New research finds that ad effectiveness depends on more than just how close a consumer is to your store—it matters how close they are to your rival.
Nearly 90% of consumers say they prefer personalized ads to non-personalized ones—and location is becoming one of the most powerful personalization signals available. In 2025, advertisers spent at least $57 billion on location-targeted campaigns, a figure growing well above the broader ad market.
The technology has kept pace: Connected TV and IP-based targeting can now deliver different ads to different households on the same block. Retailers like Kroger and Albertsons have partnered with media companies—Disney Advertising and Omnicom Media Group, respectively—to share first-party customer data for sharper targeting. Even non-advertising platforms like Uber are entering the space, seeking to monetize their granular rider location data. The plumbing for precision is in place. The strategies deployed on that plumbing have not kept up. The dominant approach to location-based advertising remains remarkably simple: Draw a radius around each store, target everyone inside it, and assume that proximity equals responsiveness. This is the default geotargeting option on Google Ads, Meta, and most programmatic platforms. It reflects a sensible intuition—customers who live closer face lower travel costs—but it ignores critical dimensions of how advertising actually works in physical retail markets. In a new study, we question this uniform radius-based targeting strategy for advertising. We analyze millions of retail store visits across the U.S., mapping to consumers’ home block groups , and combine this with their TV advertising exposures. Because traditional TV ads are shown uniformly within a media market, we could observe how the same ad exposure translates into different visit responses depending on customers’ proximity. Our analysis spanned over six years and multiple retail industries, from home improvement to grocery to department stores. We found that, rather than simple proximity, the location of competitive rivals significantly impact how customers respond to advertising. The implications for companies are considerable. Targeting customers who would visit you anyway or who were never going to visit your store could cost millions of dollars in wasted exposures. A Better Predictor of Ad Effects In our initial study, we examined the home improvement industry. Home Depot and Lowe’s carry similar assortments at similar prices and so compete primarily on location convenience. Instead of measuring sheer proximity , we measured customers’ relative proximity . For example, if, within a five-mile radius there was a both a Lowe’s and a Home Depot, which ads will be most successful? We found that customers who live closer to the advertising retailer than to the rival respond more to that retailer’s ads—advertising to this geographic segment translates to meaningfully higher store visits. The gap in responsiveness between “closer-to-us” and “closer-to-rival” customers is also substantially larger than the gap between customers who are simply “close” versus “far” from the store. In other words, targeting customers who are closer to you than to your rival outperforms a simple radius strategy. When we subdivided the market into four segments defined by both absolute and relative proximity, the pattern sharpened further. Among customers who are far from the focal store but relatively closer to it than to the rival, advertising effects were the strongest in our entire sample . These are exactly the customers that a radius-based strategy would exclude because they live outside the conventional targeting circle. But they are the most persuadable through advertising to visit the retailer closer to them. See more HBR charts in Data & Visuals Critically, this finding is not specific to home improvement. We replicated the analysis across mass merchandisers , grocery stores , pharmacies , and department stores . The pattern holds in every case: Relative proximity is a significant factor across all of these industries, even among retailers that are perceived as less similar to each other than Home Depot and Lowe’s and across both urban areas and less-densely populated regions. This result highlights a major gap in current advertising platform design. Today, geotargeting tools let you set a radius around your own store. However, they do not let you filter to customers who are closer to you than your competitor. Our evidence suggests this second dimension is more important than the first. Platforms that incorporate competitor location into their targeting infrastructure would offer a substantially better product. Other Factors That Impact Ad Effectiveness While competitive geography is the strongest factor we identified, our study identified three other dimensions of spatial targeting that also matter: distance, ad type, and where consumers work. Distance has a U-shaped impact The relationship between distance and ad effectiveness turns out to be less straightforward than most marketers assume. Conventional targeting assumes that ad effectiveness declines steadily with distance—the farther away a customer lives, the less responsive they are. In many categories, we found something different: an inverted U-shape. Customers in the closest distance quartile showed weaker advertising effects than those in the second and third quartiles . The most responsive segment sat at a moderate distance from the store. Two opposing forces explain this. Travel cost does eventually dampen responsiveness at far distances, namely, over 14 miles. But very close proximity dampens it too: Customers who live near the store already know what it carries, drive past it regularly, and have it on their mental radar. For them, an ad provides little new information—a “billboard effect,” where the physical storefront is already doing the advertising for you. We found that the strength of this effect depends on what the store sells. In categories with stable product assortments—home improvement, grocery, drugstores—nearby customers have little to learn from an ad, and the inverted U-shape is pronounced. In categories where assortments are refreshed frequently, such as department stores, even nearby customers face real uncertainty about what is currently in stock, making advertising more