusions based on statistical thresholds: increase bids +25% for locations with conversion rates at least 2× your account average, decrease bids by 20-40% for high-CPA areas, and exclude ZIPs with zero conversions and CPA above target after 60 days. Use radius expansions around high-performing pins to capture nearby demand.
When implementing changes, run controlled tests by creating campaign drafts or experiments and adjust bids in 10-25% increments for a 14-30 day test window, tracking conversions, CPA and impression share. Automate rules to lower bids by 30% if CPA exceeds your target for 7 consecutive days or to pause locations with fewer than three conversions in 60 days. Consider shifting winning regions to a target-ROAS strategy and pairing location-specific ad copy and sitelinks. For example, a regional retailer tested a +15% bid on five high-ROAS ZIPs and saw revenue increase 28% while CPA fell 12% after 30 days; scale winners and rollback losers.
Common Challenges and Solutions
Many advertisers encounter recurring problems: target misalignment, overlapping campaigns, and inaccurate location signals that inflate costs. You can cut wasted spend by auditing location reports, comparing city- and ZIP-level CTR/conversion splits, and aligning bid adjustments to local performance; for example, splitting campaigns by postal code has driven conversion rate lifts of 10-20% in tested retail accounts. Apply exclusions and adjust location intent settings to keep ads focused on the most profitable geographies.
Misalignment of Target Locations
If your campaign targets a 10-mile radius around downtown but your customers come from adjacent suburbs, you’ll see low store visits and wasted clicks. You should compare “people in” vs “people interested in” settings, use ZIP/postal targeting for physical storefronts, and exclude noncore neighborhoods. In practice, switching one local client from broad radius to ZIP-level targeting reduced irrelevant traffic by 28% and increased in-store conversions.
Overlapping Target Areas
Overlaps occur when multiple campaigns or ad groups target the same city or ZIPs, causing internal bidding against yourself and higher CPCs; some accounts see CPCs rise by 15-30% from unmanaged overlap. You should consolidate targeting, set campaign priorities, and apply negative locations so one campaign owns each geo segment. Monitoring shared ZIP lists and assigning unique bid strategies prevents bid cannibalization and keeps CPA steady.
To dig deeper, run a location overlap audit: export targeted ZIPs for all campaigns, calculate intersection percentages, and flag overlaps above 50%. Then centralize geo ownership-assign core Z ips to a primary campaign, push adjacent Z ips to prospecting campaigns, and use inventory-based bid adjustments. You can also automate detection with a script that alerts when two active campaigns share more than a defined percentage of targeted postal codes, enabling rapid corrective action.
Final Words
As a reminder, location targeting lets you focus budget where your customers are, refine radius and exclusion lists, adjust bids by region, and tailor ads and landing pages to local intent. You should monitor geo-performance, test segments, and combine location with schedules and device bids to maximize ROI. Use reporting and experiments to refine your settings continually.
FAQ
Q: What is location targeting in Google Ads and how does it work?
A: Location targeting lets you control where your ads are shown by specifying geographic areas – countries, regions, cities, postal codes, or a custom radius around a point. You can target positive locations to include areas where you want visibility and set exclusions to prevent ads from showing in certain places. Google matches user location based on physical location signals (IP, GPS, device location) and inferred interest (search or content relevance) depending on campaign settings. Location targeting can be applied at campaign or ad group level and interacts with location extensions, call extensions, and local campaigns to surface location-relevant assets.
Q: Which targeting options are best for local storefronts versus broader campaigns?
A: For storefronts or service-area businesses, use radius (proximity) targeting around the store address and location extensions to show distance and directions. Combine radius targeting with “people in or regularly in your targeted locations” to prioritize physical visitors. For city- or state-level coverage, target specific administrative areas and refine with exclusions for nearby regions where you don’t operate. For national or multi-region campaigns, use broader geo-targets and segment campaigns by region to apply tailored bids, budgets, and creatives. Use location groups (like business locations, demographics, or custom rule sets) to target clusters of stores or audiences without listing individual zip codes.
Q: How do presence and interest location settings affect who sees my ads?
A: Presence vs. interest determines whether Google shows ads to people physically in the targeted location or to users who show interest in that location. “People in or regularly in your targeted locations” targets users physically present or with consistent presence signals – best for driving in-store visits. “People in, regularly in, or who show interest in your targeted locations” includes users searching or browsing about the location, which can broaden reach but may show ads to remote searchers planning travel or researching. Choose presence for performance tied to foot traffic and the broader option for awareness or lead-gen where contextual interest is acceptable.
Q: What are practical optimization tactics for bids, ad copy, and landing pages by location?
A: Use location bid adjustments to increase bids where conversion rates or LTV are higher and decrease bids where performance is poor. Create location-specific ad copy and sitelinks (city names, offers valid in that region, store hours) to improve relevance and CTR. Implement location extensions and structured snippets with store details. Localize landing pages with relevant addresses, inventory availability, and localized messaging or currency. Monitor device and time-of-day performance per location and layer bid adjustments accordingly. For larger accounts, split campaigns by geography to control budgets and test creative variations per region.
Q: How should I measure location targeting performance and avoid common mistakes?
A: Use the “Geographic” and “User locations” reports in Google Ads to see where impressions, clicks, and conversions originate and to detect discrepancies between targeted areas and actual user locations. Segment by device, time, and distance (for radius campaigns) to find patterns. Common mistakes include overly broad targeting (wasting budget on irrelevant regions), failing to exclude nearby areas with poor performance, and not aligning landing pages with targeted locations. Also watch for attribution delays with store visits and cross-device behavior that can underreport local conversions. Adjust targets, apply exclusions, and run controlled tests before scaling changes.