Want to confuse someone? Start talking about the importance of geotargeting, user profiles and predictive data modeling.
Now ask them if they’ve checked into Foursquare yet.
Geolocation is hot. As in, $150-million-dollars hot. Why shouldn’t it be? It blends mobile, contextual information, gaming and serendipity into a relevant experience, enabling users to change their behavior based on the actions of their friends (or enemies).
“Checking-in” isn’t just colloquial geek-speak; it’s the new human behavior.
Have you thought about the true intention of these tools?
Despite what detractors may say, Foursquare doesn’t exist to help burglars rob your house while you’re away (pleaserobme.com). Strangers probably don’t care about your exploits/check-ins. As marketers, however, we should be concerned with the aggregate data being collected and categorized around points on a map. How can we leverage it to our advantage? How is this information pertinent to your brand and objectives?
“Open by default” (meaning that information a user posts on a social site is, by default, available to the public) has led to the development and improvement of data collection, with Facebook and Google leading the way. We may have reached a critical mass of information, but at least now we can build predictive modeling around the data and market accordingly.
Foursquare, Gowalla, Loopt and Britekite have built context into locations and places that’s a gift to marketers. The world is now a physical Wikipedia entry. Data can be aggregated from everyone and extracted from everywhere.
Consumers may think that geolocation is a game, but for us it’s a new way to talk with consumers.
With both Google and the Library of Congress committed to archiving dialogue from social platforms, conversation is now part of the public forum for discussion. It’s also universally accessible, searchable and eternal—a huge shift for content formerly viewed as spontaneous and disposable.
With this mindset, we’ve begun to see the first forays into leveraging geolocation for brands. Local businesses in major metro areas like NYC and San Francisco are creating “Tips” (partnerships/deals with brands) that prompt a location-aware ad targeted directly at the individual. These special offers are opt-in and usually of high relevance to the user.
Brands are also sponsoring in-game rewards for users. For example, Bravo has created specific Foursquare badges based on their programming. Check into a restaurant featured on “Top Chef” and earn a badge.
But what if we aren’t thinking big enough? Marketers could purchase and bid on entire swaths of physical space to target consumers.
The model breaks down like this: Companies bid for “Check-ins” around various locations. Obviously some places will have higher values then others. Then services could charge the company for the ability to communicate with a customer at each individual check-in.
A potential execution of this strategy could be enacted by a chain restaurant. The restaurant could purchase the physical space within 2-3 square miles or it could just buy space within all other restaurants and prompt consumers to reconsider their purchase decision.
It’s the Google ad model as applied to physical space. Instead of keyword targeting, companies could begin location targeting and identifying the behavior of their core customer profile. Couple this with the recent developments at F8 (Facebook‘s developer conference) and you suddenly have a crystal clear picture of a potential consumer.
Predictive algorithms are nothing new. However, they have never been used quite this way. For instance, we already recommend restaurants based on affinity (consumer likes Restaurant X, therefore he will like Restaurant Y), but not necessarily on activity (consumer checks in at Whole Foods so he is directed to an organic bistro in his neighborhood). You can predict a stock’s relative growth based on past activity, so why couldn’t you predict consumer behavior the same way?
Mobile accomplishes what was once thought impossible: it allows someone’s online identity to follow him or her around offline.
Services like Plancast, Meet Gatsby and Riotvine are utilizing the location-based web to go beyond real-time data and actually put some thought into contextualizing it. They’re taking static data and making it dynamic and predictive on an operational level. By self identifying interest in a particular event or subject consumers can be served information relevant to their future activities. In essence, marketers can now effectively target peer groups and influence a change in behavior via peer pressure.
This is just the beginning.
We keep gaining new information about ourselves and our surroundings every day. Augmented reality has become a key part of tech’s evolution; not just a footnote.
Who knows what the future will hold?