effective among these customers. There, the conventional pattern holds: Closer customers are more responsive because travel cost is the dominant factor and the billboard effect is weaker. The practical implication? The optimal targeting zone may not be a circle but something more like a donut, excluding the innermost ring where ads are redundant and focusing on the moderate-distance band where they change behavior. When combined with the relative proximity finding, the ideal segment may look like a slice of that donut: customers at a moderate absolute distance who are also relatively closer to you than to the competitor. Whether the donut applies in your category depends on how much your nearest customers already know about your offerings. Ad type changes the map Additionally, we found that not all ads benefit from the same spatial strategy. In our home improvement analysis, where we carefully classified ads by content, we found sharp differences between promotional and brand-building campaigns. Price-promotional ads—highlighting temporary deals and discounts—are most effective among customers who are relatively close to the store in absolute distance. This makes sense: A time-limited discount is novel information for everyone, but it disproportionately motivates those with low travel costs to make the trip. Brand-building ads—emphasizing product quality, store experience, and brand—tell a different story. They are ineffective among the closest customers, where the billboard effect is strongest. Their peak effectiveness is at moderate distances, among customers who are spatially predisposed to visit but need a nudge. These ads serve as reminders, and reminders matter most for people who have latent intent but lack the prompt to act. The upshot: One geofence should not govern all campaigns. Promotional ads warrant tighter targeting among nearby, contested customers. Brand and reminder ads warrant broader reach at moderate distances among customers who are closer to you than to your rivals. Work location matters as much as home location Most location targeting is built around where customers live. But people also shop on their way to and from work, and our data allowed us to test whether work location matters too. Using customers’ daytime locations as a proxy for where they work, we found that work-location proximity works in the same way that home-location proximity does. A customer whose workplace is relatively closer to your store than to the rival’s is more responsive to your ads, even if their home is not. Targeting based on both home and work locations could reach persuadable customers that a home-only strategy would miss, particularly in urban markets where commuting patterns create large separations between the two. A New Strategy for Location Targeting These findings matter now because the technology to act on them has arrived. Connected TV and IP-based ad delivery can target at the household level. Retailer data partnerships are making first-party location data available to media platforms. Emerging AI tools and algorithms that ingest competitor location data, customer movement patterns, and campaign type to dynamically adjust targeting zones are increasingly within reach for retailers with access to the underlying data. And the precision of that data matters enormously. When we compared block group–level relative proximity measures to zip code–level or county-level approximations, the correlations dropped substantially. Broader data obscures exactly the spatial patterns that make targeting effective. Therefore, platforms that allow for precise geotargeting with AI algorithms at scale stand at a significant advantage. We offer executives four steps they can take to make their advertising more successful: 1. Incorporate competitor locations into your targeting logic. Within your targeting radius, prioritize areas where you are the closer option. This requires overlaying competitor store locations onto your targeting maps—a straightforward data exercise that most ad ops teams can execute today. Our evidence suggests this single change would improve targeting more than any refinement to the radius itself. 2. Test distance bands, not just radii. Run holdout experiments by distance ring to verify whether your innermost zone is actually the most responsive. In stable-assortment categories, it often is not. At minimum, distinguish between “close” and “moderate” distance segments and measure their response rates separately. 3. Vary spatial rules by campaign. Promotional campaigns and brand campaigns should not share the same geofence. Match the targeting shape to the ad’s mechanism—tighter for promotions that need low travel cost to convert, broader for brand messages that need to remind. 4. Push your ad platforms for richer targeting. Radius-based targeting is an artifact of a simpler era. The data infrastructure now supports conditioning on competitor proximity, distance bands, and campaign type simultaneously. Retailers and advertisers should demand these capabilities, and platforms that build them will have a meaningful competitive advantage. . . . A few caveats are worth noting. Location may matter less in markets where retailers are highly differentiated on brand and price tier . Similarly, our analysis focuses on physical retail where ads drive store visits; businesses that are primarily online, or service businesses where customers don’t choose based on travel cost, operate under different dynamics. Finally, the billboard effect—which suppresses ad responsiveness among the closest customers—depends on the nature of the product. Retailers with fast-changing inventory or frequent new arrivals may find that their nearest customers are in fact their most responsive, as we observed with department stores. That said, for the many categories where competitors offer similar products and differ primarily on location—the Home Depots and Lowe’s, Walmarts, and Targets of the world—our findings on competitive geography effects are remarkably robust and should be taken seriously by those firms seeking to seize a competitive advantage. The technology revolution in location advertising as yet has been primarily about data and delivery. But, as our study shows, who wins in this space will depend in large part on their strategy.
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