What is Proximity Targeting? Definition and Privacy Concerns

Contextual Targeting

By

Patrick Bobilin
What is Proximity Targeting? Definition and Privacy Concerns

With full cookie deprecation in Google Chrome rapidly approaching in the second half of 2024, advertisers must continue to find new ways to target their users and retain reach. The loss of behavioral targeting through third-party cookies raises a lot of question marks for advertisers who rely on the ability to track user actions, and align ad content with perceived buyer intent. 

Brick-and-mortar businesses, for example, face a question mark around how they will target potential customers in close physical proximity to their business and, more importantly, how they’ll combine physical presence with behavioral signals. 

Proximity targeting helps to answer questions around reaching prospects based on location and physical movements. But, like third-party cookies, this method of targeting is facing heightened scrutiny due to privacy concerns. 

Let’s look at the pros and cons of proximity targeting, along with potential alternatives. 

What is proximity targeting?

Proximity targeting uses mobile technology, Wi-Fi signals, and Bluetooth to deliver message, ads, and notifications to a user’s device when they are in close physical proximity to a business and have opted into those communications. 

Using cell phone triangulation, proximity targeting is able to serve hyper-local ads to target customers and prospects in the right place, and at the right time. Typically, these time and location data are combined with data from a user’s past interactions with the business’s mobile app or website, along with demographics, thereby creating both a geographical and behavioral profile of the user. By doing so, advertisers can offer promotions that are relevant and usable at the time of visitation to a store. 

Advertisers can reach prospects on their mobile devices through: 

  • Bluetooth. Businesses may use battery-operated proximity beacons, strategically stationed around the store. These beacons have dedication functionality that sends out short-range signals to all devices in the area that are connected to Bluetooth and using the business’s mobile app. Customers who both have the app, are opted-in to push notifications, and have Bluetooth turned on will receive relevant promotions and coupons when they’re in close proximity to these beacons, and as they enter and move around a store. 
  • Wi-Fi. Businesses may also offer free Wi-Fi in their stores that requires users to login to their network and provide their email address. Once connected, those users’ movements can be tracked around the store, and promotions can be delivered, depending on their actions. 

Proximity targeting has become a popular marketing technique for businesses with physical retail stores and restaurants in recent years, thanks to its ability to promote relevant products and offers in real time. As shopping habits have shifted from primarily in-person to omni-channel customer experiences across physical and online stores, brick-and-mortar stores have had to adapt their sales and digital marketing strategies to convert the fewer in-store visitors they receive. 

Of course, like with third-party cookies, the availability of user devices for targeting remains a problem for proximity marketing campaigns. Users have become increasingly aware of privacy concerns related to this ad tech, and may choose to opt out of receiving ads altogether. This makes it an imperfect solution for many retailers. 

Common privacy concerns with proximity targeting

Data privacy amongst consumers is a significant and growing trend that advertisers need to take into account. According to a data privacy survey from SAS, 73% of consumers are more concerned about privacy today than they were a few years ago. More concerning, 87% of consumers say they will not do business with a company if they have concerns about that businesses' security practices. 

Trust is a major factor in consumer shopping habits these days and, unfortunately, that is in short supply for many industries. Just 18% of respondents to the SAS survey said they trust retailers to project their data, and just 16% said the same about travel companies. 

This spells big problems for companies that rely solely on proximity targeting to reach shoppers in and around their stores. Proximity marketing requires both access to data—location, behavioral, and interests data, to be exact—and consent from the consumer to use that data for marketing purposes. 

As privacy continues to grow as a concern for consumers, their willingness to give away that data is likely to shrink. This could be a fear that their data will be used in nefarious or unethical ways, not wanting to have notifications and ads on their phones, or an uneasiness about sharing real-time locational data. 

The impending deprecation of third-party cookies will also take away an additional layer of targeting that proximity marketers use to segment users—behavioral data. A shrinking user pool, and a loss of segmentation techniques, means that proximity targeting in the traditional sense is likely to become less effective. 

What’s needed is a solution that doesn’t rely on real-time locational data or behavioral insights from third party cookies. 

Geo-targeting vs. proximity targeting 

The answer to these privacy concerns is the ability to target and segment users through a combination of geo-targeting, geo-fencing, and contextual signals. 

Geo-targeting is the practice of delivering ads or content to a consumer based on their geographic location. This is typically done through either radius targeting—i.e. targeting users within a set distance around a physical business location—or location targeting—i.e. targeting users based on a selection of zip codes, town, cities, and so on. This technique is great for generating brand awareness, and for promoting location-specific campaigns and offers.

This type of targeting is not tied to a single individual, and does not actively track or interact with a user in real-time when they are within a defined physical space. Often, however, geo-targeting required third-party cookies to both isolate users, and layer on additional behavioral targeting parameters.

Proximity targeting, as we’ve discussed, is tied to a location-based advertising technique that’s tied to a specific time and place. The business goal is to make ads as relevant and timely as possible, when a user is in physical proximity to a store and, therefore, in potential buyer mode. Once users move away from that physical location, proximity-targeting no longer works. 

While both marketing techniques have their merits, they’re imperfect on their own. Proximity targeting, as explained, raises privacy issues and may lack access to users due to opt-outs. Geo-targeting will eventually become limited due to the loss of behavioral segmentation through third-party cookies. 

Combining contextual targeting with geo-targeting is the answer. This ensures that advertisers can both segment users based on interest and intent, while also being able to drill down to specific physical locations. 

How to meet user privacy needs online while optimizing spend 

Contextual advertising is a form of targeted, programmatic targeting that shows ads to users based on content of a specific web page, video, social media feed, or any other supported location in a web browser. This is done through the analysis of content on a page using machine learning and algorithms to understand that content’s context, intent, and sentiment. 

Advertisers, using tools and data providers like Peer39, can target that relevant content using contextual signals and categories that map to the desired persona, including their specific locations in the world. 

An example of targeting parameters that are available through contextual categories on Peer39 include: 

  • Keywords
  • Topics
  • Negative keyword
  • Negative topics 
  • Location and language
  • Weather data
  • Time-based intersection (where and when)

Each of the above categories can be layered, refined and segmented down to a specific target audience, thereby creating a deeper level of granularity for targeting, and transparency around performance metrics. Most importantly, contextual targeting can do this without collecting any personal data from the user, thus alleviating privacy concerns often associated with location-based marketing. 

As third-party cookies phase out for good, and user awareness and concern over data privacy increase, advertisers will have fewer and fewer options for how they target and engage with prospects. Contextual targeting is a privacy-first solution that can help fill this void. 

Thanks to recent integrations, we're focusing on location-based audiences to marry user privacy and contextual targeting.

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