Location data has always been of interest to marketers but before 2010 the technology to leverage it was rather limited. The last 8 years, however, have seen a literal boom in a range of platforms that are capturing real-time, location data, and putting it to use via geofencing “ a service where an app, or similar software, utilizes GPS, Wi-Fi or cellular data to prompt an action (the sending of advertisements to the user on social media for example) once the mobile device enters a virtual, pre-established geographical boundary.
Starbucks is a common user, with folks receiving latte coupons for example after crossing into virtual boundaries. This has worked, quite well in many cases. But the limitations are evident. For example, notifying potential consumers when they cannot properly receive messages (driving a car) as well as a limited audience reach have been frequent critiques of geofencing.
Yet marketers have stayed true, understanding that while geofencing is not the end all be all for pinpoint, location-based marketing, it has opened the door to multipurpose, social media-esque location-based initiatives which are a growing segment and eventually be a valuable resource for said marketers. The whole industry is beginning to rethink location-based data as not simply a cool tool to send notifications about promotions or display attractions near me, but as a rich data source for multiple use cases.
There are several advantages to real-time, location data. First, the information can be interpreted within the context of physical surroundings, thus identifying if the user is sharing or tweeting the specifics of an experience. For example, users occupying different modes of transport (buses, trains or bicycles) can inform others about their experiences “ congested highways in the here and now. Information extracted from location-based, social media posts of travelers can communicate traffic patterns or via public APIs report a problem – a tree is down blocking the corner of X and Y.
From an information point of view, public officials would be privy to real-time data which is of tremendous value when putting together contingency plans. From a strict data point of view, a congested highway and fallen tree would appear rather minor. But the sum of these plus more is where it really gets interesting.
Likely the most complex issue here is being able to gather all relevant points and analyze them to understand consumer needs in a given moment at a given location. For example:
I am with my girlfriends in the city, going out to a concert and looking for a public toilet. But not any toilet, preferably a clean toilet, and on our way to the public, clean toilet, we pass a bar where they serve my favorite non-alcoholic beer “ which also has a toilet (hopefully clean).
The combination of filtering choices I made using my location-based app ¦
- Clean toilet
- Bar with toilet
- Concerts
- Favorite non-alcoholic beer
¦ in this instance offer marketers, government and merchants actionable insights on consumers’ whereabouts and preferences.
For the government, perhaps there are too few public toilets that are clean? To the marketer, clean toilets will get women into the bar and you can run happy hours with non-alcoholic beer. For the merchant (the bar), there is an event nearby, a concert which attracts many women who drink non-alcoholic beer and combined with clean toilets you will end up with a full house of women.
These links are not available today on any app platform but are in progress. Such an app can help government, merchants and marketers better understand consumer journeys, provide insight into consumer needs in real, actionable time, and still heed consumer privacy and any security concerns. And last but not least, make cities a better place to live in.
