LSA17: Building the Real-World Graph

Marianna Zaslavsky

Marianna Zaslavsky, VP of Data Partnerships at Unacast, the largest proximity data platform, presented the Real World GraphTM. Unacast works with companies that deploy beacons, geo-fences, Wi-Fi and other sensors in hotels, retail stores, quick service restaurants, airports, stadiums, auto dealerships and many other commercial venues.

The data that is generated when phones interact with these sensors is currently fragmented in the marketplace. Just like Google indexed the web, Unacast is indexing the real physical world by aggregating all of these siloed data sets into one unified truth set.

The presentation began with the different trends on the deployment of sensors throughout the world and provided a few case studies: one in retail, one in a sports stadium, and one in a “smart” city. During the presentation of the case studies, Marianna explained how companies from these different verticals are using sensors to hit KPIs and achieve business objectives. The main takeaways are that:

  • The deployment of sensors is increasing
  • Tech trends lead by Apple’s iBeacon and Google’s Eddystone create ripe conditions for proximity data to explode
  • Companies across verticals can drive KPIs in revenue growth, customer engagement, app downloads and customer analytics via the deployment of sensors.

Unacast captures this data and these case studies from a directory it runs called, which is a free online resource where venues that want to deploy sensors can find companies that they may want to work with.


Marianna shared data that showed the significant increase in the deployment of sensors globally. Currently, there are 13 million sensors deployed and that is set to double every year, with a projected 500 million by 2021. There are some very compelling technology trends supporting the expansion of sensors and sensor data.

Apple’s removal of the headphone jack means that more people will keep Bluetooth turned on (according to Google, close to 50% of Americans already keep Bluetooth turned on). Bluetooth is the signal through which beacons interact with phones. The growth of Google’s Eddystone protocol also supports the growth of sensor data since Eddystone doesn’t require the sensor to interact with a phone app to capture data. All of this growth is happening because businesses are seeing real results after deploying sensors.

For example, Heineken deployed Inmarket sensors at their retail partners to drive in-store purchases and raise brand awareness in conjunction with the release of the 2015 James Bond movie “Spectre”. Heineken drove an estimated $320,000 in revenue as a result of the campaign (which represented a 6.2x ROI), as well as 200,000 in-store and point-of-sale engagements.

Marianna also spoke about the implications of the Real World GraphTM, which is a ‘truth set’ created when all of this siloed location data is aggregated. What is fascinating is that aggregation of this data allows industries to be powered by location data in ways not possible before.

For example, the advertising industry now has access to the most accurate location data on customer behaviors in retailers, malls, quick service restaurants, airports, hotels, stadiums and auto dealerships. That data can be used for retargeting, attribution, as well as audience modeling and validation of existing location data sets. Another example of how aggregated data can be used is in retail analytics.

What if a retailer could know how many of its customers shopped with a competitor within 24 hours of leaving their store? How about knowing about the locations where its customers shop where the retailer DOESN’T have stores? It’s equally compelling to understand data about locations where the retailer should have stores but doesn’t.

Another use case of aggregated location data is to power apps like augmented reality shopping apps (the Pokemon Go for shopping will soon come…) and even to power more accurate geo-filters like those on Snapchat. Lastly, what about the world of finance or city planning? Hedge funds would kill to understand foot traffic trends at a Walmart when a Target store opens within 10 miles. City planners would thrive as well with location data that could tell them where to build commerce centers or where to build hotels and transportation hubs.

The point is, when you aggregate location data, all sorts of other industries can be impacted and improved upon.

2 Responses to “LSA17: Building the Real-World Graph”

  1. Zack F says:

    I think this is a good technology as long as customers have awareness of who’s using there location data and why.

  2. Romet says:

    Good point Zack. The great thing about sensors is that it’s always opt-in. Users have to give permissions before being able to interact with any sensor.

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