Beacons are inexpensive pieces of hardware that sit in physical locations like retail stores, stadiums, airports etc. They use a special version of Bluetooth, which is a low power, short distance communication protocol. All that beacons do is transmit data, meaning beacons do not collect any data from the devices that they send information to, and all interactions with that beacon are done by the device with reference to the information sent by the beacon.Read More
Factual is a mobile data provider. Specifically, they've developed an understanding of millions (?) of locations. This means, for example, that it knows where Yankee Stadium is, and that it's called Yankee stadium. It also has a little metadata built in, including that it's a sports stadium etc. Similarly for things like PetSmart, it has data regarding the GPS locations for each branch and that it's a pet store. This is useful in a couple different ways. The first is that companies like Uber license this data, so when you search for Yankee Stadium, you don't have to put in the actual address. Their primary competence is knowing where things are on the planet, which is a relatively difficult data set to pull together, and their competitors include PlaceIQ and FourSquare (through their new Pinpoint advertising program).
But they're also very much in the ad space. Being a company focused on precise lat-long data, they can only really be effective where they can get this data. This generally means mobile app, though there are a few HTML 5 sites where this is possible. So the most logical conclusion might be enabling a brand like P&G to target users that are physically in Walmarts. Indeed, this is something Factual does. But that's a relatively limited model because it requires both temporal and physical constraints (meaning they must be in the store and looking at ads at the time they're being targeted). And it also relies on the type of stores that an advertiser would want to target. So Factual expanded their model to inferred psychographic models. That's a super fancy way of saying that they could look and see if, for example, you tend to go to pet stores every couple weeks, then you're a person with a pet - and you will be a person with a pet even when you're not in the pet store. And if you're a person that speeds along a train route every day, you're a commuter. With the pet owner example, you can now target all people that exhibit pet owning habits at any time if you're Purina. Given the flexibility of this model, you can imagine their data science team putting together some gnarly models of different types of consumers to meet different customer requests.
There is, of course, more complexity. Most exchanges don't allow third parties to harvest data about their users, combine it with other data, and sell that aggregated data. So Factual actually has to create and keep wholly separate pools of data across all the different exchanges. So you have to target the MoPub pet owners on MoPub - created only from apps that use MoPub, and the Smaato pet owners on Smaato - created only from apps that use Smaato. And so forth. That limits both the quality of the data and the workflow. However, you could apply Drawbridge or Tapad data to your MoPub pet owners segment and expand that reach, where possible. That's a really expensive option, however, since you'll be paying two separate data fees.