Derived from location data, the Real World Graph® provides a unique perspective on how consumers are contextually connected to physical locations and each other.
We look at these movements as a node network to facilitate for better and smarter online experiences, mainly through advertising, retargeting and attribution. Future applications will be found in analytics, e-commerce, bots, and AI, to name a few.
The Real World Graph® is based on indoor and outdoor data collected from the interactions mobile devices have with location products and sensors all over the globe. This data is rigorously verified and synthesized with other user data by Unacast.
We gain our understanding about the connections between sensors, people, places, and context through analyzing billions of data points across millions of mobile devices and locations every month.
Through our network of location partners, we look for locations and context, allowing our advanced filtering algorithms to make sure all data that ends up in the Real World Graph® is double-deterministic™ and of the highest accuracy possible, meaning we both know the device and the context of each location. This data is coupled with other location signals to provide the full picture.
Everything that enters our proprietary data processing pipeline is connected to the Real World Graph® in real-time. This enables us to extract a deeper understanding of the data within seconds.
We connect to physical pieces of location technology and various hardware, such as beacons, that are placed in specific and known locations, like a particular store department, or next to a specific product type. Simply put, sensor technology is granular and targeted, and it works perfectly indoors. That’s why we call it double-deterministic™.
GPS data is not as accurate as beacon data. Beacon data is not as accurate as Wi-Fi data. Wi-Fi data is not as accurate as Point of Sale data, and to further complicate things, most companies will only deal with GPS data due to scale requirements. However, even if you are collecting GPS data in the best way possible way, there are still many issues that collection methodology can’t get around. For example, a cell phone in an urban mall with multiple floors has issues with accuracy because of tall buildings blocking GPS satellites, a dense number of stores all very close together, GPS doesn’t work as well indoors, and multiple stores share the same lat/long. So it is difficult for a company to really know where a device was at.
This is where the Unafy engine and the Unascore comes into play. Since we don't buy any media or collect any data ourselves, we don't have to claim that our data is more accurate than anyone. We want to empower the whole of the industry to be able to understand how confident we can be in a user visiting a specific location and allow them to select the data that is the best for their use case.
Our data scoring algorithm looks at data from dozens of factors to help us assign a confidence score to the visit (The Unascore) providing transparency into the quality of the data itself.
Our platform provides us with a unique position to better understand the likelihood that a user went to a specific store due to our data coming from many sources. We understand things like; how quickly a user was moving, where they are most likely to shop, did multiple sources and data types confirm the user at the same location.
The Real World Graph® is built with a privacy-first mentality, as scalable data solutions of the future need to take into account all parts of the eco-system - including end-users.
We are committed to ensure that all the members of the Unacast platform who contribute data to the Real World Graph® collect and process the data in compliance with applicable laws and industry privacy standards, enabling Unacast to provide privacy-compliant data solutions to its customers.
We connect to physical pieces of hardware, like beacons, that are placed in specific and known locations like specific store departments, inside a car or next to a specific product. Simply put, proximity technology is granular and targeted, and works perfectly indoors too
Today's insights and marketing platforms are heavily focused on geo-location data and lat/long, but as this data typically comes from unverified apps and bid requests combined with a geo-location API, the accuracy is difficult to pinpoint, especially indoors