As the proximity industry grows, it’s important for brands investing in location and proximity to understand the differences between the various data points and technologies on the market. These differentiators can greatly affect how the proximity and location data is applied towards a brand’s marketing goals.
Trying to find, understand, and reach people based on their various devices is a complex task. There are two primary ways to go about it: deterministic or probabilistic. Or in plain language: where you know or where you guess.
Of the two, deterministic data sets and technologies simply offer a higher assurance of accuracy while probabilistic methods provides larger scale. While it’s true that location data has historically been probabilistic, the emergence of proximity marketing (call it “Location 2.0”) with technologies able to verify both the device and the location, is very much deterministic.
Proximity & location
Location data (or 1.0) as it is commonly understood can be rightfully categorized as probabilistic for a few reasons. First, traditional location based on GPS signals simply tells you where you are as a latitude/longitude on the planet.
The trouble with purely probabalistic data, especially in dense cities with large buildings, is we may think we know where someone is, but we sure can’t prove it. Nor can we pinpoint that person’s relative context to a particular store. While this may not be important to some marketers it could be a game changer for others.
Proximity data, on the other hand, is inherently deterministic, gathered from sensors and transmitted using Bluetooth technology, Wi-Fi or NFC based on the user’s proximity to a pre-defined context, and therefore provides a higher assurance of accuracy. Such technologies also thrive indoors opening up a broad range of potential applications in subways, malls and hospital complexes.
Proximity data can also be combined with other deterministic data (logins, transaction history) and probabilistic data (browsing history etc.) Through social streams, or branded apps, marketers can apply this data to dynamically present personalized messages and calls to action that are measurable from inception. All together, this creates a much more complete graph.
Proximity in action
Bridging the technical divide that has long separated probabilistic and deterministic data means a fundamental shift in a brand’s ability to capture, synthesize and action everything we know about a consumer. Proximity combined with mobility, social etc. provides not only a pervasive connection with the consumer, but also the conditions for a much deeper and more personal relationship with them.
For instance, the NBA’s Orlando Magic is using proximity to solve a common problem: massive queues at concession and retail stands. Thanks to proximity technologies, fans with smartphones can now be notified as to the most optimal available queue time based on the user’s exact location in the stadium. This helps to ease congestion, shorten queue times and improve fans’ experience at the game. The team is also using the movements and previous seating arrangements obtained through proximity sensors to communicate cost-efficient seat upgrades, that would remain empty otherwise.
By focusing on micro-moments with engaged “in-store” shoppers that matched certain criteria, brewer Heineken engaged customers through native, in-app communication and guide them to a product on a specific aisle and shelf.
Paramount and Best Buy were able to connect with movie lovers and drive them to see a limited-time re-release of the film “Top Gun” in 3D and also push them to purchase the 3D Blu-ray at a nearby Best Buy by identifying the right audience. The campaign provided value to fans of the original release of the movie as well as a younger audience who may have been seeing the film for the first time. By providing them with a limited-time re-release created a sense of exclusivity and connected with fans in a meaningful way enough so that they were inclined to purchase the 3D Blu-ray at Best Buy.
All of these examples show how deterministic data is gathered, and can be used for later re-engagement.
Where we stand today
In 2017, we are experiencing a transition, as proximity marketing, or Location 2.0, is growing rapidly and is maturing along with the market. BIA Kelsey predicts that location-targeted ad spend will represent 45 % of overall mobile ad revenues by 2021. By that time proximity technology will have reached scale.
The industry is certainly working towards that goal. As more and more sensors are deployed in public places, advertisers will get the kind of pinpoint accuracy they crave — and no longer settle for alternative facts.
Kjartan Slette is Co-founder and COO of Unacast, which builds the double-deterministic Real World Graph to provide verified location, context and identity to global marketing platforms. Unacast is the world’s largest platform of unique proximity sensor data.