A circle on a map is the laziest possible answer to where your patients actually come from. Multi-location healthcare brands that still target by radius are overpaying for impressions in the wrong places and underpaying for the patients they could reach
Geographic targeting is the most underappreciated lever in healthcare paid advertising. The geographic decisions an account makes — which areas to target, how to weight bids by location, where to exclude entirely, how to allocate budget across locations — determine which patients see the ads, how much each impression costs, and ultimately which campaigns produce acquired patients and which produce expensive irrelevance.
For single-location practices, the geographic decision is at least simple even when it is poorly executed. For multi-location healthcare brands — practice groups with several offices, private-equity-backed platforms, or specialty groups serving large metropolitan areas with multiple sites — the geographic decisions are far more consequential. A multi-location brand that runs all its locations on a single set of geographic settings is essentially running one of its locations correctly and the others on autopilot, regardless of which one was used as the template.
Why Radius Targeting Fails First
The most common geographic configuration — a circular radius around each location — is also the configuration that fails first as a brand scales.
A radius treats every direction equally. Three miles north of the practice is treated identically to three miles south. In actual practice, patients rarely come from equally in all directions. Population density varies. Traffic patterns and commute corridors create asymmetric draw. Competitors are clustered in some directions and absent in others. The actual catchment area of a medical practice almost never resembles a circle.
A radius also overlaps with neighboring locations for multi-location brands. Two practices five miles apart with three-mile radii produce one mile of overlap where both campaigns are competing for the same impressions. Both locations pay for the same audience. Both campaigns report performance, but the brand as a whole pays twice for impressions that could have been served once.
A radius does not respect natural geographic boundaries. A river, a highway, a city line, or a major shopping district often defines the edge of a practical catchment area more accurately than a distance measurement. Patients on one side of an interstate may rarely cross to the other side for routine care; a radius does not know this.
What Better Geographic Targeting Looks Like
Better geographic targeting starts from actual patient draw, not from a default radius. The data that informs it usually lives in the practice’s own scheduling system, where patient ZIP codes or addresses reveal where patients actually come from.
Building a target geography from that data is straightforward. Pull the ZIP codes of new patients over the past twelve months. Group them by location for multi-location brands. Identify the ZIP codes producing the highest share of new patients, the ZIP codes producing meaningful but lower volume, and the ZIP codes producing essentially none. Use those tiers to construct targeting that bids more aggressively where patients actually come from and less aggressively or not at all where they do not.
This data-driven targeting almost always outperforms radius targeting on the same budget, because it concentrates spend on areas with demonstrated patient draw rather than spreading it evenly across a circle that includes both productive and unproductive areas.
Location-Based Bid Adjustments
Beyond which areas to target, the practice can adjust how aggressively to bid within targeted areas. Google Ads supports location-based bid adjustments — increasing bids by a percentage for high-value locations, decreasing them for lower-value locations.
For a multi-location brand, this allows a single campaign to weight its bidding by location performance. The high-performing ZIP codes get aggressive bids that capture more impressions. The marginal ZIP codes get conservative bids that maintain presence without burning budget. The unproductive ZIP codes are excluded entirely.
Setting these adjustments requires data — usually at least several months of campaign performance segmented by location — but once in place, they continue to improve campaign efficiency without ongoing intervention. They are one of the highest-leverage optimizations available, and one of the most consistently underused.
Multi-Location Architecture
For brands with multiple locations, the question of how to structure campaigns geographically has several reasonable answers, and the right one depends on the brand’s specific configuration.
One campaign per location is the most common structure and works well for brands where each location operates relatively independently. Each campaign has its own budget, its own geographic targeting, its own location-specific landing pages, and its own performance data. The tradeoff is account complexity — more campaigns to manage, more reporting surfaces — but the visibility into per-location performance is worth it for most multi-location brands.
Combined campaigns with location-based bid adjustments work for brands where locations are similar enough that they can share creative and structure. The campaign targets a broader area, with bid adjustments concentrating spend near each location. This structure simplifies management but reduces visibility into individual location performance.
Hybrid structures — campaign per location for core service lines, combined campaigns for cross-location services — fit brands with mixed service models. A multi-location dermatology brand might run separate campaigns per location for general dermatology while running a combined campaign for a destination procedure offered at only one of the locations.
Location Exclusions Matter as Much as Inclusions
What to exclude is often as important as what to include. Several categories of exclusion improve campaign efficiency significantly.
Locations the practice does not actually serve. A practice in a metropolitan area with patients limited to a defined service area should explicitly exclude the parts of the metro it does not serve. Without exclusion, the campaign may serve impressions to those areas even when they are outside the practice’s effective reach.
Locations dominated by competitors. Some neighborhoods are so densely served by competing practices that the cost of competing for impressions there is not worth the marginal patient acquisition. Strategic exclusion of these areas reallocates budget to areas where the practice can compete effectively.
Locations producing low-quality leads. Some areas, even within an apparent service radius, produce inquiries that consistently fail to qualify — wrong insurance, wrong service expectation, wrong demographic fit. Identifying these areas through performance data and excluding them improves overall lead quality even when it reduces lead volume.
The People-in-Location vs. People-Searching-for-Location Distinction
Google Ads offers a setting that determines whether geographic targeting applies to people physically in the target area, people searching for the target area, or both. This setting matters more for healthcare than most advertisers realize.
For local healthcare practices, the appropriate setting is generally “people in or regularly in your targeted locations.” Patients who are physically in the area are the ones who can practically attend the practice. Patients searching for the area while located elsewhere — researching a future move, browsing on behalf of family, or simply traveling — convert at dramatically lower rates and consume budget that should reach local audiences.
Many accounts run on the default “people in, regularly in, or who have shown interest in your targeted locations” setting, which includes the searching-for-but-not-in category. For most local healthcare contexts, this setting wastes budget on lower-quality impressions, and changing it produces meaningful efficiency gains.
Multi-Location Reporting Discipline
All of this only works if the brand has the reporting in place to evaluate per-location performance. The most common pattern in multi-location healthcare brands is account-level reporting that obscures location-level differences.
Aggregate metrics hide important variation. A multi-location brand whose overall cost per acquired patient is acceptable may have one location operating efficiently and three operating poorly, with the average looking fine. Without per-location reporting, the underperforming locations remain invisible.
Per-location reporting — cost per acquired patient, lead quality, show rate, and downstream revenue, broken out by location — is the prerequisite for making location-specific decisions about budget, targeting, and bid strategy. Brands that have this reporting can optimize each location individually. Brands that do not are essentially running every location the same regardless of what each location actually needs.
