I want to take you through a problem that Unacast is seeing in the location data market. It’s this: Data providers are not always interested in making sure that their data is accurate, and they don’t provide a mechanism for the market to understand what it’s buying. To help explain, please take a look at this picture.
Here we visualize location data from a device that visited this strip mall. The arrows represent the latitude and longitude coordinates for the device. The single arrows show the device arriving in the area. The 4 arrows in the red circle in the upper-left indicate that the device spent time in that area (let’s say for 45 mins). Based on this data, you have a pretty good idea that the device went to Frank’s Clothes Store. There is also a possibility that it visited Sally’s Pizza and just parked really close to Frank’s. My guess is that you probably don’t think that there is any way that you can suggest that the device went to Parts & Craft. Don’t see Parts & Craft? Check way over on the bottom right-hand side of the picture.
Here is the funny thing. If you ask Location Data Providers to give you all the devices that went to Parts & Craft, they will give you our device that we think went to Frank’s. They’ll also tell you that the device went to Books R Us, Rando’s Deli, Cheap Furniture, Best Pharmacy, and Sally’s Pizza. You will get charged 7 times for the same exact data, depending on the question you are asking. In short, the Location Data ecosystem changes the contextualization of data based on what question they are being asked.
Why do they do this? The roots of the location data industry started in servicing the online advertising industry. The online advertising industry is notorious for demanding scale for data beyond what may be reasonably possible. To give you an example, at one point in my career, I encountered a car company running an online ad campaign targeting people in Seattle looking to buy a car. They wanted to buy more impressions per day for people looking to buy cars in Seattle than the number of people who actually live in Seattle. I would have had to show every person in Seattle the same ad 15 times a day in order for the campaign to deliver in full. For some reason I highly doubt every person in Seattle was looking to buy a car that month.
Getting back to the problem of how location data is contextualized. As the location data companies are more concerned with scale than accuracy, it becomes difficult for companies to understand what they are being sold. Unacast solves this problem by being transparent in our product.
Let’s go back to the picture. Our product will tell you that our algorithm indicated the device went to Frank’s Clothing Store. No matter what store you are interested in, we only ever assign Frank’s. We will also tell you all the nearby venues (i.e. Sally’s Pizza, Parts and Crafts etc.). The nearby venues allow you to understand which, and how many, stores are in an area. This can be used for advanced analysis like finding shoppers who have access to stores but aren’t visiting, or simply provide an indicator on how confident you can be that an assignment is correct (less dense areas make it easier to assign the venue). Doing all this means Unacast provides unmatched transparency in the market. It means we provide you with information beyond “just data”. We provide you with the ability to understand how to be experts at location data without expending all the effort. We care that you, the customer, understand what you’re buying. Not to mention, we provide unmatched cost efficiency for your dollars.